artificial intelligence assignment topics

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211 Brand New Artificial Intelligence Topics for 2023

In this blog post, you will find 211+ artificial intelligence topics for high school, college and university students. The topics are split into 21 categories, so you will surely be able to find the topics you’re looking for in mere minutes. All our topics are original and have been created by our veteran writers and editors.

We have the AI essay topics technology students need – guaranteed. And the best part is that you don’t have to pay anything for these topics. They are listed on this page, so you can use any one of them 100% free. Yes, you can even reword the topics as you see fit. After all, our company has been established with a clear goal in mind: to help every student get the best grades possible.

Remember that if you need more than just some topics, our experienced writers are at your disposal. You can get writing tips, editing and proofreading assistance, and even academic writing services from our team of PhD experts in artificial intelligence, machine learning and Natural Language Processing.

Why Our Artificial Intelligence Topics?

OK, but why would you choose our AI topics? While there may be several other websites that are offering artificial intelligence topics to students, we are unique. Here is why:

All our topics are original and have been created by our team of experts Our topics are relatively easy to write about. We’ve made sure there is more than enough information online. You can use any of our topics for free without giving us any kind of credit The list of topics is updated periodically, so you can probably find at least a dozen topics that nobody in your class has thought about every time you visit this blog post.

We know you’re anxious to get started on your academic paper. After all, you probably don’t have a lot of time at your disposal to write the paper. So, without further ado, here are our best artificial intelligence topics:

Fun Artificial Intelligence Research Topics

If you are looking for some fun artificial intelligence research topics, you have definitely arrived at the right place. Check out these ideas:

A practical application of deep learning
How do industrial robots work?
Discuss AI-assisted investments
A simple machine learning application
Using artificial intelligence for detecting fraud
Compare and contrast 3 robots
The history of artificial intelligence
Discuss narrow AI implementations
Analyze social intelligence
Define machine consciousness
Solving complex problems using artificial intelligence
Can artificial intelligence simulate the human brain?

Easy Topics in Artificial Intelligence

In case you don’t want to spend days working on your essay, we would strongly recommend you to pick one of our easy topics in artificial intelligence (it’s easy to find resources and information about these topics on the Internet):

Define deep learning
Define machine learning
Define social intelligence
The AGI approach (artificial general intelligence)
Applications of artificial intelligence in banking
Applications of AI in space exploration
Applications of artificial intelligence in social networks
Discuss machine consciousness
Ethical issues with artificial intelligence
Discuss Natural Language Processing
Advancements in artificial intelligence in 2023
The future of artificial intelligence
Using artificial intelligence to catch plagiarism
The philosophy of artificial intelligence

AI Research Topics for High School

Yes, we have an entire category dedicated to high school students. Take a look at these awesome AI research topics for high school and pick the one you like:

The risks of narrow artificial intelligence
The risks of general AI
Define and discuss the concept of superintelligence
Limitation of current artificial intelligence
Best machine learning algorithms
Programming robots in 2023
Discuss the concept of ethical machines
The impact of AI since its inception
Machine sentience: an in-depth analysis
Will robots take over the world?
Will robots replace the human workforce?
Movements against artificial intelligence
Artificial intelligence in the military
AI drones in the United States
Artificial moral agents

Difficult Artificial Intelligence Research Paper Topics

Do you want to impress your professor and your classmates? The easiest way to do this is to write about one of our difficult artificial intelligence research paper topics:

Present the most effective deep learning algorithm
Write a machine learning algorithm
Compare and contrast 3 AI systems
Discuss malevolent artificial intelligence
Top 3 breakthroughs in AI in 2023
Computationalism versus functionalism
Discuss the implementation of robot rights
Analyze the technological singularity
Discuss the concept of hyperintelligence
How do AI systems work?
Alexa’s use of artificial intelligence: a case study
Siri’s use of artificial intelligence: a case study
Netflix’s use of artificial intelligence
Amazon’s use of artificial intelligence

Artificial Intelligence Topics for Presentation

Are you preparing to start working on your presentation? No problem; we’re here to help! Take a look at these excellent artificial intelligence topics for presentation:

The current state of artificial intelligence
Major breakthroughs in AI
The basic functionality of an AI system
What does deep learning mean?
Machine learning algorithms
A presentation of Natural Language Processing
The impact of artificial intelligence
Present the concepts and ideas behind narrow AI
Present general artificial intelligence
Artificial intelligence regulations in the US
Artificial intelligence regulations in Europe
Artificial intelligence in fiction

Controversial Topics in AI

Artificial intelligence is a relatively new field, so it has plenty of controversies surrounding it. Here are some interesting, controversial topics in AI for you to write about:

Should robots be allowed to become sentient?
Do the 3 laws of robotics actually exist?
Facial recognition software concerns
Privacy laws and artificial intelligence
Should robots have rights?
The role of human judgment in robotics
Signs of bias in AI behavior
Signs of discrimination in AI behavior
Building a superintelligent artificial intelligence
Can artificial intelligence development be stopped?
Discuss AI and religion (do they get along?)
Creative works by artificial intelligence systems
Analyze the apparition of Deepfake videos
Automated grading systems in our schools

Artificial Intelligence Topics for a Thesis

If you are preparing to start working on your thesis, you surely need some good ideas. Here are some of our best suggestions for artificial intelligence topics for a thesis:

The latest advancements in AI algorithms
Quantum computing and artificial intelligence
AI experiments and their success rate
Teaching your computer to create music
AI in social media marketing campaigns
Tesla’s use of artificial intelligence: a case study
Artificial intelligence predicting election results
Analyze the most prominent machine learning technology
Discuss the simulation of the human brain by AI systems
Image recognition using artificial intelligence
Important applications of artificial intelligence today
Security applications using artificial intelligence
Analyze deep generative models

Argument Debate Topics on AI

Are you looking for an argument debate topic? We have plenty of argument debate topics on AI right here for free:

Pros and cons of probabilistic programming
AI and the Internet of Things
AI development should be heavily regulated
Giving artificial intelligence access to our weapons systems
Robot hunter-killers on the battlefield
Real-life artificial intelligence versus movies
Can AI distinguish between good or bad?
Can a computer be ethical?
Large Scale Machine Learning: the future?
Do robots have morals?
Two artificial intelligence applications that revolutionized the industry
Teaching artificial intelligence in school

AI Topics for Research Paper in College

College students should pick more difficult topics than high school students. Here are some AI topics for research paper in college that are not overly difficult:

Tools you need to write an artificial intelligence program
Regulating the AI field correctly
Human judgment in artificial intelligence
The major types of artificial intelligence
Analyzing NLP algorithms
Predicting the price of housing with AI
Analyzing reinforcement learning in artificial intelligence
Ethical problems with artificial intelligence

Computer Science AI Topics

Are you a computer science student? Do you want the most interesting computer science AI topics? Check out these ideas and pick the one you like the most:

What is artificial intelligence? (a short history)
Measuring water quality with help from artificial intelligence
Email spam prevention with artificial intelligence
Discuss automated weapons
Is AI violating your privacy?
Image recognition software
Machine learning explained
Artificial neural networks explained

AI Ethics Topics

Discussing artificial intelligence ethics issues can be a very quick way to get a top grade on your paper. Here are some of the most interesting AI ethics topics:

Making the difference between right and wrong
AI and discrimination problems
Most important ethical issues with AI
Robot assassins controlled by artificial intelligence
Weapons system errors caused by artificial intelligence
Is artificial intelligence biased?
The need for tougher regulations
Can AI become more intelligent than the human race?

Advanced AI Topics

Would you like to talk about more advanced artificial intelligence topics? We have a long list of advanced AI topics for you:

Discuss the Bayesian inference
Discuss amortized inference
Analyze the most complex AI algorithm
How does NLP work?
How does machine learning work?
How is Alexa using artificial intelligence?
Siri using artificial intelligence
An in-depth analysis of deep generative models

Artificial Intelligence in Space Ideas

As you probably already know, artificial intelligence is being used in space exploration right now. So why not write a paper about one of our artificial intelligence in space ideas:

Artificial intelligence on the International Space Station
AI use in telescope array systems
Searching for alien life using artificial intelligence
Exploring Mars using artificial intelligence
Space exploration advancements related to AI
Mars Rover Perseverance’s use of artificial intelligence
Searching for Earth-like planets using AI systems
Early detection of space bodies on a collision course with Earth

Interesting Topics in AI

If you want to write about some interesting topics, you have arrived at the right place. Check out these interesting topics in AI and choose one now:

The artificial intelligence arms race
Discuss robotics process automation
What is synthetic intelligence?
Analyze the emergent algorithm
Discuss the concept of transhumanism
Analyze the behavior selection algorithm
The COMPAS program (US courts)
Robots increasing unemployment rates in the US

Good Research Topics for AI

Looking for good topics to write about? Need a topic that won’t keep you working for an entire week? Here are some good research topics for AI that are also relatively simple:

Japan’s artificial intelligence market
Discuss Strong AI
Deep learning algorithms in real life
Making weather predictions using artificial intelligence
Discuss Alan Turing’s Polite Convention
How to ensure machines behave ethically?
Discuss the Turing test
Discuss the “AI effect”

Graduate AI NLP Research Topics

If you are a graduate and need to write an essay about Natural Language Processing, we have some very nice graduate AI NLP research topics right here:

Cybersecurity and the use of machine learning
Machine learning in lead generation
Artificial intelligence in police drones
Sending AI probes to distant planets
Top artificial intelligence applications in robotics
The limits of machine learning
NLP limitations today
AI help for terminally ill patients

Machine Learning Topics in AI

Machine learning is an integral part of artificial intelligence, so it warrants its own section. Pick one of these machine learning topics in AI and start writing your essay right away:

Machine learning optimization
Machine learning generalization
Discuss supervised machine learning
The Dimensionality Reduction approach
Discuss training models for machine learning
Analyze reinforcement learning
The ethics behind machine learning
Bias in machine learning
Applications of machine learning in 2023

Hot AI Topics

Not all artificial intelligence topics are hot. There are some that have been trending for some time though. Here are some hot AI topics that should remain trending for a while:

Can artificial intelligence help us prevent another world war?
Machine learning and its contribution to the AI field
How does reasoning work from an AI system’s perspective?
Coding AI applications in Prolog effectively
Seeing the world through the “eyes” of a robot
Discuss the concept of predictive sales in today’s world
Explain how a machine learning algorithm works

Latest Trends in Artificial Intelligence

Breakthroughs in artificial intelligence happen on almost a weekly basis nowadays. So, why not write about the latest trends in artificial intelligence:

Greater Cloud
Top artificial solutions for the IT field
Structuring big data using artificial intelligence
Discuss Automated Machine Learning tech
Conceptual design aided by artificial intelligence
Discuss the approach of Tiny ML
Analyze advancements in quantum machine learning
Discuss the concept of responsible AI

AI Risks Topics

Artificial intelligence, like any new technology, has some risks associated with it. Here are some of the best AI risks topics you can find online:

Can AI become sentient and attack us?
Can artificial intelligence be programmed to respect our privacy?
Bias in data equals bias in artificial intelligence systems that analyze it
Can a robot be trustworthy?
The risks posed by narrow AI
AI-controlled weapons of mass destruction
Artificial intelligence used as a weapon in 2023
The dangers of a superintelligent AI system

The Future of Artificial Intelligence Ideas

Are you interested in writing about the future of artificial intelligence? We have some very nice the future of artificial intelligence ideas for you. Check them out below:

The future of humans in an AI-dominated world
AI impact on our transportation industry
Customer service benefitting from artificial intelligence
Artificial intelligence replacing journalists
Amazon’s heavy use of artificial intelligence in Fulfillment Centers
Human rights in the era of artificial intelligence
Artificial general intelligence and what does it mean
War robots are not a thing of the future anymore

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65+ Topics In Artificial Intelligence: A Comprehensive Guide To The Field

Jane Ng • 24 July, 2023 • 9 min read

Welcome to the world of AI. Are you ready to dive into the 65+ best topics in artificial intelligenc e and make an impact with your research, presentations, essay, or thought-provoking debates?

In this blog post, we present a curated list of cutting-edge topics in AI that are perfect for exploration. From the ethical implications of AI algorithms to the future of AI in healthcare and the societal impact of autonomous vehicles, this “topics in artificial intelligence” collection will equip you with exciting ideas to captivate your audience and navigate the forefront of AI research.

Artificial intelligence research topics, artificial intelligence topics for presentation, ai projects for the final year, artificial intelligence seminar topics, artificial intelligence debate topics, artificial intelligence essay topics, interesting topics in artificial intelligence.

Key Takeaways

FAQs About Topics In Artificial Intelligence

artificial intelligence assignment topics

Here are topics in artificial intelligence that cover various subfields and emerging areas:

AI in Healthcare: Applications of AI in medical diagnosis, treatment recommendation, and healthcare management.
AI in Drug Discovery : Applying AI methods to accelerate the process of drug discovery, including target identification and drug candidate screening.
Transfer Learning: Research methods to transfer knowledge learned from one task or domain to improve performance on another.
Ethical Considerations in AI: Examining the ethical implications and challenges associated with the deployment of AI systems.
Natural Language Processing: Developing AI models for language understanding, sentiment analysis, and language generation.
Fairness and Bias in AI: Examining approaches to mitigate biases and ensure fairness in AI decision-making processes.
AI applications to address societal challenges.
Multimodal Learning: Exploring techniques for integrating and learning from multiple modalities, such as text, images, and audio.
Deep Learning Architectures: Advancements in neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Here are topics in artificial intelligence suitable for presentations:

Deepfake Technology: Discussing the ethical and societal consequences of AI-generated synthetic media and its potential for misinformation and manipulation.
Cybersecurity: Presenting the applications of AI in detecting and mitigating cybersecurity threats and attacks.
AI in Game Development: Discuss how AI algorithms are used to create intelligent and lifelike behaviors in video games.
AI for Personalized Learning: Presenting how AI can personalize educational experiences, adapt content, and provide intelligent tutoring.
Smart Cities: Discuss how AI can optimize urban planning, transportation systems, energy consumption, and waste management in cities.
Social Media Analysis: Utilizing AI techniques for sentiment analysis, content recommendation, and user behavior modeling in social media platforms.
Personalized Marketing: Presenting how AI-driven approaches improve targeted advertising, customer segmentation, and campaign optimization.
AI and Data Ownership: Highlighting the debates around the ownership, control, and access to data used by AI systems and the implications for privacy and data rights.

AI-Powered Chatbot for Customer Support: Building a chatbot that uses natural language processing and machine learning to provide customer support in a specific domain or industry.
AI-Powered Virtual Personal Assistant: A virtual assistant that uses natural language processing and machine learning to perform tasks, answer questions, and provide recommendations.
Emotion Recognition : An AI system that can accurately recognize and interpret human emotions from facial expressions or speech.
AI-Based Financial Market Prediction: Creating an AI system that analyzes financial data and market trends to predict stock prices or market movements.
Traffic Flow Optimization: Developing an AI system that analyzes real-time traffic data to optimize traffic signal timings and improve traffic flow in urban areas.
Virtual Fashion Stylist: An AI-powered virtual stylist that provides personalized fashion recommendations and assists users in selecting outfits.

Here are the topics in artificial intelligence for the seminar:

How Can Artificial Intelligence Assist in Natural Disaster Prediction and Management?
AI in Healthcare: Applications of artificial intelligence in medical diagnosis, treatment recommendation, and patient care.
Ethical Implications of AI: Examining the ethical considerations and responsible development of AI Systems.
AI in Autonomous Vehicles: The role of AI in self-driving cars, including perception, decision-making, and safety.
AI in Agriculture: Discussing AI applications in precision farming, crop monitoring, and yield prediction.
How Can Artificial Intelligence Help Detect and Prevent Cybersecurity Attacks?
Can Artificial Intelligence Assist in Addressing Climate Change Challenges?
How Does Artificial Intelligence Impact Employment and the Future of Work?
What Ethical Concerns Arise with the Use of Artificial Intelligence in Autonomous Weapons?

Here are topics in artificial intelligence that can generate thought-provoking discussions and allow participants to critically analyze different perspectives on the subject.

Can AI ever truly understand and possess consciousness?
Can Artificial Intelligence Algorithms be Unbiased and Fair in Decision-Making?
Is it ethical to use AI for facial recognition and surveillance?
Can AI effectively replicate human creativity and artistic expression?
Does AI pose a threat to job security and the future of employment?
Should there be legal liability for AI errors or accidents caused by autonomous systems?
Is it ethical to use AI for social media manipulation and personalized advertising?
Should there be a universal code of ethics for AI developers and researchers?
Should there be strict regulations on the development and deployment of AI technologies?
Is artificial general intelligence (AGI) a realistic possibility in the near future?
Should AI algorithms be transparent and explainable in their decision-making processes?
Does AI have the potential to solve global challenges, such as climate change and poverty?
Does AI have the potential to surpass human intelligence, and if so, what are the implications?
Should AI be used for predictive policing and law enforcement decision-making?

Here are 30 essay topics in artificial intelligence:

AI and the Future of Work: Reshaping Industries and Skills
AI and Human Creativity: Companions or Competitors?
AI in Agriculture: Transforming Farming Practices for Sustainable Food Production
Artificial Intelligence in Financial Markets: Opportunities and Risks
The Impact of Artificial Intelligence on Employment and the Workforce
AI in Mental Health: Opportunities, Challenges, and Ethical Considerations
The Rise of Explainable AI: Necessity, Challenges, and Impacts
The Ethical Implications of AI-Based Humanoid Robots in Elderly Care
The Intersection of Artificial Intelligence and Cybersecurity: Challenges and Solutions
Artificial Intelligence and the Privacy Paradox: Balancing Innovation with Data Protection
The Future of Autonomous Vehicles and the Role of AI in Transportation

Here topics in artificial intelligence cover a broad spectrum of AI applications and research areas, providing ample opportunities for exploration, innovation, and further study.

What are the ethical considerations for using AI in educational assessments?
What are the potential biases and fairness concerns in AI algorithms for criminal sentencing?
Should AI algorithms be used to influence voting decisions or electoral processes?
Should AI models be used for predictive analysis in determining creditworthiness?
What are the challenges of integrating AI with augmented reality (AR) and virtual reality (VR)?
What are the challenges of deploying AI in developing countries?
What are the risks and benefits of AI in healthcare?
Is AI a solution or a hindrance to addressing social challenges?
How can we address the issue of algorithmic bias in AI systems?
What are the limitations of current deep learning models?
Can AI algorithms be completely unbiased and free from human bias?
How can AI contribute to wildlife conservation efforts?

Key Takeaways

The field of artificial intelligence encompasses a vast range of topics that continue to shape and redefine our world. In addition, AhaSlides offers a dynamic and engaging way to explore these topics. With AhaSlides, presenters can captivate their audience through interactive slide templates , live polls , quizzes , and other features allowing for real-time participation and feedback. By leveraging the power of AhaSlides, presenters can enhance their discussions on artificial intelligence and create memorable and impactful presentations.

As AI continues to evolve, the exploration of these topics becomes even more critical, and AhaSlides provides a platform for meaningful and interactive conversations in this exciting field.

What are the 8 types of artificial intelligence?

Here are some commonly recognized types of artificial intelligence:

Reactive Machines
Limited Memory AI
Theory of Mind AI
Self-Aware AI
Superintelligent AI
Artificial Superintelligence

What are the five big ideas in artificial intelligence?

The five big ideas in artificial intelligence, as outlined in the book “ Artificial Intelligence: A Modern Approach ” by Stuart Russell and Peter Norvig, are as follows:

Agents are AI systems that interact with and impact the world.
Uncertainty deals with incomplete information using probabilistic models.
Learning enables AI systems to improve performance through data and experience.
Reasoning involves logical inference to derive knowledge.
Perception involves interpreting sensory inputs like vision and language.

Are there 4 basic AI concepts?

The four fundamental concepts in artificial intelligence are problem-solving, knowledge representation, learning, and perception.

These concepts form the foundation for developing AI systems that can solve problems, store and reason with information, improve performance through learning, and interpret sensory inputs. They are essential in building intelligent systems and advancing the field of artificial intelligence.

Ref: Towards Data Science | Forbes | Thesis RUSH

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170 Best Artificial Intelligence Topics and Research Ideas

Table of Contents

Artificial Intelligence (AI) is an amazing field of study in science and technology. In recent times, AI plays a major role in our daily life. Particularly, many AI-based smart applications came into existence after the arrival of the internet. Also, a lot of research activities are being carried out extensively in this field. Right now, are you looking for unique artificial intelligence topics for your academic projects? To help you out, here we have compiled a list of the latest artificial intelligence topic ideas. Continue reading this blog post and get exclusive AI research ideas.

What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science. Mainly it focuses on the development of smart machines that have the ability to do tasks that needs human intelligence. Based on the functionality, AI is categorized into 4 types. The common AI types include reactive machines, theory of mind, limited memory, and self-awareness.

Some popular real-time AI applications are speech recognition, chatbots, language translation, self-driving car, virtual personal assistants, and many more. To solve real-world problems, artificial intelligence uses the following techniques.

Deep Learning
Fuzzy Logic
Expert Systems
Machine Learning
Natural Language Processing(NLP)

List of Artificial Intelligence Topics and Ideas

Artificial Intelligence is a broad subject with plenty of research areas to focus on. For your academic project, you can narrow down a topic from any branch of AI. When choosing an AI topic, make sure that it is original and relatively easy to write.

Here, we have listed a few top Artificial Intelligence Topic Ideas for you to consider. Go through the below-mentioned listed and identify a good AI topic for your assignment.

Interesting Artificial Intelligence Topics

Explain Deep Learning.
The risks of narrow AI implementations.
What is machine consciousness?
Are robots going to take away our jobs?
Narrow AI versus general AI .
Searching for Earth-like planets using AI systems.
Automated weapon systems.
Misbehaving AI models and the threats they may pose.
Privacy violations by artificial intelligence algorithms.
General artificial intelligence versus narrow artificial intelligence.
The threats of narrow AI executions.
Machine learning complexities in 2021.
AI in the Mars Rovers.
Problem resolution using AI.
Explain and elucidate NLP.
Compare and contrast Artificial neural networks and Reinforcement learning
Discuss the use of intelligent audition technologies for providing personalized healthcare
Discuss the benefits and limitations of using Big Data and AI in therapeutics and genomics
How does AI get used for preserving and accessing image and video integrity?
Discuss the use of quantum AI
Critical analysis of human-environment-centered AI systems

Best AI Research Topics

What is social intelligence?
Problem-solving issues with AI.
The cybernetics approach to artificial intelligence.
Machine Learning difficulties in 2022.
Discuss reasoning from an AI perspective.
Brain stimulation as part of an AI project.
Define and describe NLP
Predicting future locations using AI.
AI interaction issues with humans.
Identifying online spam using artificial intelligence.
Predictive sales and how AI intelligence support.
Lead production applying artificial intelligence.
Artificial intelligence and data science.
Movement planning in artificial intelligence
Will robots replace our jobs?
Tesla is a robot car- Explain the concept.
How robotics can be used in healthcare?
How deep learning and Big Data can be used for improving healthcare intelligence?
Discuss the advancements and applications of deep learning in scientific studies
How reinforcement learning can be used in medical imagining?
How AI can be used in collaborative learning in the classroom?

Complex Artificial Intelligence Research Topics

Managing Big Data databases using AI.
Predicting the election results with AI.
The core principles of AI risk management.
Using AI to measure water quality.
Prolog: the programming of the future.
Discuss inductive logic programming.
Artificial intelligence vision system applications.
Knowledge representation in artificial intelligence.
Using Artificial Intelligence for marketing analytics.
AI and fraud detection.
Social intelligence in artificial intelligence.
AI in customer service operations.
Limitations of AI machines.

AI Topics for Presentation

Lead generation using artificial intelligence.
Data science and artificial intelligence.
Artificial intelligence in our hospitals.
Motion planning in artificial intelligence applications.
What are predictive sales and how can AI help?
Are AI robots going to take over the world?
Automated financial investing.
An AI system that predicts housing prices.
AI in Security applications.
AI applications that changed the world.

Simple Research Topics on Artificial Intelligence

The role of human judgment in artificial intelligence.
Achieving 100% security against online attacks.
Engineering Artificial Intelligence.
Fields that could benefit from AI.
Is AI suited for weapon systems?
Major types of artificial intelligence
Can AI discern right from wrong?
Artificial intelligence replacing journalists.
Ethical concerns related to artificial intelligence.
Writing an AI problem.
Using artificial intelligence to assassinate high-value targets.
Discrimination issues with AI.
AI impact on the transportation industry.
Amazon’s heavy use of artificial intelligence in Fulfillment Centers.
The future of humans in an AI-dominated world.

Artificial Intelligence Thesis Topics

Brain simulation techniques in AI.
Discuss automated machine learning tech.
Soft computing and computational intelligence.
AI image recognition algorithms.
Artificial intelligence: Social Intelligence.
Are there any limits to what a machine can do?
Adopting AI systems in an organization.
Discuss business intelligence from an AI perspective.
What is reinforcement learning?
Discuss Large Scale Machine Learning.

Innovative AI Research Ideas

Teaching a computer how to paint.
Planning from an AI perspective.
Best-known AI experiments that failed.
AI applications in medicine.
Data management using artificial intelligence.
Explain the latest machine learning algorithms.
AI applications in robotics.
The qualification problem in AI systems.
Developing a simple chatbot.
AI applications in customer service.

Latest Artificial Intelligence Topics

Current trends in artificial intelligence
Analyze probabilistic programming.
What is the Bayesian inference?
The Monte Carlo methods.
AI that performs surgery unassisted
Artificial intelligence on the International Space Station.
Discuss generative models.
Artificial intelligence in cybersecurity applications.
Looking for habitable planets using AI.
Describe amortized inference.
Teaching an AI robot to walk on Mars.
Comparing today’s AI with that in movies.
Discuss deep generative models.
Hyper automation and the role of machine learning.
AI in the Mars Rover Perseverance.
Using AI to discover fresh craters on the Moon.
Ways artificial intelligence can help with space exploration.
Explain how a machine learning algorithm works.
Discuss the principles of AI engineering.
Regulating AI development.

Advanced Artificial Intelligence Project Ideas

Design a system to monitor fake product reviews.
Stress diagnosis through sensor signals of skin conductance.
Chatbot system to negotiate the price.
Reinforcement learning for car driving.
Answer checker application.
Detect Parkinson’s disease with deep neural networks.
Build an automatic attendance system.
Recognition of handwritten digits.
Application for music recommendation.
Prediction system of Cancer with Naïve Bayes.

Top-rated Artificial Intelligence Topics

Discuss the approach of Tiny ML.
Can artificial intelligence be programmed to respect our privacy?
The dangers of a super-intelligent AI system.
Artificial intelligence in police drones.
Analyze advancements in quantum machine learning.
Discuss training models for machine learning.
Making weather predictions using artificial intelligence.
Discuss the Turing test.
How does reasoning work from an AI system’s perspective?
Can artificial intelligence help us prevent another world war?
Machine learning and its contribution to the AI field.
How is Alexa using artificial intelligence?
Searching for alien life using artificial intelligence.
The Dimensionality Reduction approach.
Machine learning optimization.

Read more: Top Synthesis Essay Topics and Ideas To Consider For Assignments

Unique Artificial Intelligence Topics

Sending AI probes to distant planets.
Analyze the most complex AI algorithm.
Email spam prevention with artificial intelligence.
Discuss Alan Turing’s Polite Convention.
Discuss the idea of transhumanism.
What is synthetic intelligence?
AI is used in telescope array systems.
Siri uses artificial intelligence.
Explain artificial neural networks.
Image recognition software.
Teaching your computer to create music
Quantum computing and artificial intelligence.
A presentation of Natural Language Processing.
Computationalism versus functionalism.
Limitation of current artificial intelligence.

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140 Unique Geology Research Topics to Focus On

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114 Artificial Intelligence Essay Topic Ideas & Examples

Inside This Article

Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionize various aspects of our lives. From autonomous vehicles to virtual assistants, AI technologies are becoming increasingly prevalent. If you have been assigned an essay on artificial intelligence and are struggling to come up with a topic, look no further. Here are 114 AI essay topic ideas and examples to inspire your writing:

The impact of AI on job automation: How will AI technologies reshape the workforce?
The ethical implications of AI: Should there be limits on how AI can be used?
The future of AI in healthcare: How can AI enhance medical diagnosis and treatment?
The role of AI in education: How can AI technologies improve the learning experience?
AI and privacy concerns: What are the risks associated with AI technologies and personal data?
The use of AI in criminal justice: Can AI systems make fair and unbiased decisions?
The potential dangers of superintelligent AI: Should we be concerned about AI surpassing human intelligence?
AI and creativity: Can AI systems be creative in the same way humans are?
The impact of AI on mental health: How can AI technologies assist in diagnosing and treating mental illnesses?
The role of AI in climate change mitigation: How can AI help reduce carbon emissions?
The future of transportation with AI: How will autonomous vehicles change the way we travel?
AI and cybersecurity: Can AI technologies enhance our ability to detect and prevent cyber attacks?
The impact of AI on social interactions: How will AI-powered virtual assistants affect human relationships?
Bias in AI algorithms: How can we ensure fairness and impartiality in AI decision-making?
The ethical implications of using AI in warfare: Should autonomous weapons be allowed?
AI and the arts: How can AI technologies be used in creative fields such as music and painting?
The role of AI in disaster response: How can AI help in predicting and managing natural disasters?
The impact of AI on journalism: How will AI technologies influence news reporting and media?
The use of AI in agriculture: How can AI optimize farming practices?
AI and financial markets: How can AI algorithms be used for better investment decisions?
The challenges of regulating AI: How can governments ensure safe and responsible development of AI technologies?
AI and human rights: What are the potential threats to privacy and freedom posed by AI?
The role of AI in space exploration: How can AI assist in exploring the universe?
AI and language translation: How can AI technologies improve communication across different languages?
The impact of AI on creativity: Will AI systems replace human creativity or enhance it?
The use of AI in customer service: How can AI-powered chatbots improve customer experiences?
AI and the future of work: How will AI technologies affect employment opportunities?
The role of AI in personalized medicine: How can AI help tailor treatments for individual patients?
AI and education inequality: How can AI technologies bridge the gap between privileged and underprivileged students?
The impact of AI on the economy: Will AI lead to job creation or job displacement?
AI and augmented reality: How can AI enhance the AR experience?
The use of AI in sports: How can AI technologies optimize performance and training?
AI and natural language processing: How can AI understand and generate human language?
The impact of AI on the legal profession: Will AI replace lawyers in the future?
The role of AI in combating fake news: How can AI technologies detect and prevent misinformation?
AI and emotional intelligence: Can AI systems develop emotional intelligence?
The use of AI in wildlife conservation: How can AI technologies help protect endangered species?
AI and transportation infrastructure: How can AI improve traffic management and reduce congestion?
The impact of AI on the entertainment industry: How will AI technologies shape the future of movies and gaming?
AI and personalized advertising: How can AI algorithms target ads to individual preferences?
The role of AI in disaster recovery: How can AI assist in rebuilding after natural disasters?
AI and mental well-being: Can AI technologies provide therapy and support for mental health?
The use of AI in social media: How can AI detect and prevent harmful content?
AI and the future of energy: How can AI optimize energy consumption and production?
The impact of AI on democracy: What are the implications of AI for political systems?
AI and robotics: How can AI enhance the capabilities and interactions of robots?
AI and the aging population: How can AI technologies improve the quality of life for elderly individuals?
The use of AI in retail: How can AI technologies personalize the shopping experience?
AI and virtual reality: How can AI enhance the VR experience?
The impact of AI on creativity in the workplace: Will AI systems replace or empower human creativity?
AI and autonomous drones: What are the potential applications and risks?
The role of AI in social justice: How can AI technologies address systemic biases and discrimination?
AI and disaster prediction: How can AI assist in predicting natural disasters?
The use of AI in architecture and design: How can AI technologies optimize building design?
AI and sustainable development: How can AI help achieve environmental and social sustainability?
The impact of AI on the music industry: How will AI technologies shape music production and consumption?
AI and the future of democracy: Can AI improve citizen engagement and participation?
The role of AI in personalized learning: How can AI technologies adapt educational content to individual students?
AI and autonomous robots in healthcare: What are the benefits and risks?
The use of AI in supply chain management: How can AI optimize logistics and inventory management?
AI and emotional recognition: How can AI systems understand and respond to human emotions?
The impact of AI on urban planning: How can AI technologies create smarter and more sustainable cities?
AI and cybersecurity threats: How can AI be used to detect and prevent cyber attacks?
The role of AI in personalized news curation: How can AI algorithms tailor news articles to individual interests?
AI and personalized fashion: How can AI technologies help consumers find their unique style?
The impact of AI on social inequality: Will AI exacerbate or alleviate existing inequalities?
AI and decision-making: Can AI systems make better decisions than humans?
The use of AI in cultural preservation: How can AI technologies help protect and restore cultural heritage?
AI and the future of transportation infrastructure: How can AI technologies improve roads, bridges, and public transportation?
The role of AI in early detection of diseases: How can AI assist in diagnosing illnesses at an early stage?
AI and personalized entertainment: How can AI technologies tailor movies, music, and games to individual preferences?
The impact of AI on customer behavior analysis: How can AI algorithms predict and influence consumer choices?
AI and the future of democracy: How can AI technologies promote transparency and accountability in governance?
The use of AI in disaster relief: How can AI assist in coordinating rescue and aid efforts?
AI and sustainable agriculture: How can AI technologies optimize farming practices while minimizing environmental impact?
The role of AI in personalized marketing: How can AI algorithms target advertisements to individual preferences?
AI and the future of privacy: How can AI technologies protect personal data in an increasingly connected world?
The impact of AI on creative industries: Will AI systems replace or collaborate with human artists?
AI and autonomous ships: What are the potential benefits and challenges?
The use of AI in wildlife monitoring: How can AI technologies help track and protect endangered species?
AI and the future of cybersecurity: How can AI technologies stay ahead of evolving cyber threats?
The role of AI in personalized fitness: How can AI technologies optimize exercise routines and nutrition plans?
AI and personalized travel recommendations: How can AI algorithms suggest tailored itineraries to individual travelers?
The impact of AI on income inequality: Will AI exacerbate or reduce economic disparities?
AI and the future of journalism: How can AI technologies assist in news reporting and fact-checking?
The use of AI in waste management: How can AI technologies optimize recycling and waste disposal?
AI and autonomous farming: How can AI technologies improve crop yield and reduce resource consumption?
The role of AI in personalized financial advice: How can AI algorithms help individuals make better financial decisions?
AI and the future of privacy: How can AI technologies protect personal information in the age of big data?
The impact of AI on the film industry: How will AI technologies influence movie production and special effects?
AI and autonomous construction: What are the potential applications and challenges?
The use of AI in marine conservation: How can AI technologies help protect marine ecosystems?
AI and the future of transportation logistics: How can AI optimize the movement of goods and reduce carbon emissions?
The role of AI in personalized healthcare: How can AI technologies tailor treatments to individual patients?
AI and personalized gaming: How can AI algorithms create unique gaming experiences for individual players?
The impact of AI on voting systems: Can AI technologies improve the accuracy and security of elections?
AI and sustainable urban planning: How can AI technologies create greener and more livable cities?
The use of AI in personalized nutrition: How can AI algorithms optimize diets for individual health goals?
AI and the future of privacy: How can AI technologies balance the benefits of data analysis with privacy concerns?
The role of AI in personalized advertising: How can AI algorithms target ads to individual preferences without invading privacy?
AI and autonomous underwater vehicles: What are the potential applications and challenges?
The impact of AI on wildlife conservation: How will AI technologies enhance conservation efforts?
AI and the future of transportation safety: How can AI technologies prevent accidents and improve road conditions?
The use of AI in personalized fashion design: How can AI algorithms create customized clothing?
AI and sustainable energy management: How can AI technologies optimize energy usage in homes and buildings?
The role of AI in personalized learning platforms: How can AI technologies adapt educational content to individual students' needs?
AI and the future of privacy: How can AI technologies protect personal information from unauthorized access?
The impact of AI on the gaming industry: How will AI technologies enhance gameplay and virtual worlds?
AI and autonomous construction robots: What are the potential benefits and risks?
The use of AI in personalized travel planning: How can AI algorithms create tailored itineraries based on individual preferences?
AI and sustainable transportation: How can AI technologies optimize public transportation and reduce carbon emissions?
The role of AI in personalized mental health support: How can AI technologies provide therapy and counseling?
AI and personalized music creation: How can AI algorithms compose music based on individual preferences?
The impact of AI on social media manipulation: Can AI technologies detect and prevent the spread of fake news and misinformation?

These 114 artificial intelligence essay topic ideas and examples cover a wide range of areas where AI technologies can make a significant impact. Whether you're interested in the ethical implications of AI or its potential applications in various industries, there is a topic here for you. Choose one that sparks your curiosity and start writing an insightful and engaging essay on artificial intelligence.

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12 Best Artificial Intelligence Topics for Research in 2024

Explore the "12 Best Artificial Intelligence Topics for Research in 2024." Dive into the top AI research areas, including Natural Language Processing, Computer Vision, Reinforcement Learning, Explainable AI (XAI), AI in Healthcare, Autonomous Vehicles, and AI Ethics and Bias. Stay ahead of the curve and make informed choices for your AI research endeavours.

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Table of Contents

1) Top Artificial Intelligence Topics for Research

a) Natural Language Processing

b) Computer vision

c) Reinforcement Learning

d) Explainable AI (XAI)

e) Generative Adversarial Networks (GANs)

f) Robotics and AI

g) AI in healthcare

h) AI for social good

i) Autonomous vehicles

j) AI ethics and bias

2) Conclusion

Natural Language Processing

Natural Language Processing (NLP) is centred around empowering machines to comprehend, interpret, and even generate human language. Within this domain, three distinctive research avenues beckon:

1) Sentiment analysis: This entails the study of methodologies to decipher and discern emotions encapsulated within textual content. Understanding sentiments is pivotal in applications ranging from brand perception analysis to social media insights.

2) Language generation: Generating coherent and contextually apt text is an ongoing pursuit. Investigating mechanisms that allow machines to produce human-like narratives and responses holds immense potential across sectors.

3) Question answering systems: Constructing systems that can grasp the nuances of natural language questions and provide accurate, coherent responses is a cornerstone of NLP research. This facet has implications for knowledge dissemination, customer support, and more.

Computer Vision

Computer Vision, a discipline that bestows machines with the ability to interpret visual data, is replete with intriguing avenues for research:

1) Object detection and tracking: The development of algorithms capable of identifying and tracking objects within images and videos finds relevance in surveillance, automotive safety, and beyond.

2) Image captioning: Bridging the gap between visual and textual comprehension, this research area focuses on generating descriptive captions for images, catering to visually impaired individuals and enhancing multimedia indexing.

3) Facial recognition: Advancements in facial recognition technology hold implications for security, personalisation, and accessibility, necessitating ongoing research into accuracy and ethical considerations.

Reinforcement Learning

Reinforcement Learning revolves around training agents to make sequential decisions in order to maximise rewards. Within this realm, three prominent Artificial Intelligence Topics emerge:

1) Autonomous agents: Crafting AI agents that exhibit decision-making prowess in dynamic environments paves the way for applications like autonomous robotics and adaptive systems.

2) Deep Q-Networks (DQN): Deep Q-Networks, a class of reinforcement learning algorithms, remain under active research for refining value-based decision-making in complex scenarios.

3) Policy gradient methods: These methods, aiming to optimise policies directly, play a crucial role in fine-tuning decision-making processes across domains like gaming, finance, and robotics.

Introduction To Artificial Intelligence Training

Explainable AI (XAI)

The pursuit of Explainable AI seeks to demystify the decision-making processes of AI systems. This area comprises Artificial Intelligence Topics such as:

1) Model interpretability: Unravelling the inner workings of complex models to elucidate the factors influencing their outputs, thus fostering transparency and accountability.

2) Visualising neural networks: Transforming abstract neural network structures into visual representations aids in comprehending their functionality and behaviour.

3) Rule-based systems: Augmenting AI decision-making with interpretable, rule-based systems holds promise in domains requiring logical explanations for actions taken.

Generative Adversarial Networks (GANs)

The captivating world of Generative Adversarial Networks (GANs) unfolds through the interplay of generator and discriminator networks, birthing remarkable research avenues:

1) Image generation: Crafting realistic images from random noise showcases the creative potential of GANs, with applications spanning art, design, and data augmentation.

2) Style transfer: Enabling the transfer of artistic styles between images, merging creativity and technology to yield visually captivating results.

3) Anomaly detection: GANs find utility in identifying anomalies within datasets, bolstering fraud detection, quality control, and anomaly-sensitive industries.

Robotics and AI

The synergy between Robotics and AI is a fertile ground for exploration, with Artificial Intelligence Topics such as:

1) Human-robot collaboration: Research in this arena strives to establish harmonious collaboration between humans and robots, augmenting industry productivity and efficiency.

2) Robot learning: By enabling robots to learn and adapt from their experiences, Researchers foster robots' autonomy and the ability to handle diverse tasks.

3) Ethical considerations: Delving into the ethical implications surrounding AI-powered robots helps establish responsible guidelines for their deployment.

AI in healthcare

AI presents a transformative potential within healthcare, spurring research into:

1) Medical diagnosis: AI aids in accurately diagnosing medical conditions, revolutionising early detection and patient care.

2) Drug discovery: Leveraging AI for drug discovery expedites the identification of potential candidates, accelerating the development of new treatments.

3) Personalised treatment: Tailoring medical interventions to individual patient profiles enhances treatment outcomes and patient well-being.

AI for social good

Harnessing the prowess of AI for Social Good entails addressing pressing global challenges:

1) Environmental monitoring: AI-powered solutions facilitate real-time monitoring of ecological changes, supporting conservation and sustainable practices.

2) Disaster response: Research in this area bolsters disaster response efforts by employing AI to analyse data and optimise resource allocation.

3) Poverty alleviation: Researchers contribute to humanitarian efforts and socioeconomic equality by devising AI solutions to tackle poverty.

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Autonomous vehicles

Autonomous Vehicles represent a realm brimming with potential and complexities, necessitating research in Artificial Intelligence Topics such as:

1) Sensor fusion: Integrating data from diverse sensors enhances perception accuracy, which is essential for safe autonomous navigation.

2) Path planning: Developing advanced algorithms for path planning ensures optimal routes while adhering to safety protocols.

3) Safety and ethics: Ethical considerations, such as programming vehicles to make difficult decisions in potential accident scenarios, require meticulous research and deliberation.

AI ethics and bias

Ethical underpinnings in AI drive research efforts in these directions:

1) Fairness in AI: Ensuring AI systems remain impartial and unbiased across diverse demographic groups.

2) Bias detection and mitigation: Identifying and rectifying biases present within AI models guarantees equitable outcomes.

3) Ethical decision-making: Developing frameworks that imbue AI with ethical decision-making capabilities aligns technology with societal values.

Future of AI

The vanguard of AI beckons Researchers to explore these horizons:

1) Artificial General Intelligence (AGI): Speculating on the potential emergence of AI systems capable of emulating human-like intelligence opens dialogues on the implications and challenges.

2) AI and creativity: Probing the interface between AI and creative domains, such as art and music, unveils the coalescence of human ingenuity and technological prowess.

3) Ethical and regulatory challenges: Researching the ethical dilemmas and regulatory frameworks underpinning AI's evolution fortifies responsible innovation.

AI and education

The intersection of AI and Education opens doors to innovative learning paradigms:

1) Personalised learning: Developing AI systems that adapt educational content to individual learning styles and paces.

2) Intelligent tutoring systems: Creating AI-driven tutoring systems that provide targeted support to students.

3) Educational data mining: Applying AI to analyse educational data for insights into learning patterns and trends.

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Conclusion

The domain of AI is ever-expanding, rich with intriguing topics about Artificial Intelligence that beckon Researchers to explore, question, and innovate. Through the pursuit of these twelve diverse Artificial Intelligence Topics, we pave the way for not only technological advancement but also a deeper understanding of the societal impact of AI. By delving into these realms, Researchers stand poised to shape the trajectory of AI, ensuring it remains a force for progress, empowerment, and positive transformation in our world.

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208 Artificial Intelligence Essay Topics & Research Questions about AI

If you’re looking for interesting AI research questions or essay topics, you’ve come to the right place! In this list, we’ve compiled the latest trending essay topics on artificial intelligence, research questions, and project ideas. It doesn’t matter if you’re a high school student or a Ph.D. holder: here, you will find research questions about artificial intelligence for beginners as well as professionals.

🏆 Best Essay Topics on Artificial Intelligence

✍️ artificial intelligence essay topics for college, 🌶️ hot artificial intelligence ideas to write about, 👍 good artificial intelligence research topics & essay examples, ⭐ simple & easy artificial intelligence essay titles, 🎓 most interesting artificial intelligence research titles, 💡 artificial intelligence argumentative essay topics, ❓ research questions about artificial intelligence.

Artificial Intelligence Pros and Cons: Essay Sample
Artificial Intelligence and Unemployment
Ethics of Artificial Intelligence
Artificial Intelligence Versus Human Intelligence
Artificial Intelligence and Its Impact on the Future
Artificial Intelligence and Effects of Its Rise
Artificial Intelligence in the Workplace
Artificial Intelligence in Healthcare Artificial intelligence (AI) and similar technologies are becoming more common in business and society, and now even integrating into healthcare.
Artificial Intelligence: Effects on Business Artificial intelligence is a wide range of technological advancements that deal with current and future effects on the business sector to enhance profitability.
Artificial Intelligence as a Potential Threat to Humanity Artificial intelligence (AI) demonstrates immense potential in terms of improving society as long it is developed and implemented properly.
Will Artificial Intelligence Replace Humans? The paper discusses whether artificial intelligence replaces humans. There is no single answer to the question because it affects several areas of human life.
Artificial Intelligence and Music The paper discusses use of Artificial Intelligence is rapidly expanding, with several innovative companies adopting it to create music.
Impact of Artificial Intelligence on the Education System The paper analyzes how the education system can maximize the advantages of Artificial Intelligence. It compares the traditional education system.
Artificial Intelligence and Its Role in Business The paper looks into the peculiarities of replacing human work with AI to define its potential for development and issues associated with such implementation.
Artificial Intelligence in the Labor Market The paper states that the continuous improvements in terms of developing artificial intelligence make render this technology close to reality.
Artificial Intelligence as an Enhancer of Human Abilities The paper states that using Artificial Intelligence to enhance human capabilities is a trending factor that is growing and receiving attention.
Artificial Intelligence Economy This annotated bibliography aims to discuss seven articles devoted to the topic of the artificial intelligence economy.
Artificial Intelligence: Ethical, Social, Legal Issues The field of artificial intelligence indeed brings numerous ethical, social, professional and legal issues; but are those so disturbing as some people claim?
Artificial Intelligence in Hospitality Industry The purpose of this paper is to explore the use application of AI in the hospitality industries. The paper focuses on the utilization of booking engines and hotel software systems.
Helpmewrite.AI Software’s Business Feasibility The report offers research on Helpmewrite.ai software, which is a product that helps writers, lawyers, and paralegals to compose distinct legal pamphlets promptly.
Artificial Intelligence in Healthcare: Pros & Cons Rapidly advancing artificial intelligence technologies are gradually changing health care practices and bring a paradigm shift to the medical system.
Artificial Intelligence in Self-Driving Cars The paper states that artificial intelligence in self-driving vehicles cannot conclude several favorable outcomes – or, the “least bad” effects.
AI In Accounting Essay Example Introducing artificial intelligence (AI) into accounting is viewed by many researchers as a promising practice, which has been the subject of numerous studies.
Artificial Intelligence in the Hotel Industry The results of the analysis have shown that, overall, Artificial Intelligence proves to have a positive impact on the quality of hotel services and risk management.
Game Playing in Artificial Intelligence The 9th of March, 2016, was a watershed moment in the development of artificial intelligence when the Go champion Lee Sedol was beaten by AlphaGo.
The Dawn of Artificial Intelligence: Robots Robots were created by people to satisfy their large insatiable appetites. Such a sacrilegious act against the miracle of creation may cost a lot.
Artificial Intelligence: Use and Potential Risks Automation and intelligent algorithms can significantly benefit business owners by inducing substantial savings.
Technologies & Artificial Intelligence Challenges For innovative organizations, new technologies introduce not only benefits but also new challenges as the use of artificial intelligence (AI) changes the way organizations work.
The Fundamental Role of Artificial Intelligence in the IT Industry Artificial intelligence is aimed at machine learning and providing software to address the problems in a way similar to human intelligence.
Artificial Intelligence in Supply Chain Management This paper establishes the benefits, opportunities, and challenges of adopting machine learning technologies in logistics to help SMEs boost their performance.
Artificial Intelligence in Accounting Artificial Intelligence is already replacing some accounting operations, and research indicates that automating these processes is a cost-effective option.
Artificial Intelligence in Economics Currently, the amount of data available to businesses continues to grow at an exceptional rate due to the developments in artificial intelligence and big data.
Artificial Intelligence in the Oil and Gas Industry Construction, mining, and oil and gas companies are the latecomers in this digitalization, increasingly depending on AI and machine learning (ML) based solutions frenzy.
E-Commerce: The Role of Artificial Intelligence Though the implementation of artificial intelligence within e-commerce continues to develop rapidly, there are several areas in which it affects the experiences of customers.
The Impact of Artificial Intelligence on the Future of Work Artificial intelligence is requested in the modern business world because it can lead to many positive outcomes when applied to employee monitoring.
Artificial Intelligence Threat for Employees The article “U.S. Lost Over 60 Million Jobs” published focuses on the ongoing unrest around the future of the job market and AI presence within.
The Promises and Perils of Artificial Intelligence Artificial intelligence is a powerful technology that can generate economic gains; therefore, it is critical to explore its prehistory and practical and ethical concerns.
Usage of AI and Robotics in Project Management Technological progress has allowed humanity to use the technologies they could not implement in the past centuries.
How AI and Machine Learning Influence Marketing in the Fashion Industry The study aims to determine if the perception of AI in fashion is a novel concept and whether it holds enough appeal to impact the purchasing decisions of fashion consumers.
Business Model Canvas and Artificial Intelligence The nine blocks in the business model canvas, which include vital partners, cost structure, and others in relation to AI, can be summarized to explain their role in business.
Artificial Intelligence and Global Societal Issues Examination of the latest trends in the sphere of development of manufacturing processes indicates significant growth in the interest in artificial intelligence.
Could Artificial Intelligence ‘End Mankind’ or Is It All Alarmist Nonsense? The idea of AI ending humankind and leading to a global catastrophe does not represent modern reality accurately.
Artificial Intelligence in Business Management The use of artificial intelligence in business management is a sound practice due to many benefits that this technology offers and an opportunity to secure operational controls.
Artificial Intelligence and the Future of Nursing The benefits of AI technologies include time and cost efficiency, as well as a high level of care consistency and comprehensiveness.
Algorithmic Bias of Artificial Intelligence Artificial intelligence is a rapidly developing technology which is already extensively utilized in different spheres, yet it has many considerable issues.
Artificial Intelligence: Article Review Review of an article by Vinyals, Gaffney & Ewalds (2017) discussing the use of the StarCraft II video game as a platform for AI development and testing.
Artificial Intelligence: Pros and Cons Artificial intelligence is definitely a huge step in global technological progress, and like any other technology, it can be both a weapon and a lifesaver.
Artificial Intelligence: Integrated in Healthcare This paper aims to talk about AI as an innovative idea that can be integrated into healthcare. It will detail the strategies used in executing AI.
New Technology in the Air Cargo Industry: Artificial Intelligence The article “Transport logistic: Artificial Intelligence at Air cargo” discusses how artificial intelligence will revolutionize the air cargo industry.
Artificial Intelligence and Human Intelligence Comparison AI performs many tasks that are impossible for humans to perform and can be equal to human tasks in interpreting CT scans, recognizing faces and voices, and playing games.
AI System in Smart Energy Consumption The primary aim of the paper is to expose the significant impacts of AI integration in intelligent energy consumption methods.
Artificial Intelligence Implementation in Accounting Processes Artificial intelligence seems to be a prospective technology, and its implementation in accounting processes is inevitable.
Artificial Intelligence in Aviation and Human-Machine Interfaces The following study analyzes the research results to determine the impact of artificial intelligence (AI) on aviation.
Artificial Intelligence as a Part of Imperialism: Challenges and Solutions Artificial intelligence is part of the process of imperialism, its offshoot, which is commonly called information imperialism.
Marketing Artificial Intelligence Problems The alignment problem when applying artificial intelligence in marketing occurs when managers ask a question that does not align with the set objectives.
Companies’ Reputation and Artificial Intelligence This paper discusses companies’ reputations and whether artificial intelligence (AI) has the capacity to predict customer and competitor behavior.
Medical Innovations: 3D Bioprinting Artificial Intelligence This paper will discuss two medical technological innovations that are significant for the future of a medical organization and how different stakeholders could benefit from them.
Artificial Intelligence and Big Data Impacts on Citizens The concept of artificial intelligence is complex and broad. However, researchers, theorists, and writers contribute to the creation of a clear and factual definition of this term in different ways.
Using Information Technology and Artificial Intelligence in Critical Care Medicine Artificial Intelligence in critical care is helping to care for patients faster, supervise more patients, calculate the exact dosage for patients, and collect more detailed data.
The Portrayal of Artificial Intelligence Artificial intelligence seems to be Frankenstein’s monster of the new age. Different sources provide significant insight into the portrayal of AI as monstrosity.
The Limits of Global Inclusion in AI (Artificial Intelligence) Development This article is devoted to the theme of the development and implementation of elements of artificial intelligence (AI) in the context of various countries.
AI Development, Unemployment, and Universal Basic Income The theme of AI-human relationships takes an important place in science fiction literature, movies, and video games, but it is not limited by them.
AI in Customer Service: Argument Flaws Analyzing AI’s comprehensive functionality can provide sufficient arguments for a variety of options to implement to attract and retain customers.
Artificial Intelligence Bias and Ethical Algorithms The paper argues in order to solve the problem of lack of diversity and assessing human needs correctly, there is a need to implement better guidelines for Artificial Intelligence.
Fire Scene Investigation: Artificial Intelligence Each container should be labeled uniquely, including the investigator’s name, date and time, sample number, case number, and location of recovery.
Artificial Intelligence and How It Affects Hospitality The main challenge in regards to Artificial Intelligence is its current state, which still requires extensive development in order for it to become practical and useful.
Artificial Intelligence Projects Failed The research paper provides a detailed worldwide timeline of artificial intelligence projects that were attempted and failed and the threats they have caused.
Artificial Intelligence in Medical Field The medical field constantly innovates and develops new technologies to improve patient care. Societies, in general, are significantly impacted by technological innovations.
Artificial Intelligence: Emergence of Employment Issues Artificial intelligence has become particularly widespread in the modern world, but there are significant controversies about the benefits of this technology in people’s lives.
Comparing Artificial Intelligence to Human Intelligence Intelligence is essential for humanity, as it can isolate important information from the environment and systematize it into knowledge used to solve specific problems.
Customer’s Brand Engagement: The Use of Artificial Intelligence Marketers are currently using artificial intelligence in marketing to automate procedures and provide clients with a distinctive brand experience.
Risks of Artificial Intelligence Data-Mining by Tech Corporations With the exponential advancement of Artificial Intelligence, the notion of data being valuable regarding marketability has permeated the cultural zeitgeist.
Integrated Apple Home-Based Artificial Intelligence System Integrated Apple Home-based Artificial Intelligence system is an artificial intelligence system that has been tailored to meet the end-users’ home needs.
The Turing Test and Development of Artificial Intelligence The Turing test is conducted with two people and a program, in which the program and one person communicate with a judge.
Artificial Intelligence and the Future of Business AI’s modern field came into being in the 1950s; still, decades were spent on making serious progress in the development of an AI system and turning it from a dream into a reality.
Artificial Intelligence, Insurtech, and Virtual Reality from a Market Perspective AI, Insurtech, and Virtual reality will be presented and discussed in relation to their impact on the market, as well as the disruptions and benefits they may cause.
Artificial Intelligence: Impact on Labor Workforce The development of artificial intelligence often affects drivers and retail workers, healthcare workers, lawyers, accountants, and financial professionals.
The Issue of Artificial Intelligence Integration in Private Health Sector It is possible to develop a particular insight into the perspectives of Artificial Intelligence integration in the private health sector.
What Will Happen When AI Picks Up Social Biases About Gender? Social biases on gender will not have room when Artificial Intelligence takes over and the systems are put into everyday life.
Enabling Successful AI Implementation in the Department of Defense This paper seeks to provide a summary and discuss three main points of the article “Enabling Successful AI Implementation in the Department of Defense.”
Can the World Have a Fair Artificial Intelligence? It is important to consider issues to do with AI because the matter has adverse effects on the depreciation of human labor, information protection, and manipulation of people.
AI-Improved Management Information System This paper evaluates a current management information system and directs on ways to improve it using artificial intelligence and machine learning.
Artificial Intelligence and the Labor Market This essay will argue that although the use of AI is a controversial issue, AI could be implemented positively, allowing the effective cooperation of people and robots.
Artificial Intelligence: Human Trust in Healthcare In the modern epoch of digitalization, artificial intelligence (AI) is widely utilized in education, transportation, media, banking, navigation, and healthcare.
Artificial Intelligence: The Monstrous Entity The conversation around the artificial intelligence as a monstrous entity can provide new perspectives for all discourse communities revolving around this topic.
Implementing Artificial Intelligence and Managing Change in Nursing This paper is going to talk about a planned change, namely the implementation of Artificial Intelligence (AI) in perception, thinking, planning, learning, etc.
AI and Transitional Management The article presented the two sides of artificial intelligence from an objective perspective since the general implementation of AI is almost inevitable.
Artificial Intelligence in Machinery This essay explores an operation case, discussing the tools in AI, particularly TensorFlow and Theano, and their implementation issues.
Artificial Intelligence and Related Ethical Concerns Technological progress allows people to use AI capabilities increasingly, but this concept is also related to many ethical issues about human rights.
Thinking Processes of Artificial Intelligence This essay will discuss the topic of artificial intelligence in whether artificial intelligence can be capable of thinking processes.
The Finance Portfolio Management: Impact of Artificial Intelligence Despite the existing limitations, various artificial intelligence applications can make portfolio management much more accessible.
The Future of Artificial Intelligence in Fiction and Science Although there are numerous technological advancements, not many of them have caused such a tremendous controversy as artificial intelligence.
AI, Human Control and Safety The given evaluative analysis will primarily focus on the topic of artificial intelligence, human control, and safety.
Artificial Intelligence: The Articles Review This paper presents the annotated bibliography dedicated the artificial intelligence technologies, their safety or harm to society.
Impact of Artificial Intelligence on the Labor Market The document presents annotated article in question considers the impact the spread of artificial intelligence technology may have on the labor market.
How to Create a Fair Artificial Intelligence The current research aims to find possible ways to create a fair AI: exploring power concentration, mass manipulation, depreciation of human labor, and information protection.
Artificial Intelligence in Scientific and Fiction Works I decided to research what possible benefits can come from cooperation between scientists and science fiction writers regarding the negative image of artificial intelligence.
Artificial Intelligence: Advantages and Applications The advantages mentioned above introduce multiple opportunities for applying AI to acquire improved outcomes. Discussion of such applications.
Artificial Intelligence (AI) and Universal Basic Income Articles included in the annotated bibliography describe problems of Automation and the spread of Artificial Intelligence (AI)-based technologies.
Artificial Intelligence and Emerging Ethical Risks Technological progress went far beyond our imagination, and Artificial Intelligence became an indispensable companion in everyday life.
AI and Hardware Integration in Business Work Processes AI-driven hardware within businesses has little competition as it is the leading tool for time-saving, cost-reduced, and efficient method processes.
Artificial Intelligence and Singularity Technological development will inevitably shift humanity’s future in a highly radical way. It is especially true in the case of artificial intelligence (AI).
Artificial Intelligence and Its Usage in Modern Warfare and Healthcare This paper discusses the question of AI usage in modern warfare, and the usage of Artificial Intelligence used in healthcare in the current situation with the ongoing pandemic.
Artificial Intelligence: Potential Problems and Threats Artificial intelligence can be used for unsuitable purposes, but this is not a scientific problem but rather a moral and ethical one.
Implementation of AI in Law Practice There are many benefits of AI application to large firms that have a lot of unprocessed data or smaller firms that do not have the staff to cover all the tasks.
AI: Agent Human Interactions In this case study a system that detects the status of the baby, that is, if the baby is awake, and it has an interface implemented with agent human interaction is considered.
Artificial Intelligence in Business Administration Changes The current state of AI technology does not allow launching ambitious projects that will completely change the way businesses operate.
Artificial Intelligence in the Working Process The purpose of this paper is to describe the impact of artificial intelligence (AI) on the job and its results. AI can do the job that was done by the employee for decades.
Artificial Intelligence. Unmanned Mission Communications Communication networks are essential in facilitating the operations of autonomous systems as they are used in monitoring, collecting data, and exploring hard-to-reach areas.
Artificial Intelligence: Its Potential and Use Artificial intelligence has been presented as a technology that will not replace human beings, but help them perform tasks better.
Artificial Intelligence: Science Fiction Novels Many writers created stories and novels in the science fiction genre in an attempt to predict how the life where robots are not just machines but equal members of society would be.
“Artificial Intelligence: A Competitive Advantage for the US Army” Review The document offers a substantial review of how the implementation of artificial intelligence (AI) may become a crucial competitive advantage for the US military.
The Use of Starcraft II Video Games for AI Research The article is devoted to the rules for writing effective thesises, for each rule there are examples of good and bad writing.
AI and Machine Self-Learning Machine self-learning has become a perfect solution for complex business problems that cannot be solved by software engineering or human judgment.
Explainable Artificial Intelligence in Accounting The broad implementation of AI in such fields as accounting lays the ground for the drastic changes in management and methods that are utilized by specialists.
Blockchain and Other Artificial Intelligence Systems The project describes the basic features of blockchain and AI technologies, along with the possibilities for their future use in different spheres of human activity.
Artificial Intelligence (AI) in Health Care The use of AI has increased over the past decades, making it easier for researchers to investigate the most complicated issues.
Artificial Intelligence in Enterprise Processes AI affects ERP systems even though AI-driven solutions are not implemented by the majority of businesses. AI is integrated into ERP systems to increase customer satisfaction
Artificial Intelligence, Internet of Things, and the Impact on Facilities’ Environments The use of AI and IoT is unlikely to replace facilities’ teams because the decision-making process still requires human input.
Artificial Intelligence and Ethical Implications If we create artificial intelligence based on human intelligence, some of the less needed qualities will be omitted during the process of abstraction.
Artificial Intelligence Through Human Inquiry Much about the possible uses of A.I. and its potential capacities and abilities remains uncertain, which raises many questions as to what the future of A.I. will hold for humans.
The Artificial Intelligence Machine AlphaGo Zero The selected technology is an artificial intelligence (AI) machine by the name of AlphaGo Zero. It is an evolution of previous well-known machines from the company Deep Mind.
Artificial Intelligence in Strategic Business Management Artificial intelligence basically refers to the intelligence that is created in the software or machines by mankind.
Regional Employment and Artificial Intelligence in Japan
Artificial Intelligence and the Human Race
Medicine and Artificial Intelligence
Artificial Intelligence and Machine Learning Applied at the Point of Care
Difference Between Artificial Intelligence and Human
The Four Debatable Viewpoints One May Have About Artificial Intelligence
Artificial Intelligence and Its Impact on Accounting
Rational Choice and Artificial Intelligence
The Ethics and Its Relation To Artificial Intelligence
Artificial Intelligence and Medicine
Privacy, Algorithms, and Artificial Intelligence
Artificial Intelligence: Can Computers Think
Cognitive Science and Its Link to Artificial Intelligence
Artificial Intelligence Replacing the Art of Traditional Selling
The Beauty and Danger of Artificial Intelligence
Digital Devices for Artificial Intelligence Applications
Artificial Intelligence and the Field of Robotics
Could Artificial Intelligence Replace Teachers
Artificial Intelligence and Neuromorphic Engineering
Artificial Intelligence Based Improvised Explosive Devices
Big Data Technologies and Artificial Intelligence
Artificial Intelligence and Its Effects on Business
Modern Technology and Artificial Intelligence
Multilayered Perceptron and Artificial Intelligence
Distributed, Decentralized, and Democratized Artificial Intelligence
Artificial Intelligence and Video Games
Some Considerations About Artificial Intelligence and Its Implications
Comparing Human Intelligence With Artificial Intelligence
Artificial Intelligence During the World Today
Artificial Intelligence and the Future of Human Rights
Economic Policy for Artificial Intelligence
Artificial Intelligence for Human Intelligence and Industrial
The Morality and Utility of Artificial Intelligence
Artificial Intelligence and Behavioral Economics
Blockchain and Artificial Intelligence Technologies
The Effects Artificial Intelligence Has Had on Society and Business
Marketing and Artificial Intelligence
Artificial Intelligence and Machines Automation
People Copy the Actions of Artificial Intelligence
Artificial Intelligence for Healthcare in Africa
Healthcare System Using Artificial Intelligence
Artificial Intelligence for the Future Radiology Diagnostic Service
Artificial Intelligence and Marketing
Copyright Protection for Artificial Intelligence
The Potential and Future of Artificial Intelligence
Artificial Intelligence and the Human Mind
Expert Systems and Its Relationship With Artificial Intelligence
Artificial Intelligence and Its Effect on Mankind
The Nexus Between Artificial Intelligence and Economics
Artificial Intelligence, Based Training and Placement Management
Artificial Intelligence and Its Implications for Income Distribution and Unemployment
Machine Learning and Artificial Intelligence in Finance
The Pros and Cons of Artificial Intelligence
Artificial Intelligence and the Legal Profession
Continual Learning: The Next Generation of Artificial Intelligence
Artificial Intelligence and Its Uses
Regulation Within the Development of Artificial Intelligence
Artificial Intelligence and Computer Science
Mysteries, Epistemological Modesty, and Artificial Intelligence in Surgery
Artificial Intelligence and Cognitive Reasoning
Can Artificial Intelligence Become Smarter Than Humans?
Should Humanity Fear Advances in Artificial Intelligence?
How Does Artificial Intelligence Affect the Retail Industry?
What Are Some of the Ethical Challenges Posed by the Use of Artificial Intelligence for Hiring?
Does Artificial Intelligence Impact the Creative Industries?
Can Artificial Intelligence Change the Way in Which Companies Recruit, Train, Develop, and Manage Human Resources in Workplace?
Will Artificial Intelligence Defeat Human Intelligence?
How Can Artificial Intelligence Help Modern Society?
Can Artificial Intelligence Lead to a More Sustainable Society?
What Role Will Artificial Intelligence Play in Human Affairs in the Next Few Decades?
How Can Artificial Intelligence Help Us Understand Human Creativity?
Will Artificial Intelligence Devices Become Human Best Friend?
Why Must Artificial Intelligence Be Regulated?
Should Artificial Intelligence Have Human Rights?
Why Artificial Intelligence Won’t Dominate the Future?
How Does Artificial Intelligence Impact Today’s Society?
Will Artificial Intelligence Overpower Human Beings?
Should Artificial Intelligence Take Over the Jobs of the Tertiary Sector?
How Will Artificial Intelligence Impact the World?
Should People Develop Artificial Intelligence?
How Does Mary Shelley’s Depiction Show the Threats of Artificial Intelligence?
What Can Artificial Intelligence Offer Coral Reef Managers?
How Will Artificial Intelligence Affect the Job Industry in the Future?
Should the Innovative Evolution of Artificial Intelligence be Regulated?
Will Artificial Intelligence Have a Progressive or Retrogressive Impact on Our Society?

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This essay topic collection was updated on March 8, 2024 .

Top 20 Artificial Intelligence project ideas for Beginners

Artificial Intelligence is a technique that enables machines to mimic human behavior. It is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. So based on all these features, we have curated the top 20 Artificial Intelligence Project ideas which are ideal for beginners. If this sounds intriguing, do read the blog till the very end.

Music Recommendation App
Stock Prediction
Social Media Suggestion
Identify inappropriate language and hate speech
Lane line detection while driving
Monitoring crop health
Medical diagnosis
AI powered Search engine
AI powered cleaning robots
House security
Handwritten notes recognition
Loan Eligibility Prediction
Face filter using facial detection
E commerce recommendation engine
Detecting fake products
Facial Emotion Recognition
AI Healthengine
Trying on online clothes and accessories
Spam email identification

1). Chatbot : A chatbot is an artificial intelligence software that can be used to start a conversation with a user through websites, mobile apps, calls, or messaging applications. Chatbots are increasingly becoming popular. Many of the company’s websites use chatbots to communicate with their customers, it’s used in almost all fields, be it education, medicine, Information Technology, and even banking websites, now having chatbots. For eg, EVA by HDFC bank. Now, if you’re a beginner, then you can program a simple version of a chatbot. There are many chatbots available online. Just learn from them, identify the basic structure and then build your own chatbots using the structure. You can then enhance it using your creativity and make it better.

Join ChatGPT course to understand the AI-powered language model that revolutionizing the way we communicate.

2). Music Recommendation App : Due to AI, music recommendation app which can also be known as music recommendation engines makes it quicker and easier to show music recommendations that are tailored to each user’s interests and preferences.

So how does this work? First, it collects all the data: what are the songs that the users listen to the most, what is the genre of the song, and which language is the song the user listens. Next, it stores all these data and analyses. It then recommended songs from a similar genre and the same language and the songs whose ratings are high. You would have seen this in apps like Spotify or wynk, where they have an entire section on songs recommended for you. So they use AI to make this recommendation engine. You can program this music recommendation app by learning from some online blogs or watching YouTube videos.

3). Stock Prediction : Now, many people invest in stocks, and they need a stock predictor in order for them to know when to buy the stocks. Now, it is not possible to predict what will happen in the future, but we can make estimations and informed forecasts based on the data we have in the present and past regarding the stocks. This is known as Technical Analysis, which is used to predict the stock’s price direction, will the value of the stock increase or decrease after a particular time. So, for your projects, you can create an application that analyses the trends and the stock market and offers data-driven insights. You can start off by keeping your stock prediction cycle small and then go on and try for higher values and insights. Also, if you design a good stock prediction application, there will be a great value & demand for such systems, and will make your career.

4). Social Media Suggestion: AI is being used in most of the popular social media networks that we use on a day-to-day basis. For example, Facebook uses AI and advanced machine learning to serve you all the content based on your preferences and to recognize people’s faces in photos, so you can tag them and also target users for the right advertisement. Also, Instagram which is now owned by Facebook uses AI to identify visuals. Next, LinkedIn uses AI to offer job recommendations based on your qualifications and interest, it suggests people to connect with, this also happens in Facebook. Next, Snapchat uses AI technology, to track your facial features and add filters that move with your face in real-time.

So, these were just some examples of how social media uses AI. So, you can create a project which can do any of the following tasks, like suggest the users to connect with people they might know, suggest to them some content they might like to watch or suggest some products they might be interested in and so on.

5). Identify inappropriate language and hate speech: Now, this is a project which sounds easy, but it is quite hard to identify all the hate speeches and inappropriate language. There are many companies who are trying to create this system such as Facebook, Twitter, and YouTube. So, for this project, you can use detection techniques that identify the character in a context and then compare it to content that’s already been removed as hate speech. Now, usually, this would be used for identifying any hate speech in any post(like Facebook or Twitter posts). So, design an AI system that looks into things like the text in a post, the reactions and comments to the post, and how closely it matches common phrases of hate speech. Also, if it contains at least one inappropriate word, then identify those words and report them.

6). Lane line detection while driving: Now, many of you know that self-driving cars are gaining a lot of popularity. Now, as a beginner, it would be very hard to design this, but you can design a part of it which is lane line detection while driving. This Lane line detection technique is used in many self-driving autonomous vehicles as well as line-following robots.

So you can use computer vision techniques and AI to teach the vehicle to go in a particular lane. You can use computer vision techniques such as colour thresholding to detect the lanes, so usually the lanes are colored in white colour and usually, there are double lanes in the middle of the road which separate the directions the vehicle runs in. Then there is usually one white line at the end of the road after which is the edge of the road. Usually, with all this data, you can design an AI-powered system that detects lane lines.

7). Monitoring crop health : Artificial Intelligence is being increasingly adopted as a part of the agriculture industry’s evolution. Using AI, you can perform predictive analytics to determine: what is the right date for sowing the seeds to obtain maximum yield after the previous harvest, get insights on the crop health, soil health, the fertilizer recommendations and also the next 7-day weather forecast. You can create a project which uses AI to monitor the health of the crop and check for diseases, by using various images of plants that had the same diseases. So, when a user collects the image of the plants it will be matched with images that are already stored and then diagnosis the particular disease and then maybe even provide a intelligent spraying technique and treatment automatically

8). Medical diagnosis : AI is being used in the medical industries for layering risk, identifying hotspots in chronic disease, and accounting for the social determinants of health.

For your project, you can use AI to develop a software that can be programmed to accurately spot signs of a certain disease in medical images such as MRIs, x-rays, and CT scans. For example, you can design a system that uses AI for cancer diagnosis by processing photos of skin lesions. This can be very helpful to diagnose patients more accurately and also prescribe the most suitable treatment.

9). AI powered Search engine : Design a search engine which is powered by AI which will scan billions of content available in the web and match the exact search sentences or keywords and will show the relevant information, images, videos, text and other documents. You can also use a ranking algorithm that will rank the content for a particular keyword based on various factors like the engagement rate i.e, for how long did the user spend his/her time on the website, is the content from a reliable website and so many factors. You can refer to some online blogs or watch some videos to get started. Also, for this project, you need to know a little bit about networks and how the data passes on the internet from one place to another.

10). AI powered cleaning robots: Today’s AI-powered robots possess no natural general intelligence, but they are capable of solving problems and thinking in a limited capacity. You can design a robot that uses artificial intelligence to clean a room by scanning the room size, identifying obstacles and remembering the most efficient routes for cleaning. For starters, you can design a robot that does only one of these things, then enhance it until it can effectively clean the room.

11). House security: So for this project, you can design a system which uses AI to scan and identify the face of the visitor. First the facial structure of the family members or someone who frequently visits the house can be scanned and stored. So, every time a visitor comes near the gate, the system can scan the face and if it matches the existing facial structure that is stored in the database, it can open the door and allow the person to pass, else gate can remain shut and the people living in the house could be notified that a person is waiting outside.

12). Handwritten notes recognition: Handwriting character recognition refers to the computer’s ability to detect and interpret alphabets and numbers. These inputs could be from various sources like paper documents, notes on phone, photos and other sources. Note that handwriting characters remain complex since different individuals have different handwriting styles. So you can develop a system that uses AI to scan the handwritten notes and convert them into digital format. You can use an artificial neural network, which is a field of study in artificial intelligence to design this system.

13). Loan Eligibility Prediction: One of the major problems the banking sectors face is the increasing rate of loan defaults, so the employees find it difficult to decide who they should give loans to and who not to. Even if they do give, what are the chances of the person returning the loan amount?

So to solve this problem, You can use AI to design a program that predicts whether an individual should be given a loan by assessing various attributes like their salary, their previous loans details(did he pay all the installments on time) and many more and then notify whether or not to approve the loan. This can make the process easier of selecting suitable people from a given list of candidates who applied for a loan.

14). Face filter using facial detection: This is a very interesting project. You design a system that scans the face of the users and then add filters. So the system uses AI to recognise a few of the facial features, like eyelids, cheekbones, jawline, nose bridge etc. and then based on these calculations, it then add filters. Now, this project is inspired from Snapchat which also uses AI to identify the user’s faces and then add a filter.

15). E commerce recommendation engine : Have you ever liked any clothing item on any e-commerce website, and then you see the same clothing item in the ads of some website or on social media. AI is responsible for this. In this project, you can build an E commerce recommendation engine using the similarity among the background information of the items or users to propose recommendations to users. So, for example if the user has searched for apple phones, then you can design a recommendation engine that recommends apple phones to the user. Or you can identify trends and patterns in previous and other user-item interactions and advise similar recommendations to a present user based on his existing interactions. So, for example, if the person has bought a formal shirt, then you can design your recommendation engine, to recommend more formal clothing and accessories

16). Detecting fake products: There are many duplications happening for different products. So design a system which uses Artificial Intelligence to analyze the product and determine if it is authentic or not. Unlike humans, machines can analyze minutest of inconsistencies or faults in shape, colour, texture, size and many more. They can calculate all these and analyze if the product is fake or not. This accuracy will be based on numbers of images and data of the original product, it will then compare and detect the fake ones.

17). Facial Emotion Recognition : Now, everything that’s happening in a sci-fi movie, could be our future. There are a variety of fields where Artificial Intelligence is used. One such area of interest is detecting human emotions. There are many top companies investing a lot of money in doing this. So, you can design a facial emotion detection and recognition system that can be used to identify human facial expressions. So for this, first the system would have to analyze the facial expression for some time and then perform facial feature extraction and classify the facial expression. For starters, you can design the system to identify only one expression, maybe just happy or normal. Then you can enhance it and try different emotions.

18). AI Healthengine: Create a project that will use AI to give personalized health guidance to a user. The user must provide all their medical reports and based on that, the AI system will check for any pre-existing conditions, ongoing health concerns, and gaps in general health knowledge. Then the health engine could combine both these personal details and external health data to provide informed advice to the user. It can also help users with prescription support, vaccination advice, recommended doctor visits, and specific condition guidance.

19). Trying on online clothes and accessories: Now, you would have already heard about this feature, if you ever visited the lenskart app, here you can design an AI system that takes the input images and computes the person’s body model, representing their pose and shape. The segments are then selected on which the dresses are going to be displayed on, like for eg, a shirt on the body, gloves for hands, and so on and then when the user selects a particular dress, the system can combine them with the body model and update the image’s shape representation.

20). Spam email identification: Spam detection means detecting unsolicited emails by identifying the text content of the email. So for the project, create an artificial neural network to detect and block spam emails and also ensure that the user only receives notifications regarding the emails that are crucial to them. You can also enhance this by tuning it to user preferences. For example newsletters or updates that one person likes, will be disliked by someone else, so include features that will filter the email based on individual user preferences.

21) Blindness Detection : A Blindness Detection AI project uses computer vision and deep learning to analyze retinal images and detect signs of eye conditions that may lead to blindness. It aims to facilitate early diagnosis, timely intervention, and preventive measures, potentially reducing preventable blindness. Users can upload images to the AI system, which provides predictions, risk assessments, and referral recommendations for further examination or treatment. Continuous improvement and data privacy are key considerations in the project’s development.

22). Real-time Face Mask Detector: A real-time face mask detector AI project is a computer vision application that uses AI algorithms to detect whether a person is wearing a face mask in real-time video streams or images. It helps enforce mask regulations, enhance public safety, and optimize resource allocation. The system uses a dataset for training, a CNN model for inference, and can be integrated into user-friendly interfaces to provide alerts and notifications when masks are not worn. Privacy and data protection considerations are essential, and continuous model refinement ensures accuracy.

23). Self-Driving Car Behavioral Cloning: The Self-Driving Car Behavioral Cloning AI project aims to teach autonomous vehicles to imitate human drivers by learning from their driving data. It involves collecting extensive datasets of human driving behaviors, training deep learning models using convolutional neural networks, and validating the models in simulation environments before real-world testing. The benefits include faster deployment and human-like driving behavior, but challenges like data bias and ethical considerations must be addressed. Overall, the project contributes to the advancement of safer and more human-like self-driving technology.

24). Building a Telegram Bot: Building a Telegram Bot AI project involves creating an intelligent chatbot on the Telegram platform. It uses natural language processing (NLP) and machine learning models for understanding user inputs and generating relevant responses. The bot can be designed for various purposes, such as customer support, content delivery, or games, and offers improved user experiences, automation, and scalability. Security and proper error handling are essential considerations during development. Once deployed, the bot interacts with users in real-time, providing valuable services and integrating with external services and APIs. Overall, the project enhances user experiences and offers convenient access to information and services within the Telegram messaging app.

25). Keyword Research: Keyword Research using Python is an AI project that automates the process of finding valuable keywords for SEO and content marketing. It scrapes search data, applies NLP and AI algorithms to analyze keywords, and calculates metrics like search volume and competition. The project generates keyword recommendations, aids data-driven decisions, and enhances SEO performance. It offers time efficiency, scalability, and data-driven insights for content creators and SEO professionals.

If you wish to learn AI in detail then I suggest you watch this YouTube video:

Artificial Intelligence Tutorial for Beginners | Edureka

Why do AI Projects fail?

AI projects can fail for various reasons, and understanding these pitfalls is crucial for ensuring successful implementations. Here are some common reasons why AI projects may fail:

Insufficient Data Quality and Quantity: AI models heavily rely on high-quality and diverse data for training. If the data used is limited, biased, or contains errors, the AI model’s performance can be compromised, leading to inaccurate or unreliable results.
Lack of Clear Objectives: If the project’s objectives are not well-defined or align poorly with the organization’s goals, the AI project may lack direction, fail to meet expectations, or not deliver meaningful value.
Inadequate Expertise and Talent: AI projects require skilled professionals, including data scientists, machine learning engineers, and domain experts. A lack of expertise or a shortage of talented individuals can hinder the project’s progress and outcome.
Overlooking Ethical Considerations : AI systems can have significant societal impacts, and failing to consider ethical concerns like data privacy, bias, and fairness can lead to negative consequences and public backlash.
Complexity and Overambitious Goals: Complex AI projects with lofty goals can be challenging to execute successfully, especially without a clear step-by-step approach. Overambitious objectives may lead to unrealistic timelines and resource constraints.
Integration Challenges: Implementing AI solutions into existing systems or workflows can be difficult. Integration issues and resistance to change within the organization can hinder the successful adoption of AI technologies.
Lack of Continuous Monitoring and Maintenance: AI models require ongoing monitoring and updates to adapt to changing data and business environments. Neglecting this aspect can lead to performance degradation and inefficiencies.
Cost and Resource Constraints: AI projects can be expensive and resource-intensive. A lack of adequate budget or resources may prevent the project from reaching its full potential or being scaled appropriately.
Inadequate Testing and Validation: Proper testing and validation are essential to identify and rectify errors or biases in AI models. Skipping this step can lead to unreliable outputs and potential harm.
Unrealistic Expectations: AI technologies have limitations, and setting unrealistic expectations can lead to disappointment and a perception of project failure, even if progress has been made.

To overcome these challenges and increase the likelihood of success, organizations should invest in proper planning, data preparation, talent acquisition, and ethical considerations. An iterative approach, with continuous monitoring and feedback, allows for course corrections and optimizations during the project’s lifecycle. Transparency and open communication within the team and stakeholders are also crucial for addressing any issues proactively.

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How to Launch a Career in AI ?

To launch a career in AI, follow these key steps:

1. Build a solid educational background in computer science or related fields.

2. Develop programming skills, especially in Python, R, or Java.

3. Learn mathematics and statistics for understanding AI algorithms.

4. Enroll in online courses and tutorials to learn AI concepts and tools.

5. Gain practical experience through personal projects, competitions, and open-source contributions.

6. Specialize in a specific AI subfield, such as machine learning or computer vision.

7. Engage with AI communities and attend events to network with professionals.

8. Seek internships or entry-level positions to gain industry experience.

9. Obtain AI certifications to validate your expertise.

10. Stay updated with the latest research and trends in AI.

11. Build a strong portfolio showcasing your AI projects and achievements.

12. Search for AI-related job opportunities in various industries.

Continuously improve your skills, stay persistent, and embrace learning opportunities to succeed in the dynamic field of AI.

In this article, you learned about the Top 20 AI Projects Ideas . To learn more concepts on Artificial Intelligence , then check out our Artificial Intelligence Course . This Artificial Intelligence course online will help you learn Python, Predictive Analytics, ML, Deep Learning, Natural Language Processing(NLP), Sequence Learning, etc. This AI certification course provides hands-on experience on 20+ industry projects, and 100+ case studies.

Also, Elevate your skills and unlock the future of technology with our Prompt Engineering Certification Course ! Dive into the world of creativity, innovation, and intelligence. Harness the power of algorithms to generate unique solutions. Don’t miss out on this opportunity to shape the future – enroll now and become a trailblazer in Generative AI. Your journey to cutting-edge proficiency begins here!

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106 Artificial Intelligence Essay Topics & Samples

In a research paper or any other assignment about AI, there are many topics and questions to consider. To help you out, our experts have provided a list of 76 titles , along with artificial intelligence essay examples, for your consideration.

💾 Top 10 Artificial Intelligence Essay Topics

🏆 best essay topics on artificial intelligence, 🖱️ interesting artificial intelligence topics for essays, 🖥️ good ai essay titles, ❓ artificial intelligence research questions.

AI and Human Intelligence.
Computer Vision.
Future of AI Technology.
Machine Learning.
AI in Daily Life.
Impact of Deep Learning.
Natural Language Processing.
Threats in Robotics.
Reinforcement Learning.
Ethics of Artificial Intelligence.
The Problem of Artificial Intelligence The introduction of new approaches to work and rest triggered the reconsideration of traditional values and promoted the growth of a certain style of life characterized by the mass use of innovations and their integration […]
Artificial Intelligence: The Helper or the Threat? To conclude, artificial intelligence development is a problem that leaves nobody indifferent as it is closely associated with the future of the humanity.
Artificial Intelligence: Positive or Negative Innovation? He argues that while humans will still be in charge of a few aspects of life in the near future, their control will be reduced due to the development of artificial intelligence.
Artificial Intelligence and Humans Co-Existence Some strategies to address these challenges exist; however, the strict maintenance of key areas under human control is the only valid solution to ensure people’s safety.
Artificial Intelligence and Related Social Threats It may be expressed in a variety of ways, from peaceful attempts to attract attention to the issue to violent and criminal activities.
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Artificial Intelligence in the Documentary “Transcendent Man” The artificial intelligence is becoming a threat to the existence of humanity since these machines are slowly but steadily replacing the roles of mankind in all spheres of life.
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What Progress Has Been Made With Artificial Intelligence? According to Dunjko and Briegel, AI contains a variety of fields and concepts, including the necessity to understand human capacities, abstract all the aspects of work, and realize similar aptitudes in machines.
Artificial Intelligence: A Systems Approach That is to say, limitations on innovations should be applied to the degree to which robots and machine intelligence can be autonomous.
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Artificial Intelligence Reducing Costs in Hospitality Industry One of the factors that contribute to increased costs in the hospitality industry is the inability of management to cope with changing consumer demands.
Artificial Intelligence in Healthcare Delivery and Control Side Effects This report presents the status of AI in healthcare delivery and the motivations of deploying the technology in human services, information types analysed by AI frameworks, components that empower clinical outcomes and disease types.
Artificial Intelligence for Diabetes: Project Experiences At the end of this reflective practice report, I plan to recognize my strengths and weaknesses in terms of team-working on the project about AI in diabetic retinopathy detection and want to determine my future […]
Artificial Intelligence Company’s Economic Indicators On the other hand, it is vital to mention that if an artificial intelligence company has come of age and it is generally at the level of a large corporation, it can swiftly maneuver the […]
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Artificial Intelligence and the Associated Threats Artificial Intelligence, commonly referred to as AI refers to a branch of computer science that deals with the establishment of computer software and programs aimed at the change of the way many people carry out […]
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20 Artificial Intelligence Project Ideas for Beginners [2024]

Explore exciting and innovative artificial intelligence project ideas to kickstart your journey into the world of AI and Deep Learning | ProjectPro

In this space, we will explore the most innovative and impactful Artificial Intelligence projects, from cutting-edge research to real-world applications. Whether you're a tech enthusiast or simply curious about the future of AI, you'll find plenty of exciting ideas and insights to inspire you. Let's dive in!

Artificial Intelligence has made a significant impact on our daily lives. Every time you scroll through social media, open Spotify, or do a quick Google search, you are using an application of AI. The AI industry has expanded massively in the past few years and is predicted to grow even further, reaching around 126 billion U.S. dollars by 2025. Multinational companies like IBM, Accenture, and Apple are actively hiring AI practitioners. The median salary of an AI engineer as of 2021 is $171, 715 that can go over $250,000.

The field of AI is vast, and there are many areas within the industry that you can choose to specialise. Say , if you are intrigued by facial recognition systems and image generation, you can choose to work in the field of computer vision . If you’d like to build models that can converse with people and learn human language, you can work in the field of NLP (Natural Language Processing) .

There is a lot of work being done today for the advancement of Artificial Intelligence. Companies need AI specialists who can build and deploy scalable models to meet growing industry demands. It isn’t tough to get started in the field of AI. While there is complexity involved in building machine learning models from scratch, most AI jobs in the industry today don’t require you to know the math behind these models. Many companies require individuals who can build AI solutions, scale them, and deploy them for the end-user. Many high-level libraries and frameworks can help you do this without an in-depth knowledge of how the models work.

There are a variety of AI projects you can do to gain a grasp of these libraries. If you are looking to break into AI and don’t have a professional qualification, the best way to land a job is to showcase some interesting artificial intelligence projects on your portfolio or show your contributions to open-source AI projects.

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Building Artificial Intelligence projects not only improve your skillset as an AI engineer, but it also is a great way to display your artificial intelligence skills to prospective employers to land your dream future job.

20 artificial intelligence projects ideas for beginners to practice in 2024, latest open source ai projects.

Current AI Projects | Google AI Projects
Artificial Intelligence Projects for Students

Top 3 AI Projects on Github

How to launch a career in ai .

Without much ado, let’s explore 20 Artificial Intelligence projects you can build and showcase on your resume. These AI projects will have varying levels of difficultyâ€Š- â€Šbeginner, intermediate, and advanced. ProjectPro industry experts suggest starting with simple artificial intelligence projects if you are new to the AI industry. As your skills progress, you can move on to practising more advanced AI based projects .

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Top Artificial Intelligence Projects for Beginners

Here are a few projects on artificial intelligence in the field who are interested in learning ai concepts.

1. Resume Parser AI Project

Recruiters spend a lot of time skimming through resumes to find the best candidate for a job position. Since there can be hundreds of applications for a single position, this process has been automated in several waysâ€Š- the most common is keyword matching. Resumes are shortlisted and read by the recruiters based on a set of keywords found in a candidates resume. Otherwise, the resume is discarded, and the candidate is rejected for the job. However, this screening process has many drawbacks. Candidates are aware of the keyword matching algorithm, and many of them insert as many keywords as possible into their resumes to get shortlisted by the company.

You can build a resume parser with the help of artificial intelligence and machine learning techniques that can skim through a candidate’s application and identify skilled candidates, filtering out people who fill their resume with unnecessary keywords.

You can use the Resume Dataset available on Kaggle to build this model. This dataset contains only two columnsâ€Š—â€Šjob title and the candidate’s resume information.

The data is present in the form of text and needs to be pre-processed. You can use the NLTK Python library for this purpose. Then, you can build a clustering algorithm that groups closely related words and skills that a candidate should possess in each domain. Words that are similar in context (and not just keywords) should be considered. You can assign a final weightage score to each resumeâ€Š—â€Šfrom 0 (least favourable) to 10 (most favourable). This is the most beginner-friendly project if you want to learn AI.

Dataset: Kaggle Resume Dataset

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2. Fake News Detector Project in AI

Fake news is misleading or false information that is circulated as news. It is often difficult to distinguish between fake and real news, and it isn’t until the situation gets blown out of proportion that it comes to light. The spreading of fake news becomes especially dangerous during times like elections or pandemic situations. Fake rumours and misinformation that pose harm to human lives are threatening to people and the society.

Fake news needs to be detected and prevented early, before it causes panic and spreads to a large number of people.

For this very interesting project, you will build a fake news detector , you can use the Real and Fake News dataset available on Kaggle.

You can use a pre-trained machine learning model called BERT to perform this classification. BERT is a Natural Language Processing (NLP) model that has been made open-source. You can load BERT into Python and just add one additional output layer for your text classification task.

3. Translator App

If you are interested in getting started in the field of Natural Language Processing , you should try building a translator app with the help of a transformer.

A transformer model extracts features from sentences and determines the importance of each word in a sentence. A transformer has an encoding and decoding component, both of which are trained end-to-end.

You can build your own AI translator app with a transformer. To do this, you can load a pre-trained transformer model into Python. Then, transform the text you want to translate into tokens and feed it into the pre-trained model.

You can use the GluonNLP library for this purpose. You can also load the train and test dataset for this AI project from this library.

Python Package: GluonNLP

4. Instagram Spam Detection

Have you ever received a notification that someone commented on your Instagram post? You excitedly pick up your phone and open the app only to find that it’s a bot promoting some knockoff brand of shoes. The comment section of many Instagram posts is filled with bots. They can range from annoying to dangerous, depending on the type of call to action they require from you.

You can build a spam detection model using AI techniques to identify the difference between spam and legitimate comments.

You might not be able to find a dataset that has a collection of Instagram spam comments, but you can collect the data for this analysis by scraping the web. Access the Instagram API with Python to get unlabelled comments from Instagram.

You can use a different set of data for training, like Kaggle’s YouTube spam collection dataset. Then, use keywords to classify words that commonly appear in spam comments.

Use a technique like N-Gram to assign weightage to words that tend to appear in spam comments, then compare those words with each scraped comment from the web. Another approach you can take is the use of a distance-based algorithm like cosine similarity . These approaches will yield better results based on the type of pre-processing you apply.

If you remove stop-words, whitespaces, punctuation and clean the data correctly, you will find that the algorithm performs better as it can match similar words with each other.

You can also use a pre-trained model like ALBERT for better results. While distance or weightage matching algorithms work well in finding similar words, they are unable to grasp the context of a sentence.

NLP models like BERT and ALBERT can do this better, as they consider factors like sentence context, coherence, and interpretability.

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5. Object Detection System

You can demonstrate skills in the field of computer vision with this project. An object detection system can identify classes of objects present within an image by utilizing computer vision techniques in the background.

For example, suppose an image contains a picture of you working on a laptop. In that case, an object detection system should be able to identify and label you (human) and the computer, along with your position in the image.

You can use Kaggle’s Open Images Object Detection dataset for this project. There is a pre-trained object detection model that has been made open-source called SSD. This model was trained on a dataset of everyday objects called COCO and can identify things like tables, chairs, and books .

You can further train the output layer of this model on the Kaggle Open Images dataset to build your object detection system with high accuracy.

Dataset: Kaggle Open Images Object Detection Dataset

6. Animal Species Prediction

Another interesting computer vision project you can do is to predict an animal’s species based on an image.

You can do this with the Animals-10 dataset on Kaggle. There are ten different categories of animals in this datasetâ€Š—â€Šdog, cat, horse, spider, butterfly, chicken, sheep, cow, squirrel, elephant.

This is a multi-class classification problem, and you will need to predict the species of the animal based on its picture in the dataset.

You can use a pre-trained model called VGG-16 for this purpose. You can load this model into Python with the Keras library.

VGG-16 is a Convolution Neural Net (CNN) architecture trained on ImageNet, which contains over 14 million images. It consists of pictures of everyday objects, fruits, vehicles, and certain species of animals.

After loading the VGG-16 model into Python, you can train on top of it with the labelled images in the Kaggle dataset to classify the ten different types of animals.

Dataset: Animals-10 Kaggle Dataset

7. Pneumonia Detection with Python

Many diseases such as cancer, tumours, and pneumonia are detected using computer-aided diagnosis with the help of AI models.

There are open image datasets available on Kaggle for disease detection. You can try your hand with disease prediction on one of these datasetsâ€Š—â€Šthe Chest X-Ray Images (Pneumonia Detection) dataset on Kaggle.

This dataset consists of three types of labelled lung X-Ray imagesâ€Š—â€ŠNormal, Bacterial Pneumonia, and Viral Pneumonia. You can build a model that categorises a patient’s health condition into one of these three categories based on an X-Ray image of their lungs.

To build this model, you can use a Python library called FastAI. FastAI is an open-source library that allows users to quickly create and train deep learning models for various problems, including computer vision and NLP.

This library provides a higher level of abstraction than Keras and is very easy to work with if you are a beginner. A problem that takes over 30 lines to solve with Keras can be solved in only five lines of code with FastAI.

You can download the ResNet50 pre-trained model from FastAI and train on top of this model to build the classifier. ResNet50 allows us to train incredibly deep neural networks with over 150 layers, and training on top of it will give you good results.

Dataset: Kaggle Chest X-Ray Images

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8. Teachable Machine

If you are an AI enthusiast, you’ve probably heard about Google’s Teachable Machine. Teachable Machine is a web-based tool that was created to make machine learning accessible to everyone.

If you visit Google’s Teachable Machine site, they allow you to upload pictures of different classes and then train a client-side machine learning model on these pictures.

Graphical user interface, application, websiteDescription automatically generated

An example of how Teachable Machine works:

There are two classes of images you need to upload. First, you upload around 100 pictures of yourself and label them as Class 1. Then, you upload another 100 photos of your cat and label it as Class 2.

Then, you click on the “Train Model” button, and a client-side machine learning model will learn to distinguish between pictures of you and your cat.

You can then use this model to make new predictions on images.

Google released Teachable Machine some time back, so people who aren’t well versed with AI can visit the site and train their models. It allows non-technical people to get acquainted with machine learning.

You can build your version of Google’s Teachable Machine.

The steps you need to take are as follows:

Create a client-side application that allows users to upload images of multiple classes.

Collect the images, transform them, and train them on top of a pre-trained model. You can do this on the client-side using a language like JavaScript. Pre-trained machine learning models can be accessed in JavaScript through languages like ml5.js and tensorflow.js.

After the model is trained, send a notification on the screen , so the user knows it’s done. Then, get the user to upload pictures of each class to make predictions on new images.

9. Autocorrect Tool

Autocorrect is an application of AI that we use every day. It makes our lives easier by taking care of spelling mistakes and grammatical errors.

To build an autocorrect tool in Python, you can use the TextBlob library in Python. This library has a function called ‘ correct().’ If you call this function on a piece of text, it will identify incorrect words and replace them with the closest word to the one typed.

It is a relatively simple task, but it’s essential to keep in mind that the TextBlob library isn’t perfect. The underlying algorithm cannot detect certain mispelt words and makes corrections when the initial word was correct, like replacing ‘is’ with ‘ as.’

This tool isn’t able to grasp the context between thee two words and doesn’t do any kind of mapping to identify words that are commonly used together. For example, if I were to write ‘ I like your short’ instead of ‘ I like your shirt,’ the algorithm wouldn’t correct me. These words are spelt correctly but don’t fit in the context of the sentence.

You can enhance the limitations of this model by building your ownâ€Š—â€Šyou can use a pre-trained NLP model like BERT that has been trained to predict words that fit into a specific context.

10. Fake Product Review Identification

This AI project is similar to the Instagram spam detection project listed above. There are many business owners out there who fabricate reviews for their products to get more sales misleading individuals who are looking to purchase high-quality products.

You can build a fake review identification system to solve this problem. Kaggle has a dataset called Deceptive Opinion Spam Corpus that you can use for this project. This dataset contains 1600 hotel reviewsâ€Š- 800 of them are positive, and another 800 are negative.

These reviews are already labelled, so you just need to do some data pre-processing and tokenisation on all this data before training your model. You can use transfer learning for this purpose with pre-trained models like BERT, RoBERTa and XLNet.

Intermediate/Advanced Level Artificial Intelligence Project Ideas

The projects in this section of advanced AI projects aren’t tricky, but they require you to have more advanced knowledge of AI skills to build and deploy end-to-end AI projects. These projects are best suited for AI professionals.

1. Price Comparison Application

Have you ever seen a dress in a store and wanted to know the lowest price you could get it ?

In this AI project, you can build an app that allows users to upload a picture of the item they want to buy. Then, the app will scan through many online stores and find the lowest price for the item . This way, the user gets the best possible deal.

To create an app like this, you will first need to create an algorithm that can identify objects in an image. For example, if the user uploads a picture of a pink floral dress, the algorithm should identify the colour and style of the dress correctly.

You can use transfer learning for this AI project and train on top of models like VGG-16 with a pre-existing database of item descriptions. Once the model is built, you can give the user a choice to specify additional information about the itemâ€Š—â€Šbrand, outlet, etc.

After collecting all this information, you need to build an algorithm that identifies online stores based on the brand information provided. Create an automated tool that opens these sites and scrapes pricing information from at least 3–4 online stores.

Then, return the site name and pricing information to the user, along with a link to where they can buy the item from. The only part of this project that incorporates AI ideas is the item description based on the image uploaded by the user. Everything else requires you to have model deployment skills, the ability to render information quickly to the user, and a firm grasp of data science programming languages .

Dataset: Kaggle Fashion Dataset

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2. Ethnicity Detection Model

Race breakdown is an essential part of customer segmentation in companies. However, it isn’t always easy to access this data as it isn’t publicly available.

You can build an ethnicity detection model that detects a person’s ethnicity from their picture.

OpenCV doesn’t have a package that can detect race yet, but you can build your own CNN or use transfer learning to build this model.

You can use the UTKFace dataset for training. Ethnicity detection models developed using this dataset have been able to achieve an accuracy of almost 0.80.

Dataset: Kaggle UTKFace Dataset

GitHub: Ethnicity Detection in Python

3. Traffic Prediction

Have you ever been stuck in a sudden traffic jam for over an hour? If you knew that there would be heavy traffic, you would’ve taken an alternate route to save time.

You can build a traffic jam prediction model using deep learning techniques in Python. You can use openly available Waze datasets for this purpose. You can get data of various traffic event types, along with their date, time, and exact location. You can then build a model that predicts the location and time of the next traffic jam.

There are many existing models and research papers on AI ideas implementation that you can read, and many different methodologies have managed to produce high results.

One approach that managed to win a hackathon was the use of RNNs to predict severe traffic jams. Waze data was used to identify heavy traffic events. Then, a sequence of events leading up to the traffic jam was recorded along with their timestamps to train an RNN.

The model was built purely in Python with the Keras library and delivered highly accurate results.

Dataset: Waze Open Dataset

4. Age Detection Model

When we look at a person’s face, we can usually discern the age group they belong to. We can tell if a person is young, middle-aged, or old. In this AI project, you can automate this process by creating a deep learning age detection model. Companies often use demographic data to market their products better and define their target audience. However, this data isn’t always easy to get.

Firstly, users of social media platforms like Facebook often lie about their age. This information is also often hidden and isn’t made publicly available. By building an age detection model, you can easily predict a person’s age using their profile picture and don’t need to waste time trying to scrape data that isn’t made public.

You can do this easily with a library called OpenCV. OpenCV is an open-source library used for image processing and computer vision. You can use it to process image data quickly to identify faces, objects, and even hand-writing. You can install the OpenCV library and access it easily with Python. OpenCV has a package called DNN (Deep Neural Networks) that can be used to import models from well known deep learning frameworks. You can use a framework called Caffe for this task which has pre-trained models for age and gender.

5. Image to Pencil Sketch App

In this advanced level artificial intellignece project, you can create a web application that converts an image uploaded by a user into a pencil sketch.

To do this, you can take the following steps:

Create a front-end application that allows users to upload a picture of their choice. You can do this using HTML and JavaScript.

In the back-end, use Python and import OpenCV.

OpenCV has a package that allows you to convert images into grayscale, invert the colour of an image, and smoothen the image, so it looks like a sketch.

Once the final image is obtained, display it on the screen for the user to see.

This is a relatively simple AI project since libraries are available that will handle the image conversion for you. However, the more challenging part is building a functional app that users can interact with since it requires knowledge of languages outside of Python.

6. Hand Gesture Recognition Model

You can create a gesture recognition web application in Python. To do this, you can use the hand gesture recognition database on Kaggle. This dataset consists of 20,000 labelled gestures.

You can train this dataset on VGG-16. You can also use OpenCV to collect a live stream of video data and use the model to detect and make predictions on hand gestures in real-time.

You can even build a hand gesture recognition app. Deploy your model on a server and let it make predictions as users hold up a variety of hand gestures.

Dataset: Kaggle Hand Gesture Recognition

7. Text Generation Model

In this project, you can build a deep learning model that can automatically complete a sentence. The model will predict the end of a sentence given the first few words as a writing prompt.

You can use this model to write stories or complete funny text messages.

To build a text generation model, you can use OpenAI’s GPT-2 model. GPT-2 is an open-source artificial intelligence that users can access for a variety of NLP tasks.

You can access GPT-2 in Python by cloning their GitHub repository, which we will link to below. Once you clone the repository, you can simply run the Python files and provide input text string. Also, give the number of words you want GPT-2 to generate based on the text entered and GPT-2 will come up with an entire article with the number of words you mentioned.

A lot of the text generated by GPT-2 doesn’t make sense, but you can use it to re-create your favourite stories or even write an article. A lot of it is literary garbage, but it’s fun!

Again, you can turn this into an app very quickly with just a few lines of code. Let the user enter a word at the prompt and display an article written by GPT-2.

GitHub: GPT-2

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8. Colour Detection

In this artificial intelligence project, you will build a model that can detect the colour of an image.

You can use the Color Recognition dataset on Kaggle. To build this project, you will need to convert each image in the dataset into RGB channels. Then, you can calculate the distance from the colour in the input image to the three different colour channels with a formula like this:

d = abs(Redâ€Š—â€ŠithRedColor) + (Greenâ€Š—â€ŠithGreenColor) + (Blueâ€Š—â€ŠithBlueColor)

To further enhance this AI project, you can create an app that displays multiple colours on the screen. Once the end-user clicks on a colour, the algorithm will automatically calculate the distance and create a prediction, displaying it on the screen.

Using OpenCV in Python , you can display this text exactly where the user clicked on the screen and draw a rectangle or bounding box around it.

Dataset: Kaggle Color Recognition Dataset

9. Sign Language Recognition App with Python

Sometimes, it can be challenging to communicate with people who have hearing disabilities. Learning sign language can be complicated, and it isn’t a skill most of us have.

In this project, you can build a sign-language recognition app in Python. To do this, you need to take the following steps:

Use the World-Level American Sign Language video dataset that has around 2000 classes of sign languages. You will need to extract frames from the data to train your model.

You can load the Inception 3D model that was previously trained on the ImageNet dataset.

Train a couple of dense layers on top of the I3 model using the frames from the dataset you loaded. You can do this to generate text labels for sign language gesture image frames.

Once you’re done building the model, you can choose to deploy it. Building an application that allows people with a hearing disability to converse with people who don’t know ASL is extremely useful. It serves as a means of communication for two people who wouldn’t have a conversation otherwise.

Dataset: World-Level American Sign Language dataset

10. Detecting Violence in Videos

Videos displaying violence or sensitive content are harmful and can negatively impact a person’s mental health. Videos like this on social media should be marked with a trigger warning or censored for individuals who don’t like to view violent content.

In this project, you can build a deep learning model that detects violence in videos and automatically generates a warning, informing users to watch it at their own risk.

To train this model, you can use datasets that contain both violent and non-violent content (these will be linked below). You can extract image frames from these videos and train a CNN on them. There are various pre-trained models that you can use to accomplish this task, including VGG16, VGG19, and Resnet50.

People have managed to achieve high accuracy scores (over 90%) for this task with the help of transfer learning. Since transfer learning uses models that have already been trained on millions of general images, these models usually perform better than models you train from scratch.

Datasets: Violent Flows Dataset / Hockey Fight Videos Dataset

Access Data Science and Machine Learning Project Code Examples

Here are a few latest interesting AI projects that are worth exploring-

1. Blindness Detection

You will create a machine learning model to support disease diagnosis in this project. You'll use tonnes of images acquired in rural regions to aid in the automatic detection of diabetic retinopathy. The model will help prevent blindness and detect other future diseases, such as glaucoma and macular degeneration. Use two pre-trained models for this project: Resnet101 and Resnet152. Use the APTOS 2019 Blindness Detection dataset to build the image classifier model. The dataset includes the train.csv file, which contains 3,662 retinal images.

2. Real-time Face Mask Detector

We have all been wearing masks for almost two years due to the Covid-19 outbreak. Many public places, including shopping malls, theatres, and restaurants, refuse to admit anyone who isn't wearing a mask.

Face Recognition System

The goal of the research is to build a custom deep learning model to identify whether someone is wearing a mask or not. For creating the face mask detection model, use the face mask dataset available on Github. There are 1,376 photos in this collection, divided into two categories: with masks and without masks. In this project, you'll learn how to use Keras and TensorFlow to train a classifier that can automatically detect whether or not someone is wearing a mask. You can fine-tune the MobileNet V2 architecture using pre-trained ImageNet weights.

3. Self-Driving Car Behavioral Cloning

The project on artificial intelligence aims to train a Deep Network to mimic human steering behavior while driving in a simulator by Udacity, allowing it to move independently. The network uses the frame of the frontal camera (for example, a roof-mounted camera) as input and predicts the steering direction at each instant. For this project, you can gather the data with the Udacity simulator itself or use the "off-the-shelf" training set by Udacity.

By suitably adjusting the ground truth steering angle, use the frames from the side cameras to augment the training set. Add dropout layers after each convolutional layer and each fully-connected layer until the last one to prevent overfitting.

Current AI Projects | Google AI Project Ideas

If you possess the skills and knowledge expertise, you can practice these projects and use them as references to build new projects. Here are a few new and upcoming Google AI projects that you must explore.

1. Hidden Interfaces for Ambient Computing

This is one of the most unique and fascinating Google AI projects . In this project, parallel rendering helps to create ultrabright visuals that can penetrate through bare surfaces. This project aims to use rectilinear graphics on low-cost, mass-produced passive-matrix OLED displays to reveal hidden graphic interfaces.

This project on artificial intelligence involves implanting interface technology beneath different surfaces/materials, and using such technology results in higher brightness levels and low-cost displays from beneath surfaces like wood, textiles, etc. Also, the project explores the benefits of using passive-matrix OLEDs (PMOLEDs), whose basic architecture minimizes cost and complexity.

Source link- Hidden Interfaces for Ambient Computing

2. Improved Detection of Elusive Polyps

This AI project showcases how Google leverages machine learning to assist gastroenterologists (GIs) in the fight against colorectal cancer by enhancing the efficiency of colonoscopies.

Breast Cancer Detection using Logistic Regression

The project aims to create a machine learning model that will aid the GI in detecting polyps in the area under observation, thereby overcoming the problem of incomplete detection. The developed CNN model relies on an architecture that integrates temporal logic with a single frame detector to get more precise outcomes. This project involves working on two different Neural Network architectures- RetinaNet and LSTM-SSD.

Source link- Detection of elusive polyps using a large-scale AI system

3. Document Extraction using FormNet

Complex patterns such as tables, columns, etc., in form documents, limit the efficiency of rigid serialization methods.

This upcoming Google AI project introduces FormNet, a sequence model that focuses on document structure. The model helps minimize the inadequate serialization of form documents. For this project, you will develop a Rich Attention (RichAtt) mechanism that uses a 2D spatial link between word tokens to calculate more accurate attention weights. Then, for each word, create Super-Tokens by using a graph convolutional network (GCN) to embed representations from neighboring tokens. Finally, show that FormNet surpasses conventional approaches while using minimal pre-training data and delivers cutting-edge performance on the CORD, FUNSD, and Payment benchmarks.

Source Link- Document Extraction using FormNet

Python AI Projects for Students

Check out these AI Python projects for students if you're a fresher looking for exciting AI ideas to expand your knowledge and skillset.

1. Building a Telegram Bot

A bot is a computer program that you can program to carry out specific activities. Bots usually imitate or completely replicate human behavior.

Build a Telegram Bot

IOne of the most exciting AI Python projects involves using the Telegram API to build a Telegram bot with Python. You must first obtain a Telegram bot API from the BotFather Telegram account. BotFather is a simple bot that provides a unique API to help build other bots. Once you have the API key to build your telegram bot, the next step is to install the telegram package. Making a “Hello World” program is the simplest method to get your bot up and running. You just need to simply program your chatbot with a command on which your telegram bot will respond with the message “Hello, World”.

2. Keyword Research using Python

Google Trends is a popular keyword research tool that assists researchers, bloggers, digital marketers, etc., in determining how frequently people search for a keyword in the Google search engine during a specific period. Google Trends is useful for keyword research, especially when creating articles covering trending topics.

This project will show you how to use Python to do keyword research to determine the most popular topics and keywords. The first step is to access Google trends using the Google API and the pytrends package in Python. You can quickly install pytrends using the pip command – pip install pytrends . You must first sign in to Google since we use Google Trends to find popular topics. To do so, import the TrendReq method from the pytrends.request method. You can also obtain daily search trends worldwide using the trending searches() method.

3. Fuel Efficiency Prediction

This project on artificial intelligence aims to forecast the outcome of a constant value, such as a price or a probability. Build a model that predicts vehicle fuel efficiency using the Auto MPG dataset, one of the most well-known datasets among machine learning practitioners. You need to give the model descriptions (including the number of cylinders, displacement, horsepower, weight, etc.) of various vehicles from a specific period. Using the Python pandas package, import the data. Make two sets of data: one for training and one for testing. Use the seaborn library's pairplot() method to visualize the data. Build your prediction model using the sequential API with two hidden layers and one output layer that will return a single value.

4. Earthquake Prediction Model

One of the significant unsolved challenges in environmental studies is earthquake prediction. This project will show you how to use Machine Learning and the Python programming language to develop a model for Earthquake Prediction.

Before loading and reading the dataset (use the dataset available on Github), import the essential Python libraries, such as pandas, NumPy, and matplotlib . Explore the key features of earthquake data and design an object for those features, such as date, time, latitude, longitude, depth, and magnitude. Before developing the prediction model, visualize the data on a world map to display a complete overview of where the earthquake frequency will be higher. Split the data into a training set and test set for validation. Lastly, build a neural network to fit the data from the training set.

5. Car Price Prediction

Car price prediction is one of the most basic AI Python projects for final-year students. This project will show you how to use PyTorch to train a model that will help you predict automobile prices using Machine Learning. For this project, use a dataset that includes the costs of different cars and the variable you will predict, i.e., the selling price of the vehicles.

Import all essential libraries, such as pandas, matplotlib, and others, and then load the dataset. To use the data for training, you must transform it from a dataframe to PyTorch Tensors, which require converting them to NumPy arrays. Convert these arrays to PyTorch tensors, then use them to build a variable dataset.

Check out these fascinating artificial intelligence projects with source code available on Github to help you understand AI applications in different fields.

1. Reverse Image Lodging

This project in AI uses an image similarity-based recommendation system to help people choose their favorite Airbnb accommodation. Use the InsideAirbnb dataset from Airbnb, which contains an entire list of apartments in the United States. You can use the urllib2 library to scrape photos and other information from Airbnb apartments in Boston.

Start by gathering basic details about Airbnb apartments from InsideAirbnb (location, descriptions, price range, and homepage URL) and saving it to MongoDB running on an EC2 instance. Save the apartment photos to S3 after scraping them from Airbnb. In the meantime, scrape highlight tags and user reviews and save them for future reference. Extract an HSV-Histogram feature for each image by first decomposing the image into H/S/V channels, then creating histograms for each channel and combining the three histograms. To validate the features in the photos, use Calinski-Harabasz metric score and Cosine distance to calculate the similarities in the images.

Source link- Reverse Image Lodging

2. Pest Prediction and Detection

This artificial intelligence project aims to create an efficient irrigation and pest detection solution that allows you to make well-informed decisions and improve the yield quality. The project's first stage comprises installing any programmable device (such as a solenoid valve) at the beginning of the canal pipe/stream. Each of the smaller devices in the field will have a temperature and humidity sensor, which will supply you with real-time sensor data. Create an MQTT network to link each client to the local server device.

The second stage of this project entails building a CNN model to predict the growth of a specific pest for a particular crop using the real-time temperature and humidity data. Compute a basic estimate of previous years' NDVI values to compare to the farmers' current land vegetation index and display it on your portal.

Source link- Pest Prediction and Detection

3. Plant Disease Classifier

In this AI project, you will create an AI application that can detect plant illnesses by implementing a deep learning model. You will use the Pytorch framework and a Convolutional Neural Network (CNN) architecture to implement the deep learning model.

open source artificial intelligence projects

Image Classifier for Plant Species Identification

Train the image classifier to distinguish the various plant diseases by looking at a picture. You can use this classifier to create a phone app that tells you what kind of disease your camera looks at. This project uses the "Plant Village" dataset that includes 38 plant disease classes and one background class from the open dataset of background images from Standford.

Source Link- Plant Disease Classifier

The artificial intelligence projects ideas described above are by no means an exhaustive list. AI is an incredibly vast field, and with some creativity and technical know-how, you will be able to create some fantastic artificial intelligence software projects to showcase on your portfolio. With the democratisation of AI, it has become increasingly easy for a machine learning engineer to build AI models to solve business problems across diverse business domains. High-level libraries like FastAI and open-source pre-trained models have made AI accessible to everyone. As long as you have an intermediate understanding of machine learning and programming, you can build models to fit various use-cases. Explore solved end-to-end artificial intelligence and machine learning projects to learn AI and start applying your AI skills in practice by working on simple hands-on artificial intelligence projects to take the first step towards pursuing a career in AI.

1. What are some good AI Projects for Beginners?

Object Tracking System - An object detection system can detect multiple objects in a picture. You can utilize Kaggle's Open Images Object Detection dataset for this project. SSD is an open-source object dete
Fake News Detector - The Real and Fake News dataset on Kaggle can be used to create a fake news detector. The classification can be done with the help of a pre-trained machine learning model called BERT, and BERT is a free and open-source Natural Language Processing (NLP) model. For your text categorization job, try loading BERT into Python and add one more output layer.
Animal Species Prediction- Predicting an animal's species is another exciting AI computer vision project. You can use the Animals-10 dataset on Kaggle to perform this multi-class classification task. Use the VGG-16 pre-trained model and the Keras library to import this model into Python.

2. Why AI Projects fail?

Some of the reasons causing the failure of AI projects-

Low data quality- Companies should verify that they have sufficient and relevant data from trusted sources representing their business activities, have correct labels, and are acceptable for the AI tool employed before commencing on an AI project.
Poor team collaboration - Data scientists, data engineers, IT professionals, designers, and line of business workers must work together to create a successful AI project.
Shortage of talent- Companies cannot achieve much with AI unless they have a team with sufficient training and business domain experience.

3. What is the best programming language for artificial intelligence projects?

There is no one "best" programming language for artificial intelligence (AI) projects, as different languages have their own strengths and weaknesses. Popular choices include Python, R, and C++, each with its own set of libraries, frameworks, and tools for building AI applications. The choice of language ultimately depends on the specific project requirements, team expertise, and other factors.

4. What are some challenges that can arise during an artificial intelligence project, and how can they be overcome?

Challenges that can arise during an artificial intelligence project include data quality, lack of domain expertise, algorithm selection, and interpretability. These challenges can be overcome through careful data collection and preprocessing, collaboration with domain experts, experimentation with different algorithms, and developing methods for explaining AI model outputs. Additionally, incorporating ethical considerations throughout the project can help ensure responsible AI development.

Access Solved Big Data and Data Science Projects

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Artificial Intelligence Topics – Comprehensive Guide & Insights

Table of Contents

Do you know we are living in an era where we can literally find AI-enabled devices everywhere? Have you ever encountered a situation where you were apparently involved in a discussion over Smartphones with your friends and the next moment, you get to see a couple of Smartphone ads and recommendations popping on your mobile screen? That’s AI or Artificial Intelligence for you! It is said, by 2030, AI will add 15.7 trillion dollars to the world’s GDP, thus, boosting it by a whopping 14% .

This, pretty much sums up the significance of Artificial Intelligence in today’s world. Now, coming to the primary context of the discussion that is Artificial Intelligence topics for academic writing. It goes without saying.

AI is the future which is already here. No wonder, almost every academic institution is focused on assigning essays and dissertations on artificial intelligence topics.

Easier said than done, students are often found to be perplexed in terms of laying the right focus on AI subject matters and determining the right slant or unique perspectives to approach a topic and developing the same. Now that you too are on the same page, wondering how to come up with an interesting topic on AI essentials and their whereabouts, simply take some time in reading this blog.

Here’s everything you need to know and learn.

How to Develop Artificial Intelligence Topics?

First things first, you should get the hang of all the needful elements to be considered and strategies to be implemented while going about an AI topic and prior to settling for the same. Unless you are aware of what it takes to develop the right AI topic at the end of the day, things will not work out in the way they are supposed to be.

Take a look below and know how to land the perfect paper on Artificial Intelligence by developing the perfect topic down the road.

● Focus on your key inclinations

This is absolutely important. Artificial Intelligence, as a subject matter, is broad and comes up with a lot of critical elements to be understood from time to time. So, it is suggested to focus on your key inclinations or the area of specialization before deciding on a particular topic and ideating the same. Also, it is equally important for you to answer a few crucial questions before selecting the topic and initiating the final draft. Here’s what you should try answering.

What is the topic all about?
Do I have the right knowledge and insights into the particular topic?
What references or point of perspectives should I bring forth?
Is the topic relevant to my area of specialization?
Are there major problems associated with the topic?
If yes, then how do I ideate to resolve the same?

The idea is to come up with well-knit, constructive answers to each of the questions mentioned above and never miss out on picking a topic that will allow you to expand research avenues and bring something interesting to the table.

● Focus on the references to be used

This is yet another crucial point to be noted when it comes to picking and developing an ideal AI topic. It goes without saying, you cannot proceed with an assignment without drawing and citing the right references. Especially when it comes to working on assignments based on AI topics, you should take the aspect of referencing and citation quite seriously. Take note of the following suggestions and know how to go about this chunk of criticality with perfection.

Take a closer look at the topic.
See what aspects or conceptual nitty-gritty the subject is hinting at.
Now, decide on the type of references or information you should essentially come up with.
Make sure the references are rational, relevant and in no way controversial.
Do not come with outdated information as AI, as a subject matter is ever changing and dynamically updated in all shapes and forms.
Make sure the claims you will make or the research findings you will cite are backed by real-time data and the latest industry reports.
Now, take a look back at the key requirements of the assignment and decide on the right referencing format/style to be used across the paper.
These may include APA, MLA, Chicago, Harvard, Vancouver and Oxford referencing styles.

Simply abide by the suggestions as mentioned above and never miss out on starting your next AI assignment help on AI with a bang.

● Know your primary standpoint

For example, if you are supposed to elaborate on the essentials of Machine Learning and Data Science and all you can offer is a mere background history of the subject matter, then all your efforts and ideation will simply go down the drain. You have to decide on a particular standpoint or the primary idea behind drafting the paper. Take note of the following suggestions and know how to add a dash of excellence in this matter.

You should ideally take a stand or decide on a particular perspective to elaborate on, the moment you would read through the topic or the subject matter.
As mentioned earlier, you are supposed to focus on at least a couple of problems associated with the topic.
For example, if the topic is based on data science, you may choose the aspect of “vulnerability of data science and security concerns” as your primary standpoint.
You can now move ahead with a clear elaboration on why do you feel certain things are still vulnerable in the hands of AI and Data Science and what can be done in order to eradicate such odds once and for all.
This way, you will be able to know what you are about to unveil and how.
Also, on the other hand, you target audience will get the hang of what they are about to explore across the assignment.

This, as a result, will make the topic selection more transparent, open-ended and well-communicated.

● Create the perfect outline

This is as important as anything. No assignment is perfect assignment if it lacks the right outline and an organized approach towards preparing the final draft. Simply take note of the following suggestions and know how to come up with a well-knit, structured assignment on AI-based topics.

Frame the perfect introduction based on your standpoints, key perspectives and the problems you would resolve the down the road.
Ideate a three-fold body paragraph and make it a point to include new perspectives and fresh ideas in each of the three paragraphs.
Now, focus on the concluding note by establishing a strong correlation between the thesis statement/introduction and the main body paragraphs.
Also, it is suggested to mention a couple of alternative research avenues and key takeaways for your readers to dig further down the line.

Once you will get the hang of each of the conceptual nitty-gritty of creating the perfect outline, going about the final draft will no more be a major concern. After all, there’s no substitute for a perfectly outline assignment on Artificial Intelligence topics.

● Keep an eye out for the latest AI trends

You cannot add up strong, relevant points if you miss out on acquiring the right knowledge and insights into the latest trends in AI. All you need to do is to keep an eye out for the latest news and reports on AI and its scopes and opportunities. Here are some of the most talked about and interesting facts and trends associated with the essentials of AI.

2023 is expected to see more ethical and explainable AI innovations and models.
This year, we are expected to realm of a more sophisticated cyber securityand AI in voice technology.
The collaboration between AI and human will only get better and hit the top spot by the end of this year.
AI allows marketers to gain on-point knowledge and insights into their customers’ preferences and behavior.

While these are only a few of the most sought-after and notable trends to rule 2023, you can always keep an eye out for more such similar trends and important chunk of information that can be used in your assignment. Also, choosing a topic based on the trending events and phenomenon will make your paper and claims all the more credible.

Now that you are aware of how to choose and develop an AI-based academic topic with precision, let’s move on to the next important segment that is knowing how to land the perfect paper on time.

How to Write a Flawless Assignment on Artificial Intelligence?

Coming to yet another crucial segment of the blog, writing a flawless assignment on Artificial Intelligence requires you to pay heed to a couple of essential suggestions and guidelines. Now that you are stuck in the rut or struggling with a fast-approaching deadline, take a look below and know how to go about a flawless paper on AI topics.

Focus on coming up with factually correct information and always back your claims with the right reference.
See if the primary topic is relevant to your area of study and the fact that it allows you to maintain the right balance between narrow and broad analytical avenues.
Create a separate list of relevant references, interesting data and statistics related to the assigned topic.
Now, your job is to keep referring to the list of references and bring each one of them into play in accordance with the ideas introduced and perspectives suggested.
Take enough time to revise the paper from scratch and make all necessary changes in order to make the paper syntactically and grammatically accurate.
Also, it is equally important for you to cite good examples in order to make the paper more readable and engaging in all aspects.
Come up with short, relevant and engaging paragraphs instead of unnecessarily lengthy and overly convoluted chunks.

The next time you would attempt assignment topics on Artificial Intelligence, pay heed to each of the suggestions mentioned above and never look back.

50+ Engaging Artificial Intelligence Topics for your Next Assignment

All said and done, it’s time for some quick brainstorming across some of the most sought-after Artificial Intelligence topics.

You may refer to this list on the go for engaging ideas and slants for AI-based academic papers.

Here you go!

Analyze and elaborate on the role of AI in behavior change and human learning
Elaborate on the role and significance of AI in hotel management
The role of AI and Data Science in building a more sustainable future for us
The role of Artificial Intelligence in Finance management
The role and significance of AI in food, agriculture and farming?
Is there a need for AI to develop further across the agricultural realm?
Elaborate on the five big ideas on AI and Data Science
What are the four basic concepts of Artificial Intelligence?
Can AI enhance human emotions? Elaborate on this topic with examples
Explain the key technologies backing AI and its essentials
Elaborate on the six main principles of AI along with examples
What are the 7Cs of Artificial Intelligence? Explain and elaborate on their applications, with examples
Is AI really the future or is it pushing us away from human emotions?
How AI will facelift the domain of education and learning in the years to come?
Are there potential cons associated with the concept of Artificial Intelligence?
How to overcome the odds and obstacles that may come in between AI and its road to creating a sustainable environment?
Elaborate on the nine types of Artificial Intelligence and elaborate on the application of each one of them
Explain the key significance on Big Data and AI in High Energy Physics
Elaborate on the correlation between Artificial Intelligence and Machine Learning
The future of Robotics in the hands of Artificial Intelligence – Explain with examples
Do we need more innovations and resources in order to give AI the right shape?
What is a limited memory AI? Explain with examples
What is self-aware AI? Explain with examples
What is Artificial Super Intelligence? Explain with examples
What are the positive impacts of Artificial Intelligence on our society?
What are the negative impacts of AI on our society?
Is Alexa an AI? Are there further scopes to develop Alexa?
Can artificial intelligence boost efficiency at workplace?
Can artificial intelligence boost efficiency across sports and recreation?
Can AI really replace jobs and humans?
Elaborate on the basic concept and application of AI in today’s world.
What is Theory of Mind AI? Explain with examples
Elaborate on the life and achievements of the father of AI, John McCarthy.
Elaborate on the 4 major categories of AI – Explain with examples

While these are only some of the most talked-about topics in AI, you can always keep an eye out for other related subject matters such as Big Data, Encrypted Data, Chatbots and more.

Most Frequently Asked Questions By Students

1. what are some artificial intelligence short topics.

Here are some short AI topics for your reference.

Data Science and Machine Learning
AI and Technology
AI and Education
Big Data and AI
Reinforcement Learning

2. How to Write Artificial Intelligence Topics?

Here’s how to write AI topics.

Focus on the topic or the main context of the discussion
Figure out the primary problems associated with the same
Keep an eye out for the latest references and trending affairs
Come up with a unique slant to initiate the final draft

3. Why artificial intelligence is an interesting topic?

Frankly speaking, AI is the talk of the town, the need of the hour. In addition, the subject matter has a lot of interesting dimensions and aspects to offer. These include Robotics , Big Data, Gamification, Data Science and the likes. Each of these key factors makes AI an interesting topic for every academic assignment.

4. What are the Advanced Artificial Intelligence Project Ideas?

Here are some interesting project ideas on advanced artificial intelligence.

Fuzzy Systems
AI in High Energy Physics
Sophia – Hanson Robotics
Representation and Reasoning in AI
Perception in AI

5. Where I can find best Artificial Intelligence Topics?

You can find the artificial intelligence topics all over the internet but the best ones are available at MyAssignmenthelp.com.

6. What are some simple Artificial Intelligence Topics?

Here are some simple Artificial Intelligence topics.

AI and Machine Learning
Social media and the role of AI
Virtual agents and biometrics
Robotic process automation
Peer – to- Peer network

7. What are some Difficult Artificial Intelligence Topics?

Here are some difficult artificial intelligence topics.

Speech recognition
Nature language generation
AI in sports and politics
Deep learning platforms
Bid Data and Hadoop

8. Who Provides me best Artificial Intelligence Topics for my college Assignment?

Our in-house experts are right here, available round the clock to provide you with the best AI topics for your college assignment.

9. What are some trending topics of Artificial Intelligence?

Here are some trending Artificial Intelligence topics.

AI apps and creativity
Ethics and regulation in AI
Digital twinning
AI for personalization
Low code, no-code AI

10. Which is the best website providing me Artificial Intelligence Topics?

Undeniably, MyAssignmenthelp.com is the best website to provide you with Artificial Intelligence topics.

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7 Steps to Learn AI From Scratch in 2024: Best free Resources

Artificial Intelligence (AI) is revolutionizing every industry, from healthcare to finance to entertainment. As we move into 2024, there has never been a better time to dive into AI and equip yourself with the skills to be part of this exciting field. This article covers a step-by-step guide to help you learn AI from scratch, using the best free resources available.

Steps to Learn AI From Scratch in 2024

What is artificial intelligence (ai).

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. These intelligent systems can perform tasks such as recognizing speech, making decisions, and translating languages. AI can be categorized into two types:

Narrow AI (Weak AI) : AI systems designed and trained for a specific task, such as voice assistants (Siri, Alexa) or recommendation systems (Netflix, Amazon).
General AI (Strong AI) : Hypothetical AI systems that possess the ability to perform any intellectual task that a human can do, with the same level of competence. This type of AI remains theoretical and is a subject of ongoing research.

Learning AI is now positions you at the forefront of technological advancements, providing skills that are high demand across multiple industries, ensuring job security and career growth. So below mentioned are the steps that you can follow to Learn AI From Scratch in 2024.

Step 1: Understand the Basics of AI

Before diving into the technical aspects, it’s crucial to grasp what AI is and its potential applications. Start with these resources:

Recommended Free Resources:

Coursera: AI For Everyone by Andrew Ng : This is a non-technical course that provides a broad overview of AI, its implications, and applications.
Elements of AI : A series of free online courses created by the University of Helsinki to demystify AI.

Recommended Books:

“Artificial Intelligence: A Guide for Thinking Humans” by Melanie Mitchell : This book offers a comprehensive and accessible introduction to AI, covering its history, concepts, and implications.
“Life 3.0: Being Human in the Age of Artificial Intelligence” by Max Tegmark : Explores the future of AI and its impact on society, providing a thought-provoking read for beginners.

Machine Learning Basic to Advanced – Self Paced

Step 2: Learn Python Programming

Python is the most popular language for AI development due to its simplicity and extensive libraries. Begin with the basics and gradually move to more complex topics.

Codecademy: Learn Python 3 : An interactive course perfect for beginners.
Google’s Python Class : Provides written materials, lecture videos, and lots of code exercises to practice Python coding.
“Automate the Boring Stuff with Python” by Al Sweigart : This book is great for beginners and covers practical programming skills with Python.
“Python Crash Course” by Eric Matthes : A hands-on, project-based introduction to Python for beginners.

Best Way To Start Learning Python – A Complete Roadmap

Step 3: Dive into Mathematics for AI

A solid understanding of mathematics is crucial for AI. Focus on linear algebra, calculus, probability, and statistics.

Khan Academy : Offers comprehensive courses in linear algebra, calculus, and statistics.
3Blue1Brown (YouTube Channel) : Provides visually intuitive explanations of complex math concepts.
“Mathematics for Machine Learning” by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong : This book covers essential mathematical concepts needed for machine learning.
“The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman : A classic book on statistical learning theory and methods.

Step 4: Get Acquainted with Machine Learning

Machine Learning (ML) is a subset of AI that focuses on building systems that learn from data. Start with the basics and gradually move to more advanced topics.

Coursera: Machine Learning by Andrew Ng : This is a classic and highly recommended course that covers all fundamental aspects of ML.
Google AI’s Machine Learning Crash Course : A practical introduction to ML with exercises and interactive visualizations.
“Pattern Recognition and Machine Learning” by Christopher M. Bishop : An excellent textbook for understanding the theoretical aspects of ML.
“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron : A practical guide with examples and code snippets to help you implement ML algorithms.

100 Days of Machine Learning – A Complete Guide For Beginners

Step 5: Explore Deep Learning

Deep Learning is a subset of ML that uses neural networks with many layers. It is the driving force behind most of the recent advances in AI.

Deep Learning Specialization by Andrew Ng on Coursera : Although it has a paid certification, the course materials are accessible for free.
Fast.ai : Offers practical deep learning courses with a top-down approach, emphasizing coding first.
“Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville : Known as the “Bible” of deep learning, this book covers deep learning theory and practice in great detail.
“Deep Learning for Coders with Fastai and PyTorch” by Jeremy Howard and Sylvain Gugger : A hands-on guide to deep learning with practical applications using the Fastai library and PyTorch.

Complete Deep Learning Tutorial

Step 6: Practice with Projects

Applying what you’ve learned through hands-on projects is crucial. Start with small projects and gradually tackle more complex problems.

Kaggle : A platform with datasets and competitions to practice and hone your AI skills.
Google Colab : Provides a free environment to write and execute Python code in the cloud, making it easy to collaborate and share your work.
“Data Science Projects with Python” by Stephen Klosterman : This book offers practical project-based learning to apply your data science and machine learning skills.
“Python Data Science Handbook” by Jake VanderPlas : Comprehensive guide on using Python for data science, including many practical examples and projects.

Step 7: Stay Updated and Join the Community

AI is a rapidly evolving field. Staying updated with the latest developments and being part of the AI community can provide invaluable support and opportunities.

ArXiv.org : A repository of research papers where you can read about the latest advancements in AI.
Reddit (r/MachineLearning) : A community where you can discuss topics, ask questions, and share knowledge with other AI enthusiasts.
Towards Data Science (Medium) : Offers articles, tutorials, and resources written by AI practitioners.
“Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig : A comprehensive textbook covering a wide range of AI topics.
“Deep Learning with Python” by François Chollet : Written by the creator of Keras, this book provides a practical introduction to deep learning with Python.

7 Steps to Learn AI From Scratch in 2024 – FAQ’s

Why should you learn artificial intelligence right now.

Learning AI now positions you at the forefront of technological advancements, providing skills that are in high demand across multiple industries, ensuring job security and career growth.

Is AI a High-Paying Job?

Yes, AI-related roles are among the highest-paying jobs in the tech industry due to the specialized skills required and the increasing demand for AI professionals.

Should I Learn AI in 2024?

Yes, learning AI in 2024 is highly beneficial due to the growing demand for AI expertise, the expanding applications of AI in various industries, and the potential for high-paying and fulfilling career opportunities.

How to Learn AI Step by Step?

Begin with foundational knowledge of AI, learn Python programming, study essential math, dive into machine learning concepts, progress to deep learning, and apply your skills through hands-on projects and real-world applications.

How Long Does it Take to Learn AI?

The time it takes to learn AI can vary, but with consistent effort, you can gain a solid understanding of AI fundamentals and start building projects within 6 months to a year.

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Artificial Intelligence

Artificial Intelligence Assignment Topics

Table of Contents

Introduction to Artificial Intelligence

Artificial Intelligence (AI) is a field of computer science that focuses on enabling machines to be able to act in ways that might otherwise require intelligence and human-level cognition. It can be either rule-based, or deep-learning based AI depending on the context. Rule-based AI refers to systems designed by humans, where specific rules are programmed into the system for it to facilitate decision making processes within a set domain. Deep Learning works differently from rule based AI – instead of algorithms written by coders being fed into the system, data inputs and model outputs allow the program to learn proper output behaviors autonomously. These forms of artificial intelligence challenge traditional boundaries between information processing devices such as computers and living organisms (plants, animals and humans), blurring different computational models like Machine Learning, reinforcement learning etc with key aspects of organic brains; thereby opening up new possibilities for extended cognitive abilities like pattern recognition, natural language generation / understanding etc.,

History of Artificial Intelligence

The history of Artificial Intelligence (AI) is a fascinating and ever-evolving tale. While its modern incarnation began with the development of the first programmable digital computers in the 1940s, AI has been around long before then. For example, early versions of mechanical calculators that could solve mathematical equations based on instructions from an operator date back to 17th century mathematician Blaise Pascal. In 1950, following years of philosophical debate about machines being capable of abstract thought, British mathematician Alan Turing proposed a test for determining whether or not a machine was intelligent: if it could think like a human being. From here, AI rapidly developed into something more advanced than anyone had imagined possible at the time; moving from simple mathematics analysis to data storage, logic problem-solving strategies and natural language processing capabilities over time – all requiring extensive research and innovations in technology along the way. Today’s applications range from sophisticated medical diagnostics and gaming technologies to autonomous vehicle systems widely used today as well as self-learning robots performing many tasks better faster than their human counterparts can do currently. AI continues to develop at a rapid pace – providing opportunities for technological advancements we have yet to even imagine!

Types of Artificial Intelligence

Artificial Intelligence (AI) is an area of computer science that focuses on creating intelligent machines that can think and act like humans. The development of AI technology has the potential to revolutionize the way people work and interact with computers. There are several different types of Artificial Intelligence, including rule-based expert systems, neural networks, fuzzy logic systems, evolutionary computing methods and natural language processing (NLP). Rule-based expert systems make decisions based on pre-defined rules. Neural networks are highly complex artificial networks created by connecting computational elements in layers similar to neurons in the human brain; they allow a machine to learn from data or experience using statistical probabilities. Fuzzy logic systems use non-binary coding to account for uncertainty when making decisions, while evolutionary computing methods adapt existing solutions over time through learning cycles designed by introducing mutation factors into its program code. Natural language processing allows machines to interpret human languages such as English so people may communicate more effectively with their electronic devices.

Understanding Machine Learning

Machine learning is an especially important branch of artificial intelligence (AI) that enables computers to learn from data and improve their performance over time. It can often be used for tasks such as image recognition, natural language recycling, sentiment analysis and more. Furthermore, it also allows applications to take reasonable actions based on real-world situations, despite being unable to explicitly predict a certain outcome. To better understand machine learning principles one must understand concepts such as supervised vs unsupervised learning, neural networks and deep learning etc., Through this understanding AI experts are able to build reliable models capable of comprehending the interactions between big datasets with concise yet efficient algorithms. Knowing which algorithm best suits any particular problem or task is key towards successful outcomes in using this technology.

The Benefits of Artificial Intelligence

Artificial Intelligence (AI) has become increasingly prevalent in our daily lives. From voice assistants to self-driving cars, AI is being used to automate various tasks. The use of AI has numerous benefits that can help improve people’s lives and make businesses more efficient. For one thing, AI systems are better than humans at identifying patterns, so it is useful for analysing large datasets quickly and accurately. This makes companies run more efficiently as AI systems have the potential to detect problems earlier and track operations with greater accuracy than traditional methods would allow. Moreover, deep learning algorithms give machines the ability to learn from their mistakes; this means programs get smarter over time rather than having a static set of rules like traditional approaches do. Additionally, Artificial Intelligence also reduces human bias by eliminating emotion from decisions; as such it can be used for automated decision-making which leads to more consistent outcomes compared with manual decision-making tools or processes that rely on individual judgement. Lastly, Automation powered by AI allows us do things faster and simpler; by automating mundane but necessary tasks businesses save money while freeing up workers’ time for higher value work such as problem solving or strategic planning activities that still require human involvement and creativity that technology cannot provide yet

Common Artificial Intelligence Assignment Topics

Artificial intelligence (AI) is a cutting-edge field of study with many potential applications and implications. As AI technology advances, so too do the educational resources designed to instruct students in the fundamentals of computing, data analysis, robotics, neural networks, machine learning and more related topics. This can be seen reflected in the variety of AI assignments being given at all educational levels – primary school through college. Common AI assignment topics includes creating algorithms to support self-driving cars; developing a computer program that can monitor betting fraud based on predictive analytics; designing Artificial Neural Networks (ANNs) for text classification problems; using fuzzy logic systems to solve problem scenarios; and evaluating instances where human decision making differs from automated decisions made by machines.

Step-by-step Process for Writing an Assignment on AI

Writing an assignment on AI requires careful planning and familiarity with both the topic and various resources. To ensure a successful outcome, follow these steps:

1. Define your research question or area of focus – Before you start writing, you need to clearly define the scope of your research in order to create a workable plan for tackling your assignment task. 2. Research thoroughly – Conduct comprehensive research about AI technologies; explore relevant topics such as machine learning or neural networks; analyze existing data sets related to AI applications; understand terminology related to this field; read books written by experts in the field; and watch videos from key influencers talking about AI advancements. 3. Outline the structure of your essay – Determine what specific elements are required for completing the project successfully (eg., introduction, body paragraphs etc.). Create an outline that will allow you to stay organized while developing each section independently but also layout potential transitions between them that can weave items together into one cohesive whole.. 4. Write drafts – Now it’s time to start writing! Use sources gathered during previous step and be aware of deadlines imposed by professor (if any). Revise multiple drafts keeping in mind desired length and scope allocated for given piece so adjustments can be made when necessary prior to submitting finished product.

5. Edit & Review – Once complete write-up is drafted, edit content carefully ensuring correct spelling grammar usage plus correct referencing style is applied throughout all text fields according at instructions provided with assigned paper.; Also review ideas discussed involving general logic their relevance within context which means going back through different sections checking progressiveness throughout composition before making final submission for grade decisioning process .

Challenges and Drawbacks of AI

AI is changing the way we conduct business, from automating mundane tasks to helping us make decisions faster. However, there are several challenges and drawbacks of AI that must be recognized. Firstly, AI requires vast amounts of data for an accurate solution. Too little or incorrect data could lead to inaccurate results or models that have unexpected behavior which could create potentially hazardous outcomes in critical operations. It can also take a substantial amount of time and resources to develop an AI model especially when more complex algorithms are employed by larger organizations with large datasets. Additionally, building these models require specialized personnel who not only understand the mathematics but also possess a deep understanding of the application domain they build it in since parameters need tuning accordingto specific requirements such as accuracy and speed metrics within each domain. Another aspect stemming from its complexity is Openness because even after deployment it may remain difficult to achieve complete transparency on how exactly a particular decision was made due to hidden layers within certain neural network architectures as well as complex non-linear relationships between multiple input features in many supervised learning problems. Lastly, despite training sophisticated deep neural networks on labeled dataset composedof millions or sometimes billions of examples some individual patterns maybe overlooked leading torandom errors that maycreate serious consequences at times like in medical diagnosis applications where small mistakes can put people’s lives at riskor banking fraud detection applications whereeven a tiny discrepancyin determining true positivesmay result ingreat financial losses topotentially hundredsor thousands customers all at once if false alarms triggered too early .So dealing with such tradeoffs demands careful consideration and strategizing before deploying anymachine learning modelin production environments requiring special focus on issues relatedto trust governance as muchas accuracy improvements .

AI’s Impact on the Future

Exploring the impact of artificial intelligence (AI) on the future can expose some exciting possibilities. Driven by advances in technology, AI has become capable of learning at a rapid pace and thoroughly analyzing data from large sources. As these capabilities continue to evolve, industries around the world are quickly taking advantage of what AI technologies have to offer. The ability for an AI-driven solution to automate processes, generate insights about consumer behaviour and empower users with access to otherwise unavailable datasets means that this technology stands poised to revolutionise countless operations in the coming years.

Furthermore, those working in close contact with AI technology may find themselves faced with new opportunities for growth as their machines will be able to help them accomplish far more than was ever possible before its introduction. It is believed that many jobs that were previously carried out manually will end up being gradually replaced by efficient robots; although there might initially be some job losses due to automation, eventually it should lead towards increased productivity while introducing newer employment models such as creating more IT oriented roles or managing AIs within different sectors including healthcare and finance etc..

Finally, outside of business settings various aspects of everyday life could start benefiting from using AI powered devices marked by better delivery services through autonomous cars/trucks or greater availability of goods enabled through machine driven stocking systems among other advancements., All in all it’s both exciting and scary when pondering over the implications involved stemming from advancements seen within Artificial Intelligence today!

FAQs on AI Assignments

The FAQs on AI Assignments is a useful resource for students to get clarification on the topics related to artificial intelligence assignments. It addresses common questions concerning types of AI tasks, tools used in these tasks, resources necessary for completion and other important issues. For instance, it can provide information on how complex Artificial Intelligence assignment concepts should be broken down into smaller pieces in order to make them easier to understand. Additionally, the FAQs covers viewing tutorials demonstrating basic techniques needed to complete an assignment and overview of existing Artificial Intelligent applications relevant to a specific problem statement or project. Furthermore, it provides advice regarding formatting considerations and general tips necessary when writing up reports containing AI-related results as part of an assessment submission.

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Integrating AI into assignments

Main navigation.

Here we offer strategies and perspectives on integrating AI tools into assignments and activities used to assess student learning.

Creating your course policy on AI

An effective syllabus works to motivate learning, define goals, explain course structure, and provide support to students as they learn.
Be clearly stated and specific
Clarify the context or conditions of allowable AI use
Explain processes and consequences for non-compliance
Have a thoughtful pedagogic rationale in support of student learning
Connect to support resources
Show support for student well-being

Outcomes for this module

In this module, we will analyze activities and assignments used for assessing learning, provide student-centered perspectives, and offer strategies for developing assessment activities and assignments that integrate student use of generative AI chatbots.

After completing this module, you should be able to:

Describe why your assessment activities are meaningful to learners.
Identify and clarify the learning objectives of your assessment activities.
Identify relevant strategies that can be applied to assessment activities in your course.
Empathize with student perspectives on using AI in course assessment activities.

Warm-up with a metacognitive exercise

As you begin to explore, think about what you already know and the opinions you may already hold about the educational aspects of AI chatbots. This metacognitive exercise can help you identify what you want to explore and what you already understand. Making connections to what you already know can deepen your learning and support your engagement with these modules.

Begin with the prompt, “Describe an assignment or assessment activity that integrated technology in a way that was effective and engaging for your learning,” and respond to the poll below.

Unpacking your assessment activities and assignments

When designing or adapting an activity or assignment used to assess learning, whether you integrate AI or not, we encourage you to consider two questions: why is this meaningful, and what are students supposed to learn from it?

Define why it is meaningful

Students can learn better when they are motivated and can make meaningful connections to coursework (Headden & McKay, 2015). We might assume that students’ motivations focus on their grades, but that assumption does not provide the full picture, and when applied in isolation it is not likely to sustain deep learning. Articulating what makes an activity meaningful, motivational, and memorable for students can help you create an engaging activity or assignment that enhances student learning and motivation.

Concerning AI chatbots, perhaps the activity or assignment addresses AI in ways that prepare students for future careers, enhance their social connections, or touch upon broader issues they care about. We encourage you to talk with your students about what they find meaningful to inform the design of your activities and assignments. What leads them to want to engage?

Also, reflect on why the assignment is meaningful to you. Is it simply convenient to implement (and standard in your experience as a student and teacher) or does it connect to something deeper in your pedagogy? Perhaps the assignment reinforces the norms and values that you share with other professionals in your discipline, allows you to connect with students in more meaningful ways, builds foundational skills for other parts of the curricula, or explores emergent opportunities and challenges with AI for your field.

Define what students are intended to learn

Next, identify and clarify the underlying learning objectives of the assignment or activity. The objective should describe the observable skills or behaviors students will have learned to perform after completing the activity. Clearly articulated learning objectives can help you develop activities that support learning and assessments that accurately measure student learning.

When thinking about AI chatbots and how they impact writing, you might ask yourself, “What are the underlying learning objectives being addressed through writing?” Instructors may assign writing tasks to assess how students engage with content. In the past, teachers could assume with good reason that a student producing coherent writing must have engaged with the content to generate writing that makes sense. However, we might also question this assumption about the automatic connection between coherent writing and deep engagement. The advent of generative AI has certainly exacerbated this.

Do you ask your students to write to demonstrate and reinforce content knowledge? Do they write to analyze and critique a position? Do they write to formulate arguments and cite evidence? Do they write as a form of creative expression? When you think about the available options, you can likely develop many ways for students to learn and demonstrate these skills with or without writing. Ultimately, honing in on the underlying learning objectives can help you integrate generative AI tools into an assignment.

Students can benefit from understanding how AI works and the educational opportunities and challenges that it presents. Consider offering the content in the modules in this guide to your students as supplemental reading or as part of a class activity.

Strategies for implementing AI into activities and assignments

As you think through how you might address or integrate AI tools in an assessment activity or assignment, we encourage you to consider a range of possibilities related to the specific aims of your course and the needs of your students. Here we offer a variety of pedagogical strategies for you to consider. We present these strategies in the context of students using AI chatbots, but they also apply to contexts without AI. Remember why your assignment is meaningful in relation to your learning objectives to help you select appropriate strategies.

Leverage multiple modalities

Consider ways to diversify when and where you assess student learning and the formats students use to express what they’ve learned.

Use more in-class assignments

Strategies like the flipped classroom model assign lecture content as homework and use the in-class time for learning activities (Lage et al., 2000). You can use this in-class time to integrate more low-stakes assessment activities during which you can better guide students toward using AI in ways that support learning.

Multiple modes of expression

Students may differ in how they can best articulate what they know. Using multiple modalities of expression, such as having students complete assignments that require speaking or graphic representations instead of only written text, stands out as an established strategy within the Universal Design for Learning framework that could apply here. While chatbots primarily generate written text, other AI tools can generate music, graphics, and video. You can thus create assessment activities that integrate multiple modalities at once.

For example, if you are assessing students’ understanding of cultural exchange in the ancient world, students might create a mind map or timeline to visually represent important trends, events, or concepts covered in the assigned readings. AI might then be used to generate images of artifacts, portraits, or cityscapes based on historical descriptions.

Make grading practices clear

Consider ways to clarify for students how they are being graded and what is expected of them.

Require robust citation

Have students learn about and adopt more robust citation practices, especially if they use AI tools for writing. You might begin with conversations about what plagiarism entails and why ethics matter in higher education and your discipline. Then connect students to resources on citation and documentation .

If you and your students decide to use AI tools, you can find style guidelines about citing AI-generated text for APA style and MLA style . These guidelines advise writers to cite the AI tool whenever they paraphrase, quote, or incorporate AI-generated content, acknowledge how they used the tool (for brainstorming, editing, and so on), and vet secondary sources generated by AI. For example, students could include citations for AI in the Works Cited section of their work and also include a statement describing why and how they used AI chatbots.

Establish and communicate clear assessment criteria

Try to bring assessment activities, learning objectives, and evaluation criteria into alignment. For example, if your objectives and assessments center around students proposing a solution to an open-ended problem, then the evaluation criteria might touch upon the feasibility, impact, or comprehensiveness of the proposed solutions. The criteria can vary a lot depending on your content and course, but your students benefit when you communicate these criteria and the purpose and reasoning behind them (Allen & Tanner, 2006).

For example, when integrating AI chatbots into a writing task for students, you might put more weight on the quality of their ideas and the validity of cited sources and less weight on structure, grammar, and word choice. You might then create a rubric that you discuss with students in advance so they have a clear understanding of what will guide you in assessing their work.

Assess learning throughout the course

Consider ways to assess student learning throughout your course as opposed to assessing mostly at the end of the course.

Emphasize the process

You may be able to more effectively assess student learning during the different stages of the process as opposed to assessing learning based on their finished work (Xu, Shen, Islam, et al., 2023). Whether or not students use AI tools, they can benefit from segmenting a large project into smaller components with multiple opportunities for feedback and revision. Also, consider how you might adjust grading criteria or grade weights to put more emphasis on the process.

For some steps in the thinking process, such as brainstorming ideas, formulating a position, and outlining a solution, allowing students to use AI tools might benefit their process. For example, you might have students begin with low-stakes free-writing, such as brainstorming, then use AI chatbots to explore possible areas for further investigation based on the ideas students generate through their exploratory writing. Students might then critique and revise the AI-generated ideas into an outline.

Leverage formative feedback

Teachers provide formative feedback to students throughout the learning process to stimulate growth and improvement. Formative feedback can help students identify misunderstandings, reinforce desirable practices, and sustain motivation (Wylie et al., 2012). You and the teaching team might provide feedback directly to students or you might facilitate students giving feedback to each other. You might then assess how students follow up on feedback they receive.

You can use AI tools to inform your feedback to students or generate feedback directly for students. AI tools could provide instant, individualized feedback efficiently and frequently, supplementing the feedback provided by your teaching team. For example, you might share your existing assignment, rubric, and sample feedback with the chatbot and give it instructions on when and how to give feedback. Importantly, you should review feedback generated by chatbots for accuracy and relevance. Refine and save the prompts that work best. You might later share the prompts you’ve developed with students so they may use them to generate feedback themselves.

Make assignments more meaningful

Consider how you might make your assignments more relatable and meaningful to your students.

Personalize assessments

When done thoughtfully, connecting assessments to the personal experiences, identities, and concerns of students and their communities can help to motivate and deepen learning (France, 2022). You might also connect assignments to contexts specific to Stanford, your course, or your specific group of students.

With AI, you or your students might generate practice questions on topics that came up during a specific class discussion or generate analogies for complex concepts based on their interests and backgrounds. You might ground an assessment activity in local contexts, such as having your engineering students propose a plan to improve Lake Lagunita.

Use real-world assessment tasks

Assignments that leverage real-world problems, stakeholders, and communities that students are likely to engage with in their work lives can be motivational and valid ways of evaluating a student’s skills and knowledge (Sambell et al., 2019).

For example, students might work with real (or AI-simulated) business or community partners to develop a prototype product or policy brief. Students might have more time to work with those stakeholders and refine their proposal concepts if they can use AI tools to assist with time-consuming tasks, such as summarizing interview transcripts, writing a project pitch statement, or generating concept images.

AI itself might provide a relevant topic of study for your course. For example, you might examine AI as part of a discussion in a course about copyright and intellectual property law. Or you might analyze AI companies such OpenAI or Anthropic as case studies in a business course.

Assess more advanced learning

Consider ways you might assess more advanced or wider-ranging learning goals and objectives.

Emphasize metacognitive reflection

Metacognitive reflection activities, where students think about what and how they learn, can help students improve their learning (Velzen, 2017). You might use polls, discussion activities, or short writing exercises through which students identify what they already know about the topic, what they learned, what questions remain, and what learning strategies they might use for studying.

AI chatbots can help guide the reflection process like this reflection tool being developed by Leticia Britos Cavagnaro at Stanford d.school . Or perhaps students complete some activities with AI, then reflect on how it benefits or hinders their learning, and what strategies they might use to best leverage AI for learning.

Prioritize higher-order thinking

While students should develop mastery over foundational skills such as understanding concepts, identifying key characteristics, and recalling important information, practicing higher-order thinking skills, such as solving complex problems, creating original works, or planning a project, can deepen learning. For example, you might frame student essays as a defense of their views rather than a simple presentation of content knowledge. You might adjust assessment criteria to prioritize creativity or applying skills to new contexts.

Prioritizing higher-order thinking can encourage students to use AI tools to go beyond simply generating answers to engaging deeply with AI chatbots to generate sophisticated responses. Students could conduct preliminary research to find reliable sources that verify or refute the claims made by the AI chatbots. AI chatbots might then generate feedback, provide prompts for further reflection, or simulate new contexts.

Putting it all together

Here we offer a practical example: first, a typical assignment as usually designed, and then how you could enhance the assignment with some strategies that integrate AI chatbots.

When thinking about your course, start with small changes to one assignment and steadily expand upon them. Try to use AI chatbots for your other work tasks to build your fluency. Talk with students and colleagues about how the changes to your course work out concerning student engagement and learning. When integrating AI into an existing assignment, begin with an assignment that already has clearly defined learning objectives and rationale. Begin by using AI or other technology to supplement existing parts of the process of completing the assignment.

More examples of AI assignments

AI Pedagogy Project from metaLAB (at) Harvard
Exploring AI Pedagogy from the MLA-CCCC Joint Task Force on Writing and AI
TextGenEd: Continuing Experiments, January 2024 Collection from WAC Clearinghouse

Example of an assignment without AI

Currently, your students in an epidemiology course write essays summarizing the key concepts of an academic article about the socio-determinants of diabetes . This assessment activity has meaning because it focuses on a foundational concept students need to understand for later public health and epidemiology courses. The learning objective asks students to describe why socio-economic status is a strong predictor for certain diseases. Students write a five-page essay about a disease that can be predicted by socio-economic status including at least three additional citations. Students complete the essay, which counts for 30% of the final grade, before the final exam.

An example of an assignment that integrates AI

Using some of the strategies in the above sections, you might redesign this assignment to integrate the use of AI chatbots. Keep in mind that you would likely make small changes to a major assignment over multiple quarters. Consider some of the ideas below.

A meaningful assignment

The redesigned assessment activity carries more meaning to students because they might have personal experience of some communities adversely affected by these kinds of diseases, and public health issues like this intersect with other social injustices that students have expressed concern about.

Learning objectives

The objectives of the assessment activity include that students will be able to:

Describe how this disease affects particular communities or demographics
Explain the difference between correlation and causality regarding socioeconomic status and the disease
Propose a public health intervention that could help to address this issue

Assignment elements with AI

Students generate explanations of medical terminology in the selected articles to aid with reading comprehension. They generate several analogies for the core concept that apply to their own life experiences and communities. Students share these analogies in a Canvas forum graded for participation. Instructors provide general feedback in class.

Informed by the article, students then prompt a chatbot with biographical stories for two fictional characters from communities they care about incorporating differing socio-economic factors. Then they guide the chatbot in generating a dialogue or short story that illustrates how the two characters could have different health outcomes that might correlate with their socio-economic status. Students might use AI image generators for illustrations to accompany their stories. Students submit the work via Canvas for evaluation; the teacher shares exemplars in class.

Using an AI chatbot prompt provided by the instructor, students explore possible ideas for public health interventions. The provided prompt instructs the chatbot only to help students develop their ideas rather than suggesting solutions to them. With the aid of the chatbot, the students develop a public health intervention proposal.

Assignment elements without AI

Students discuss the differences between correlation and causation, critically analyze the generated characters and stories, and address any biases and stereotypes that surfaced during the activity. You facilitate the discussion with prompts and guidelines you developed with the aid of AI chatbots. Students write an in-class metacognitive reflection that you provide feedback on and grade for completion.

Students draw posters that summarize their proposed intervention. They critique and defend their proposals in a classroom poster session. Students complete a peer evaluation form for classmates. You evaluate the posters and their defenses with a grading rubric that you developed with the aid of an AI chatbot.

Students write an in-class reflection on their projects summarizing what they have learned over the length of the project, how the activities aided their learning, and so on. This is submitted to Canvas for grading and evaluation.

Student-centered perspective on using AI for learning

When thinking about integrating generative AI into a course assignment for students, we should consider some underlying attitudes that we, the authors, hold as educators, informed by our understanding of educational research on how people learn best. They also align with our values of inclusion, compassion, and student-centered teaching. When thinking through ways to integrate AI into a student assignment, keep the following perspectives in mind.

AI is new to students too

Like many of us, students likely have a wide range of responses to AI. Students may feel excited about how AI can enhance their learning and look for opportunities to engage with it in their classes. They may have questions about course policies related to AI use, concerns about how AI impacts their discipline or career goals, and so on. You can play a valuable role in modeling thoughtful use of AI tools and helping students navigate the complex landscape of AI.

Work with students, not against them

You and your students can work together to navigate these opportunities and challenges. Solicit their perspectives and thoughts about AI. Empower students to have agency over their learning and to think about AI and other technologies they use. Teaching and learning are interconnected and work best in partnership. Approach changes to your teaching and course to empower all students as literate, responsible, independent, and thoughtful technology users.

Look at AI and students in a positive light

Education as a discipline has repeatedly integrated new technologies that may have seemed disruptive at first. Educators and students typically grapple with new technology as they determine how to best leverage its advantages and mitigate its disadvantages. We encourage you to maintain a positive view of student intentions and the potential of AI tools to enhance learning. As we collectively discover and develop effective practices, we encourage you to maintain a positive and hopeful outlook. We should try to avoid assuming that most students would use generative AI in dishonest ways or as a shortcut to doing course assignments just because some students might behave this way.

Assess and reinforce your learning

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Course and Assignment (Re-)Design , University of Michigan, Information and Technology Services
ChatGPT Assignments to Use in Your Classroom Today , University of Central Florida

Works Cited

Allen, D., and Tanner, K. (2006). Rubrics: Tools for Making Learning Goals and Evaluation Criteria Explicit for Both Teachers and Learners. CBE - Life Sciences Education. 5(3): 197-203.

Ashford-Rowe, K., Herrington, J., & Brown, C. (2014). Establishing the critical elements that determine authentic assessment. Assessment & Evaluation in Higher Education, 39. https://doi.org/10.1080/02602938.2013.819566&nbsp ;

Bijlsma-Rutte, A., Rutters, F., Elders, P. J. M., Bot, S. D. M., & Nijpels, G. (2018). Socio-economic status and HbA1c in type 2 diabetes: A systematic review and meta-analysis. Diabetes/Metabolism Research and Reviews, 34(6), e3008. https://doi.org/10.1002/dmrr.3008&nbsp ;

CAST. (n.d.). UDL: The UDL Guidelines. Retrieved January 22, 2024, from https://udlguidelines.cast.org/&nbsp ;

Exploring AI Pedagogy. (n.d.). A Community Collection of Teaching Reflections. Retrieved January 22, 2024, from https://exploringaipedagogy.hcommons.org/&nbsp ;

France, P. E. (2022). Reclaiming Personalized Learning: A Pedagogy for Restoring Equity and Humanity in Our Classrooms (2nd ed.). Corwin.

Headden, S., & McKay, S. (2015). Motivation Matters: How New Research Can Help Teachers Boost Student Engagement. Carnegie Foundation for the Advancement of Teaching. https://eric.ed.gov/?id=ED582567&nbsp ;

Hume Center for Writing and Speaking. (n.d.). Documentation and Citation. Retrieved January 22, 2024, from https://hume.stanford.edu/resources/student-resources/writing-resources… ;

Lage, M. J., Platt, G. J., & Treglia, M. T. (2000). Inverting the Classroom: A gateway to creating an inclusive learning environment. Journal of Economic Education, 31(1), 30-43.

metaLAB (at) Harvard. (n.d.). The AI Pedagogy Project. Retrieved January 22, 2024, from https://aipedagogy.org/&nbsp ;

MLA Style Center. (2023, March 17). How do I cite generative AI in MLA style? https://style.mla.org/citing-generative-ai/&nbsp ;

Office of Community Standards. (n.d.). What Is Plagiarism? Retrieved January 22, 2024, from https://communitystandards.stanford.edu/policies-guidance/bja-guidance-… ;

Sambell, K., Brown, S., & Race, P. (2019). Assessment to Support Student Learning: Eight Challenges for 21st Century Practice. All Ireland Journal of Higher Education, 11(2), Article 2. https://ojs.aishe.org/index.php/aishe-j/article/view/414&nbsp ;

The WAC Clearinghouse. (n.d.). January 2024. Retrieved January 22, 2024, from https://wac.colostate.edu/repository/collections/continuing-experiments… ;

U-M Generative AI. (n.d.). Course and Assignment (Re-)Design. Retrieved January 22, 2024, from https://genai.umich.edu/guidance/faculty/redesigning-assessments&nbsp ;

Van Velzen, J. (2017). Metacognitive Knowledge: Development, Application, and Improvement. Information Age Publishing. https://content.infoagepub.com/files/fm/p599a21e816eb6/9781641130240_FM… . ISBN 9781641130226.

Wylie, E. C., Gullickson, A. R., Cummings, K. E., Egelson, P., Noakes, L. A., Norman, K. M., Veeder, S. A., ... Popham, W. J. (2012). Improving Formative Assessment Practice to Empower Student Learning. Corwin Press.

Xu, X., Shen, W., Islam, A. A., et al. (2023). A whole learning process-oriented formative assessment framework to cultivate complex skills. Humanities and Social Sciences Communications, 10, 653. https://doi.org/10.1057/s41599-023-02200-0

Yee, K., Whittington, K., Doggette, E., & Uttich, L. (2023). ChatGPT Assignments to Use in Your Classroom Today. UCF Created OER Works, (8). Retrieved from https://stars.library.ucf.edu/oer/8

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We hope that you found these modules useful and engaging, and are better able to address AI chatbots in your teaching practice. Please continue to engage by joining or starting dialogues about AI within your communities. You might also take advantage of our peers across campus who are developing resources on this topic.

Institute for Human-Centered Artificial Intelligence
Accelerator for Learning
Office of Innovation and Technology , Graduate School of Education

We are continuing to develop more resources and learning experiences for the Teaching Commons on this and other topics. We'd love to get your feedback and are looking for collaborators. We invite you to join the Teaching Commons team .

Learning together with others can deepen the learning experience. We encourage you to organize your colleagues to complete these modules together or facilitate a workshop using our Do-it-yourself Workshop Kits on AI in education. Consider how you might adapt, remix, or enhance these resources for your needs.

If you have any questions, contact us at [email protected] . This guide is licensed under Creative Commons BY-NC-SA 4.0 (attribution, non-commercial, share-alike) and should be attributed to Stanford Teaching Commons.

Artificial Intelligence (AI) in Education

Background Information
AI and ethics
Academic integrity, syllabi statements, & AI

AI detection Tools

Use ai and document the work, move away from the five paragraph essay, in-class essays, collaborative activities & discussion, meaning-making activities, brain dump activities, explain the process, impromptu oral exams, more obscure reading selections, field observations, recommended readings, references for assignment ideas.

How to cite AI

Attribution

This page is based on Chatbots & Critical Pedagogy from AI in the Classroom .

AI Detection Tools are in development; however, they may not be reliable because they are just emerging. Faculty who choose to use a detection tool should use caution when interpreting results, because false positives are possible.

What are Artificial Intelligence (AI), ChatGPT and AI Detection Tools?
GenAI detection software not yet reliable enough (University of Pittsburgh)

The general practice of citation is that you cite anything that comes from somewhere else; anything that isn't your original thought, isn't common knowledge, and/or is a place where you pulled information from.

Where an assignment requires an AI source to be cited, you must reference all the content from tool that you include in your assignment. Failure to reference externally sourced, non-original work can result in scholastic dishonesty. References should provide clear and accurate information for each source and should identify where they have been used in your work.

AI archives The website extension saves your chatGPT or Bard conversations and creates a URL, which allows readers to reference the original conversation used by the author. This tool is particularly useful for creating citations.
Teaching Toolbox: Chat GPT Provides several ideas and suggestions that can foster responsible use of ChatGPT in assignments.

Chatbots can follow this format easily. Encourage your students' originality by moving away from this formulaic format.

Tip: If you want to stick with the five-paragraph essay, test out your prompt on an advanced chatbot like ChatGPT. Greene ( 2022 ) writes, "If it can come up with an essay that you would consider a good piece of work, then that prompt should be refined, reworked, or simply scrapped... if you have come up with an assignment that can be satisfactorily completed by computer software, why bother assigning it to a human being?"
Sticking with essays? Warner ( 2022 ) suggests focusing on process rather than product. Scaffolding learning and allowing students to explain their thinking and make learning visible along the way are strategies that may help you confirm student originality: "I talk to the students, one-on-one about themselves, about their work. If we assume students want to learn - and I do - we should show our interest in their learning, rather than their performance."

In the short-term, you can have your students write essays in class and on paper .

For longer research papers, students will have access to chatbots outside of class.
Students may need to use online resources for their writing.
You won't be able to use the LMS feedback tools for annotation, rubric scoring, and grading.
Note: Some students may have accommodations to type their work rather than handwrite it. Make sure to follow student accommodations when assigning work

Use collaborative activities and discussions to mitigate the use of chatbot responses in your class.

While students may generate ideas from a chatbot, they will need to discuss with one another whether they want to use the chatbot responses, if they fit the prompt, and if they are factually accurate.

These strategies can work for online courses with a few tweaks. For discussions, ask students to post a recording rather than text. While students may generate a response using ChatGPT, creating their video will require more interaction with the content than copy-pasting a text response would.

Engage your students in meaning-making activities to demonstrate their learning.

This could include: Skits*, Drawings and Sketches, Concept Mapping, Infographics*, Digital Storytelling*, or Write* or revise Wikipedia articles (Wiki Education). Other ideas from:

Let students choose a medium and activity (“Digital Media Design Student Choice Board” by Torrey Trust is licensed under CC BY NC SA 4.0 )
Fun formative assessment: 12 easy, no-tech ideas (Ditch That Textbook)

* Note that a chatbot can provide an outline for these activities.

Brain dumps are an ungraded recall strategy.

The practice involves pausing a lecture and asking students to write everything they can recall about a specific topic. Read more at:

Brain Dump: A small strategy with a big impact (Retrieval Practice)

During or after writing, students explain their process or thinking.

Students could:

Use Comments in Word or Google Docs;
Create a video explaining their change history on a Google Doc;
Use Track Changes to show their revisions.

Consider using planned or impromptu oral exams.

You may consider including phrasing in your syllabus about conducting oral exams if you suspect plagiarism through the use of a chatbot.

When selecting readings, consider sourcing more obscure texts for your students to read.

Chatbots may have less information in their training data on obscure texts. As an example, the New York Times reports that, "Frederick Luis Aldama, the humanities chair at the University of Texas at Austin, said he planned to teach newer or more niche texts that ChatGPT might have less information about, such as William Shakespeare’s early sonnets instead of 'A Midsummer Night’s Dream'" (Huang, 2023).

(Note that ChatGPT is currently trained on data through 2021. Some educators suggest using newer writings and research, but this strategy isn't foolproof since the training models for chatbots are updated frequently.)

Coordinate times to take your class to conduct field observations; students can note their observations and write a reflection about their experience.

A Teacher's Prompt Guide to ChatGPT Created by Centre for Education Statistics and Evaluation, New South Wales, Australia
Critical Questions about Technology We encourage you to approach chatbot tools with a critical lens before structuring course assignments with these tools. Some students may be unaware of these tools and what they can do, and others may only be thinking about how they can benefit from the tool.
ChatGPT and Assessment by Ean Henninger, UNM Office of Assessment
Teaching With and About AI By Lori Townsend, University Libraries

Aaronson, S. (2022, November 28). My AI safety lecture for UT Effective Altruism . Shtetl-Optimized: The blog of Scott Aaronson .

Bowman, E. (2023, January 9). A college student created an app that can tell whether AI wrote an essay . NPR .

Caines, A. (2022, December 29). ChatGPT and good intentions in higher ed. Is a Liminal Space .

Caren, C. (2022, December 15). AI writing: The challenge and opportunity in front of education now . Turnitin .

Chechitelli, A. (2023, January 13). Sneak preview of Turnitin’s AI writing and ChatGPT detection capability . Turnitin .

Ditch That Textbook. (2022, December 17). ChatGPT, chatbots and artificial intelligence in education .

Greene, P. (2022, December 11). No, ChatGPT is not the end of high school English. But here’s the useful tool it offers teachers . Forbes .

Hick, D.H. (2022, December 15). Today, I turned in the first plagiarist I’ve caught using A.I. software to write her work [Facebook post]. Facebook .

Huang, K. (2023, January 16). Alarmed by A.I. chatbots, universities start revamping how they teach . New York Times .

Kelley, K.J. (2023, January 19). Teaching actual students writing in an AI world . Inside Higher Ed .

OpenAI. (2022, December). ChatGPT FAQ .

Trust, T. (2023). ChatGPT & education [Google Slides]. College of Education, University of Massachusetts Amherst.

Warner, J. (2022, December 11). ChatGPT can't kill anything worth preserving: If an algorithm is the death of high school English, maybe that's an okay thing . The Biblioracle Recommends .

Watkins, R. (2022, December 18). Update your course syllabus for chatGPT . Medium .

Wiggers, K. (2022, Decemer 10). OpenAI’s attempts to watermark AI text hit limits . TechCrunch .

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AI Teaching Strategies: Transparent Assignment Design

The rise of generative artificial intelligence (AI) tools like ChatGPT, Google Bard, and Jasper Chat raises many questions about the ways we teach and the ways students learn. While some of these questions concern how we can use AI to accomplish learning goals and whether or not that is advisable, others relate to how we can facilitate critical analysis of AI itself.

The wide variety of questions about AI and the rapidly changing landscape of available tools can make it hard for educators to know where to start when designing an assignment. When confronted with new technologies—and the new teaching challenges they present—we can often turn to existing evidence-based practices for the guidance we seek.

This guide will apply the Transparency in Learning and Teaching (TILT) framework to "un-complicate" planning an assignment that uses AI, providing guiding questions for you to consider along the way.

The result should be an assignment that supports you and your students to approach the use of AI in a more thoughtful, productive, and ethical manner.

Plan your assignment.

The TILT framework offers a straightforward approach to assignment design that has been shown to improve academic confidence and success, sense of belonging, and metacognitive awareness by making the learning process clear to students (Winkelmes et al., 2016). The TILT process centers around deciding—and then communicating—three key components of your assignment: 1) purpose, 2) tasks, and 3) criteria for success.

Step 1: Define your purpose.

To make effective use of any new technology, it is important to reflect on our reasons for incorporating it into our courses. In the first step of TILT, we think about what we want students to gain from an assignment and how we will communicate that purpose to students.

The SAMR model , a useful tool for thinking about educational technology use in our courses, lays out four tiers of technology integration. The tiers, roughly in order of their sophistication and transformative power, are S ubstitution, A ugmentation, M odification, and R edefinition. Each tier may suggest different approaches to consider when integrating AI into teaching and learning activities.

For full text of this image, see transcript linked in caption.

Questions to consider:

Do you intend to use AI as a substitution, augmentation, modification, or redefinition of an existing teaching practice or educational technology?
What are your learning goals and expected learning outcomes?
Do you want students to understand the limitations of AI or to experience its applications in the field?
Do you want students to reflect on the ethical implications of AI use?

Bloom’s Taxonomy is another useful tool for defining your assignment’s purpose and your learning goals and outcomes.

This downloadable Bloom’s Taxonomy Revisited resource , created by Oregon State University, highlights the differences between AI capabilities and distinctive human skills at each Bloom's level, indicating the types of assignments you should review or change in light of AI. Bloom's Taxonomy Revisited is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Access a transcript of the graphic .

Step 2: Define the tasks involved.

In the next step of TILT, you list the steps students will take when completing the assignment. In what order should they do specific tasks, what do they need to be aware of to perform each task well, and what mistakes should they avoid? Outlining each step is especially important if you’re asking students to use generative AI in a limited manner. For example, if you want them to begin with generative AI but then revise, refine, or expand upon its output, make clear which steps should involve their own thinking and work as opposed to AI’s thinking and work.

Are you designing this assignment as a single, one-time task or as a longitudinal task that builds over time or across curricular and co-curricular contexts? For longitudinal tasks consider the experiential learning cycle (Kolb, 1984) . In Kolb’s cycle, learners have a concrete experience followed by reflective observation, abstract conceptualization, and active experimentation. For example, students could record their generative AI prompts, the results, a reflection on the results, and the next prompt they used to get improved output. In subsequent tasks students could expand upon or revise the AI output into a final product. Requiring students to provide a record of their reflections, prompts, and results can create an “AI audit trail,” making the task and learning more transparent.
What resources and tools are permitted or required for students to complete the tasks involved with the assignment? Make clear which steps should involve their own thinking (versus AI-generated output, for example), required course materials, and if references are required. Include any ancillary resources students will need to accomplish tasks, such as guidelines on how to cite AI , in APA 7.0 for example.
How will you offer students flexibility and choice? As of this time, most generative AI tools have not been approved for use by Ohio State, meaning they have not been vetted for security, privacy, or accessibility issues . It is known that many platforms are not compatible with screen readers, and there are outstanding questions as to what these tools do with user data. Students may have understandable apprehensions about using these tools or encounter barriers to doing so successfully. So while there may be value in giving students first-hand experience with using AI, it’s important to give them the choice to opt out. As you outline your assignment tasks, plan how to provide alternative options to complete them. Could you provide AI output you’ve generated for students to work with, demonstrate use of the tool during class, or allow use of another tool that enables students to meet the same learning outcomes.

Microsoft Copilot is currently the only generative AI tool that has been vetted and approved for use at Ohio State. As of February 2024, the Office of Technology and Digital Innovation (OTDI) has enabled it for use by students, faculty, and staff. Copilot is an AI chatbot that draws from public online data, but with additional security measures in place. For example, conversations within the tool aren’t stored. Learn more and stay tuned for further information about Copilot in the classroom.

What are your expectations for academic integrity? This is a helpful step for clarifying your academic integrity guidelines for this assignment, around AI use specifically as well as for other resources and tools. The standard Academic Integrity Icons in the table below can help you call out what is permissible and what is prohibited. If any steps for completing the assignment require (or expressly prohibit) AI tools, be as clear as possible in highlighting which ones, as well as why and how AI use is (or is not) permitted.

Promoting academic integrity

While inappropriate use of AI may constitute academic misconduct, it can be muddy for students to parse out what is permitted or prohibited across their courses and across various use cases. Fortunately, there are existing approaches to supporting academic integrity that apply to AI as well as to any other tool. Discuss academic integrity openly with students, early in the term and before each assignment. Purposefully design your assignments to promote integrity by using real-world formats and audiences, grading the process as well as the product, incorporating personal reflection tasks, and more.

Learn about taking a proactive, rather than punitive, approach to academic integrity in A Positive Approach to Academic Integrity.

Step 3: Define criteria for success.

An important feature of transparent assignments is that they make clear to students how their work will be evaluated. During this TILT step, you will define criteria for a successful submission—consider creating a rubric to clarify these expectations for students and simplify your grading process. If you intend to use AI as a substitute or augmentation for another technology, you might be able to use an existing rubric with little or no change. However, if AI use is modifying or redefining the assignment tasks, a new grading rubric will likely be needed.

How will you grade this assignment? What key criteria will you assess?
What indicators will show each criterion has been met?
What qualities distinguish a successful submission from one that needs improvement?
Will you grade students on the product only or on aspects of the process as well? For example, if you have included a reflection task as part of the assignment, you might include that as a component of the final grade.

Alongside your rubric, it is helpful to prepare examples of successful (and even unsuccessful) submissions to provide more tangible guidance to students. In addition to samples of the final product, you could share examples of effective AI prompts, reflections tasks, and AI citations. Examples may be drawn from previous student work or models that you have mocked up, and they can be annotated to highlight notable elements related to assignment criteria.

Present and discuss your assignment.

Students gathered around a laptop, smiling.

As clear as we strive to be in our assignment planning and prompts, there may be gaps or confusing elements we have overlooked. Explicitly going over your assignment instructions—including the purpose, key tasks, and criteria—will ensure students are equipped with the background and knowledge they need to perform well. These discussions also offer space for students to ask questions and air unanticipated concerns, which is particularly important given the potential hesitance some may have around using AI tools.

How will this assignment help students learn key course content, contribute to the development of important skills such as critical thinking, or support them to meet your learning goals and outcomes?
How might students apply the knowledge and skills acquired in their future coursework or careers?
In what ways will the assignment further students’ understanding and experience around generative AI tools, and why does that matter?
What questions or barriers do you anticipate students might encounter when using AI for this assignment?

As noted above, many students are unaware of the accessibility, security, privacy, and copyright concerns associated with AI, or of other pitfalls they might encounter working with AI tools. Openly discussing AI’s limitations and the inaccuracies and biases it can create and replicate will support students to anticipate barriers to success on the assignment, increase their digital literacy, and make them more informed and discerning users of technology.

Explore available resources It can feel daunting to know where to look for AI-related assignment ideas, or who to consult if you have questions. Though generative AI is still on the rise, a growing number of useful resources are being developed across the teaching and learning community. Consult our other Teaching Topics, including AI Considerations for Teaching and Learning , and explore other recommended resources such as the Learning with AI Toolkit and Exploring AI Pedagogy: A Community Collection of Teaching Reflections.

If you need further support to review or develop assignment or course plans in light of AI, visit our Help forms to request a teaching consultation .

Using the Transparent Assignment Template

Sample assignment: ai-generated lesson plan.

In many respects, the rise of generative AI has reinforced existing best practices for assignment design—craft a clear and detailed assignment prompt, articulate academic integrity expectations, increase engagement and motivation through authentic and inclusive assessments. But AI has also encouraged us to think differently about how we approach the tasks we ask students to undertake, and how we can better support them through that process. While it can feel daunting to re-envision or reformat our assignments, AI presents us with opportunities to cultivate the types of learning and growth we value, to help students see that value, and to grow their critical thinking and digital literacy skills.

Using the Transparency in Learning and Teaching (TILT) framework to plan assignments that involve generative AI can help you clarify expectations for students and take a more intentional, productive, and ethical approach to AI use in your course.

Step 1: Define your purpose. Think about what you want students to gain from this assignment. What are your learning goals and outcomes? Do you want students to understand the limitations of AI, see its applications in your field, or reflect on its ethical implications? The SAMR model and Bloom's Taxonomy are useful references when defining your purpose for using (or not using) AI on an assignment.
Step 2: Define the tasks involved. L ist the steps students will take to complete the assignment. What resources and tools will they need? How will students reflect upon their learning as they proceed through each task? What are your expectations for academic integrity?
Step 3: Define criteria for success. Make clear to students your expectations for success on the assignment. Create a rubric to call out key criteria and simplify your grading process. Will you grade the product only, or parts of the process as well? What qualities indicate an effective submission? Consider sharing tangible models or examples of assignment submissions.

Finally, it is time to make your assignment guidelines and expectations transparent to students. Walk through the instructions explicitly—including the purpose, key tasks, and criteria—to ensure they are prepared to perform well.

Checklist for Designing Transparent Assignments
TILT Higher Ed Information and Resources

Winkelmes, M. (2013). Transparency in Teaching: Faculty Share Data and Improve Students’ Learning. Liberal Education 99 (2).

Wilkelmes, M. (2013). Transparent Assignment Design Template for Teachers. TiLT Higher Ed: Transparency in Learning and Teaching. https://tilthighered.com/assets/pdffiles/Transparent%20Assignment%20Templates.pdf

Winkelmes, M., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., Weavil, K. (2016). A Teaching Intervention that Increases Underserved College Students’ Success. Peer Review.

ARTIFICIAL INTELLIGENCE AT NORTHWESTERN

Assignments, generative artificial intelligence and assignments.

Once you've chosen the policy framework for students' use of generative artificial intelligence (GAI) in your course, you will need to extend that guidance to your assignments and assessments.

(Framework inspired by Forbes, M. & Brandauer J. "What’s my stance on genAI in this class?" Gettysburg College Johnson Center for Teaching and Learning. Retrieved 8/20/2023 from https://genai.sites.gettysburg.edu/positions-and-policies )

If you choose to "close" a particular assignment to the use of GAI, you may want to articulate your rationale. Closing the assignment may mean that you will need to redesign it. You might consider using guidelines for the assignment that ask students to do what text-generating Large Language Model (LLM) tools such as ChatGPT, Bard, or Claude do not do well. For example, you could:

Make the assignment very current , as some LLMs have knowledge cutoff dates of more than a year ago.
Incorporate hyperlocal context, which may have intrinsic appeal to students and may encourage critical understanding about the LLM's capacity to yield worthwhile outputs.
Add clear source and citation requirements if they differ from general course expectations.
Add specific elements to a rubric that assess critical thinking.

You might also follow the guidelines of the Universal Design for Learning and focus on multiple means of expression. Students could:

Create video or audio responses to the assignment, rather than text
Annotate in a text using Perusall or Hypothesis , which are integrated with Canvas.

While in-class written work and high-stakes assessments can effectively "close" assessments to GAI, keep in mind that these practices may put students with accommodations through AccessibleNU at a disadvantage and can exacerbate student stress.

Conditional or Open: Permitting or Requiring GAI Use

If your course is open or if you choose conditional use of GAI for your course, consider the following elements when permitting or requiring students to use GAI on assignments:

Students should only be required to use what is available and free
Share your reasoning for the use of GAI in the assignment and its value to students
Ask students to use only data that are not private or personal (See Syllabus statement for instructors who engage students in using generative AI systems/software )
Add a warm-up exercise to familiarize students with the tools you have chosen
Be specific about how you would like the students to use the GAI and explain why: is it a starting point or idea generator? A debate partner? An editor for student-authored work?
Incorporate reflective opportunities to inspire metacognition
Consider the principles of the Universal Design for Learning and allow multiple means of expression (presentations, video essays, etc.)
Familiarize your students with the Northwestern University Library citation guidelines for GAI

Some ideas for having students use LLMs as a starting point for an assignment include:

Start with an i ntroductory exercise that provides an ethical use case of ChatGPT
Brainstorm by asking an LLM questions about the material or subject (theories, frameworks, problems, etc.)
Ask an LLM to brainstorm ideas for a project, such as a new business idea
Ask an LLM for feedback on their work
Ask an LLM to summarize a source they have consulted
Ask an LLM to analyze data they have gathered
Prompt the LLM to take one side of a debate
Prompt the LLM to write a sonnet on a particular topic and compare it with an existing sonnet
Prompt the LLM to summarize a historical event, person, or period and have students discuss, correct, interrogate for accuracy and credibility

As part of the assignment, students could be asked to explain their use of GAI:

Include a reflective paragraph on their LLM usage that details how they used it, what it provided and why it was or was not beneficial to their final product
Include a copy of all prompts and text from the LLM as an appendix
Identify issues of bias, relevance, and accuracy that they encounter while using an LLM
Post using Discussions in Canvas to share work with the LLM while it is in progress

Example: Ask ChatGPT to write an essay on your topic

"You have been researching a particular topic for your final presentation. I'd like you to ask ChatGPT 3.5 to write a 500-word essay on the historical importance of your topic. Copy that essay into Word, along with the prompt you gave it. Then turn on Track Changes in Word and edit the essay: correct any errors, verify any facts that ChatGPT cited by putting a comment on the fact and showing at least one other source for it, improve the writing to make the essay clearer and more interesting. Then write a paragraph or two titled "Feedback" that explains your overall assessment of the ChatGPT essay and give it a grade. Submit your Word document to Canvas."

Rationale: This assignment falls part way through a course, after students have developed expertise on a topic. By engaging with ChatGPT, students will:

Express their expertise by corroborating or debunking the items in the essay
Use historical analytical skills by verifying facts and checking other sources
Use editing skills to improve writing
Put themselves in an evaluative mode and explain their thinking.

Consider the following examples of assignments that have been adapted to make use of GAI in ways that will advance learning:

Example 1: A communication and marketing plan

Part of the original assignment asked students to develop a communication and marketing plan, which took about three weeks.

The revised assignment instructed students to ask ChatGPT to draft multiple communication and marketing plans. Next, the students are required to analyze the results; identify, with justification, the best elements of the various plans; and adapt these into a single plan.

Rationale: This assignment may shorten the amount of time devoted to the nuts and bolts of the assignment - developing the plan - allowing the students more time to evaluate, analyze, and synthesize.

Example 2: A lab report

Part of the original assignment asked students to gather data and write a lab report detailing the purpose, methods, and findings of their experiment.

In the revised assignment, students were given an editable, ChatGPT-generated lab report as an example of "C" quality work. In addition, they were familiarized with the rubric for evaluation. Students were asked to update the lab report with their own results, edit its analysis, and try to improve it from "C" to "A" quality work.

Rationale: This assignment will not shorten the amount of time devoted to the laboratory work, but it may deepen students' analysis and editing skills.

Video Examples

Ignacio Cruz, Assistant Professor, Communication Studies, "Classroom Activity: AI-Enabled Hiring."
Ken Alder, Professor, History, "Assignment: Using ChatGPT for Research Projects."

See the Northwestern University Writing Program AI Resources site for an extensive list of ways to incorporate generative AI into writing assignments.

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In-Depth Artificial Intelligence Research Topics
Important Artificial Intelligence Dissertation Topics

Are you struggling to decide on a topic for your paper? Worry not! This blog will provide with all you need to choose the best topic for your dissertation. Besides, it will also tell you about what it is and the tips to remember while deciding. After reading this, you will easily decide on the perfect artificial intelligence dissertation topics for the document. So, you will read more about it ahead.

What Is Artificial Intelligence? Understand in Brief!

Artificial intelligence, or AI, is the ability of a machine to perform tasks related to cognitive functions, or, as we call it, the human work-frame. It can do everything you imagined, being human functions and never others. It includes activities like reasoning, learning, exercising, thinking, interaction, and creativity. Likewise, it's much more than we already got a glimpse of, with the wide range of development in AI. Artificial intelligence can do functions that humans might take several infinite years to do in the blink of an eye, like solving complex calculations. It has made AI dissertation topics, a curious choice for students to learn about.

Today, AI is used in almost all places and has become a part of your life, whether you realize it yet or not. It is helping us navigate the world of easier functioning with tasks such as logistics, predictive maintenance, customer service, and much more.

So, this blog will help you to know about it, along with helping you choose some of the best artificial intelligence dissertation topics that will guide you to learn more about it in depth.

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Know About the Basic AI Dissertation Structure

With the rapid increase in AI use, it is becoming a topic of interest to learners, with its applications in almost all sectors. But, before deciding your research topics on AI, it's essential to understand its basic structure first. It's necessary so you don't get confused with what's to be done in your document. It mainly depends on the type of paper, but scientifically, there are a few things common in them all. There are some of them below for your proper understanding.

Knowing these will help you in deciding the artificial intelligence dissertation topics for your paper.

The Introduction:

Here, you have to mention the context of your studies. Talk about your problem statement and the motivation behind whatever you choose. Give a brief description of the AI and scope you are looking to achieve, along with its significance. Tell your audience about the artificial intelligence dissertation ideas for the overall study overview.

Background Chapters:

In these chapters, you might want to include everything that proceeds in your paper. Along with all the experiments, methods, discussions, results, and organization, we include them all in different sections and chapters here. Also, you will clarify the paper type you decide among all the different types of dissertation documents.

The Conclusion:

While ending your dissertation, remember to interlink it all before wrapping up. Connect everything, including all the discussions and results, with each other before you end. Ensure to talk about the artificial intelligence dissertation topics that you select. Furthermore, elaborate on the future call to action for the document and how it can have an impact. Avoid adding new information here, but focus and highlight whatever you have already written.

So, you have read about the basic structure of writing a dissertation paper. Now, let us read about how to choose the best AI dissertation topics for it.

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6 Tips for Choosing Dissertation Topics on AI

Many tips around might confuse and divert your attention. So, here we have combined the basic and most essential tips in one place to help you find good research topics on AI. So, moving further you will read about them in detail.

A dissertation document may take a long time to finish, so select a topic that interests you. It is very significant to choose dissertation topics in artificial intelligence that make you curious as well as help you in your career. Picking such a subject or field for your research will enable a great understanding of it. Furthermore, it will give you the additional strength to move ahead on your chosen path as you like. It will help you maintain the same passion throughout your journey.

Your artificial intelligence research topics should be quite different in themselves. Picking a unique topic will give you the freedom to take the desired approach to the topic and find your results. For this, you can either select a completely off-beat topic that requires dedicated research within its scope. Moreover, take your perspective on something already done before. It will help make an impression on your mentor and audience with something they haven't read yet.

A dissertation project is academic writing which has everything contributing towards something. Therefore, deciding on a fuzzy idea might not give the desired results. To avoid this from happening, you should select a topic that is precise and follows a proper dissertation structure . It will help you explore the topic and draw concise results from the given word count. Keep it broad for the proper research scope.

For this, you can even seek dissertation help online to make your document worth it all.

There are various types of research, so it is necessary to plan what type of research you wish to do and its relevance. For this, you can even find many examples of dissertations online or in your university library. However, it should contribute to your field and advance the reader's knowledge about the problems and solutions. To do this in a good way, feel free to decide on something that is currently working or is commonly faced. Analyse and collect the data, and then define these details about your paper.

Doing good research before choosing artificial intelligence dissertation topics for you is probably the best thing you can do. It will help you know if there's enough scope to proceed with the idea in your head. Keep narrowing down to the potential topic that looks good to you and getting more specific slowly. Furthermore, try to find a proper niche that you wish to cover in your document. For this, you can try the artificial intelligence assignment help to get support in deciding the steps to move ahead.

Being objective while working on your paper is necessary because it will help you stay balanced and do justice to it. Sometimes, when you are in the flow, it's easier to lose track and leave blind spots. To avoid that, imagine yourself as an outsider and look at the work from a new perspective.

Seek help from your mentor because they are there to help and have years of experience to see things you may miss. So, seek their guidance and recommendations to find the best artificial intelligence dissertation topics for your document.

Remember that it's not bad to seek help whenever you need it. Be flexible and strengthen your mind for all the changes you face on your journey. It will ensure that you have an open mind while choosing your Dissertation Topics on AI and make them useful. So, these are all the basic tips to help you do just that.

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61 Artificial Intelligence Topics for Dissertations

Here, you will learn about some of the most trendy artificial intelligence topics for dissertations that are used in the areas with their in-depth fields of research. But, remember that with the new developments daily, these might need more new things added from time to time. So, let's go through some of them below.

Master Artificial Intelligence Dissertation Topics

Limitation of artificial intelligence
Use of artificial intelligence in education
Online security and threats using AI
Businesses using artificial intelligence
Automated banking with AI
Data management from artificial intelligence
Stopping online attacks using AI
Best trends in artificial intelligence
Use of AI at unimaginable places
AI in machine learning

Trendy Dissertation Topics on Artificial Intelligence

Educating artificial intelligence
Beginning of AI and its development
Major ethical issues caused by the use of AI
AI breaching data privacy
Development in computing after AI
AI quantum and edge computing
Space exploration with AI
Collaboration of robotics and event management
How can AI save lives?
Achieving the impossible with AI

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Unique AI Research Paper Topics

Robotic and automated driving
Educational artificial intelligence
National security threats with the wide use of AI
Disappointing AI experiments
AI robotics in the Mars rover
Lack of intellectual and emotional knowledge in AI
Internet of Things (IoT) and artificial intelligence (AI)
Technologies with AI & ml (machine learning)
Brainstimulation with artificial intelligence
Big data analysis using artificial intelligence

In-Depth Artificial Intelligence Research Topics

AI perspective in cybernetics
Social intelligence vs. Emotional intelligence in AI
The threat caused by the narrow use of artificial intelligence
Data science and artificial intelligence
Major challenges in using artificial intelligence
How does AI learn behavioral patterns?
Virtualization in computer frameworks using AI
Future of AI in Cybersecurity
Data mining by artificial intelligence
AI in online payment frauds

Important Artificial Intelligence Dissertation Topics

Ethical hacking using artificial intelligence
AI law enforcement
Types of artificial intelligence
Common issues in AI
Artificial intelligence and schooling
Hybrid techniques of AI
AI chatbots (Siri, Alexa)
Use of AI in logistics
Making of artificial intelligence
Clash of creative domains with AI
Using AI to solve complex problems

Here, you read about the 61 best artificial intelligence dissertation topics that will help you brainstorm the ideas for your paper.

Struggling With Dissertation Topics? Ask Our Experts!

First, deciding on some good artificial intelligence dissertation topics and then working on lengthy documents can sometimes be tough. Especially when you have to take care of everything, even an error can bring you many steps backward. Thus, you can hire our experts or seek support from the Assignment Desk, which provides very cheap dissertation writing services .

The professionals here have years of experience in writing documents with the subject expertise you might need. Furthermore, various offers and tools on the Assignment Desk will help you find the perfect artificial intelligence dissertation topics for your paper. So, contact us today!

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Top 16 Artificial Intelligence Project Ideas & Topics for Beginners [2024]

Artificial intelligence controls computers to resemble the decision-making and problem-solving competencies of a human brain. It works on tasks usually linked with learning or thinking, including reasoning and self-correction. Moreover, artificial intelligence blends robust datasets and computer science to derive solutions to the problem. You can design scalable AI-based solutions and acquire self-learning via practical applications.

The choice of artificial intelligence projects depends on various factors like your interest, budget, time, and trending topics.

Let’s look at some exciting AI project ideas and topics for beginners to improve their skills and enhance their portfolios.

Top AI Project Ideas & Topics

1. fake news finder.

Fake news means false or ambiguous information spread to misguide people. Occasionally, fake news is presented so professionally that people completely trust it. It is imperative to differentiate between original news and fake news. If not detected early, it can create many unimaginable issues.

Utilize the Real and Fake News dataset available on Kaggle to develop a fake news detector project. The classification of fake and original news occurs via a pre-trained ML model known as BERT. Essentially, it is an open-source NLP model being loaded into Python.

2. Teachable Machine

Working on a Teachable Machine is one of the most interesting artificial intelligence project ideas for beginner-level AI enthusiasts. A Teachable Machine refers to a web-based tool developed to offer people easy access to machine learning functionalities. Its website allows you to upload images of various classes. Subsequently, you can train a client-side ML model on those images. This project enables you to learn many potent machine-learning functionalities.

3. Autocorrect Tool

When you start working on such AI based projects , you can gradually streamline your everyday tasks. Autocorrect application of AI is used in daily life, which assists in correcting spelling and grammatical errors.

You can build this project in Python using its TextBlob library. Its function ‘correct()’ will be helpful for this project.

4. Fake Product Review Identification

It is one of those AI projects for beginners that can deter business owners who usually upload fake product reviews on their websites. Its implementation will ensure that customers will not be diverted to false product reviews when they perform their product research. You can use Kaggle to build this project. Kaggle contains a Deceptive Opinion Spam Corpus dataset with 1600 reviews (800 positive and 800 negative reviews).

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5. Plagiarism Analyzer

Plagiarism Analyzer is one of the most prevalent artificial intelligence project ideas. The reason is it can detect plagiarism which is imperative to ensure original content. It can be challenging to determine the originality of the content without using a tool. This project helps you to build a plagiarism analyzer application to ensure originality and authenticity across a piece of content.

6. Bird Species Predictor

Topic experts can manually classify birds, but the process can be challenging and monotonous since it needs a massive data collection. The Bird Species Predictor project uses AI-based categorization, which uses a random forest to predict bird species.

7. Stock Price Predictor

It is one of the most valuable artificial intelligence projects for finance professionals and students aspiring to embark on a career in finance. This project provides access to a broad range of datasets. These datasets let you learn how to use ML algorithms to inspect a considerable amount of data. The availability of a vast amount of data simplifies finding models and patterns. Ultimately, it becomes easy to predict the future stock market precisely.

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8. customer advice system.

It is one of the most prominent AI project ideas for those business owners willing to understand the customers’ product preferences. It uses a customer advice system to gain instant feedback on customers’ opinions of products. You need to build a real-time message tool within your e-commerce app. It helps you to communicate with customers and discerns their opinions regarding the products.

9. Lane Line Recognition

AI based projects are valuable for vehicles too. This project helps you develop a system connecting line-following robots and self-driven vehicles. So they can have real-time analysis of lane lines on a road. If self-driving cars are not effectively trained, it can lead to roadside accidents. This project solves this problem by using Python’s Computer Vision. It contributes to effectively detecting self-driven vehicles and reduces the risks of roadside accidents. Python’s OpenCV library helps you to accomplish this project.

10. Handwritten Digit Recognition

This project aims to develop a system that can identify handwritten digits using artificial neural networks. Usually, characters and digits written by humans represent different sizes, shapes, styles, and curves. The computers must be able to identify manual writing. The mentioned project uses artificial neural networks to develop a handwritten digit identification system to decode the digits that humans write accurately. CNN (Convolution Neural Network) is used for identifying digits on paper.

11. Pneumonia Detection

It is one of the most useful AI project ideas to detect pneumonia and ensure good health for people. Capturing patients’ X-ray images help you to detect diseases like a tumor, cancer, pneumonia, etc. But the images feature low visibility, and interpretation can be complex. This project aims to develop an AI system using CNN (Convolution Neural Networks) to identify pneumonia from a patient’s X-ray pictures effectively. It trains software solutions to detect and interpret this disease’s results accurately. The software processes the relevant information and tests it in the built-in database.

12. Recommendation System for Customers

It is one of the most versatile and prevalent AI projects for beginners in customer management. It builds a recommendation system that helps customers infer more details on products, music, video, and more. It uses concepts of machine learning, data mining, and ANN. The system drives more customers to the website and ultimately boosts the sales of a business.

13. Recognizing the genre of a song

This project imparts AI knowledge to beginners in an easy and fun-filled way. It is one of the famous mini- AI projects that gradually strengthen your AI skills. It helps you to recognize a song’s genre.

It uses an artificial neural network to identify the song and its genre. Subsequently, it showcases the appropriate playlist. You need to use Python’s Librosa library to derive all the necessary details of the song.

14. Predicting users’ forthcoming location

Travelers usually find it difficult to explore, especially when they travel to unaccompanied places. This AI project predicts the user’s most likely next location. It can be a restaurant or holiday venue. The projects make informed decisions using the LempelZiv (LZ) algorithm, Neural Networks (NNs), Markov Model (MM), Association rules, and Bayesian Networks.

15. Translator app

The translator app is an AI project that uses NLP fundamentals. It helps you to develop a translator app that helps translate a sentence from an unfamiliar language to your native one. It can be challenging and laborious to train an AI model from the beginning. However, you can use this project’s pre-trained models called ‘transformers’ that help you to translate any sentence easily. Python’s GluonNLP library can greatly assist in creating this app.

16. Housing Price Predictor

This project idea uses fundamental AI features to estimate home price variations. It also uses ML models and algorithms. To develop your dataset, you must download a public dataset from web scraping or Kaggle.

The next step is to clean the dataset by determining different null values, anomalies, duplicate entries, etc. Subsequently, you need to calculate different related histograms. As the project progresses, you will be acquainted with test web scraping methods and huge datasets to hold proficiency in the same.

Get Started With Your Machine Learning Journey on UpGrad

Hoping to cement your identity in the innovative world of artificial intelligence? upGrad’s Professional Certificate in Machine Learning and Artificial Intelligence program can be the right push you need to embark on this dynamic journey. This 7-month course imparts skills like Advanced SQL, Machine Learning, Predictive Analytics using Python, Time Series, NLP, Data Visualization, Hypothesis Testing, Decision Tree Models, and more.

Its exceptional aspects include 300 hours of hands-on learning, 100+ hours of live sessions, a Capstone project in your preferred domain, fortnightly small group coaching sessions, and more.

You can explore job opportunities as a Data Scientist, Senior Data Analyst, Mathematician/ Statistician, Big Data Engineer/ Data Engineer, Software Developer, etc., after completing this course.

Kick-start your career by working on such AI projects for beginners and gradually working on more advanced ones to enhance your skills and portfolio. These projects can fuel your growth, enhancing your skills and experience level simultaneously. So, make sure to work on any of the AI projects listed here and start soon!

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Frequently Asked Questions (FAQs)

A. (i) Reactive AI: It is the fundamental type of AI programmed to offer a predictable output as per the input. (ii) Limited Memory AI: It learns from past data and develops empirical knowledge based on observation of data or actions. (iii) Theory of Mind AI: This AI type can comprehend and remember emotions. (iv) Self-aware AI: Machines equipped with this AI type are automatically aware of their mental states and emotions.

A. You need to consider six steps when managing AI projects. They are listed below. (i) Problem identification (ii) Testing the problem solution (iii) Data management (iv) Choosing the suitable algorithm (v) Training the algorithm (vi) Product deployment on the suitable platform.

A. AI projects fail due to these reasons. (i) AI follows a data-centric approach that implies insufficient time or funds to collect data. (ii) Improper planning for continued AI, data iteration, model, and lifecycle. (iii) Misalignment of real-world data and communication against training models and data.

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.chakra .wef-1t4fkg7{margin-top:16px;margin-bottom:16px;line-height:normal;color:#ffffff;display:block;background:#000000;margin:0px;font-size:2.5rem;padding-left:16px;padding-right:16px;padding-bottom:12px;border-radius:0.25rem;border-top-left-radius:0;border-bottom-left-radius:0;-webkit-box-decoration-break:clone;-webkit-box-decoration-break:clone;box-decoration-break:clone;}@media screen and (min-width:37.5rem){.chakra .wef-1t4fkg7{display:inline;}}@media screen and (min-width:56.5rem){.chakra .wef-1t4fkg7{font-size:4rem;}} AI - artificial intelligence - at Davos 2024: What to know

'Artificial Intelligence as a Driving Force for the Economy and Society' is a key theme at the World Economic Forum's Annual Meeting. Image: Unsplash/Damian Markutt

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'Artificial Intelligence as a Driving Force for the Economy and Society' is a key theme at the World Economic Forum's Annual Meeting.
Advances in technology have the potential to help us solve global challenges, but innovation and guardrails are essential.
Read about some of the key sessions, reports and initiatives at Davos 2024 on AI, innovation and technology.
Check back here for regular updates throughout the week and use the navigation bar on the right to catch up on what you've missed .

If you’d never considered artificial intelligence's impact on your life, 2023 was probably the year that changed that.

From jobs to skills, and regulations and governance, AI permeated conversations like never before.

The impact it will have on jobs is on the radar of the International Monetary Fund (IMF) which has just released its Staff Discussion Note Gen-AI: Artificial Intelligence and the Future of Work .

It finds almost 40% of employment globally is exposed to AI, which rises to 60% in advanced economies. Among workers, those that are college-educated and women are more exposed to AI, but also more likely to reap the benefits, while strong productivity gains could boost growth and wages.

Countries around the world have been exploring regulation and governance around AI, including the European Union, where a draft deal on AI rules was agreed in December.

We also held our own AI Governance Summit , in response to rising concerns about the technology’s impact, released a set of recommendations , and explored the impact of AI and large language models on jobs .

As we look ahead to 2024 at Davos , AI as a driving force is one of our four key themes. Below, we’ll keep you up to date on what to watch, read and look out for.

Live updates on key AI sessions

Dive into the key quotes, tweets and YouTube clips from Davos sessions on AI.

What to know from Day 2

Generative AI: Steam Engine of the Fourth Industrial Revolution?

Speakers from government and business discussed the implications of generative AI following its rapid emergence in 2023, and how we can manage the risks.

But there was also a strong focus on how much it could boost productivity and its possible applications, with Senator Mike Rounds, from South Dakota, US, believing it can transform healthcare.

Finnovation

Business leaders discussed how to ensure the benefits of AI outweigh the risks in fin-tech.

The Expanding Universe of Generative Models

Gen AI is advancing rapidly, but what is the latest research and development in the field and what future opportunities will the technology offer?

"AI can solve really hard, aspirational problems, that people maybe are not capable of solving" such as health, agriculture and climate change, said Daphne Koller , Founder and CEO at Insitro Inc.

"We're not done with scaling [LLMs], we still need to push up," said Aiden Gomez , Co-founder and CEO of Cohere Inc.

A Conversation with Satya Nadella

Microsoft’s CEO on AI and limiting ‘unintended consequences’

The Forum's Founder and Executive Chairman, Klaus Schwab had his annual fireside with the Microsoft CEO, which touched on balancing the risks and "unintended consequences" against the benefits of generative AI.

“The biggest lesson of history is… not to be so much in awe of some technology that we sort of feel that we cannot control it, we cannot use it for the betterment of our people.”

AI: The Great Equaliser?

We need to bridge the gap between AI's potential and its practical application. How can we ensure equal access to the technology?

"AI will not rescue the SDGs," said Amandeep Singh Gill , the UN Secretary-General's Envoy on Technology.

Rwanda's Minister of Information Communication Technology and Innovation, Paula Ingabire , said AI was more of an opportunity than a challenge for the Global South but digital literacy and the cost of devices need to be addressed.

What to know from Day 3

Thinking through Augmentation

Much of the potential of AI hinges on its use in the workplace. This session brought together the chief executives of Deloitte, Sanofi, L'Oréal, and Exponential View, to explore the most likely scenarios for jobs and productivity.

Job function groups with the highest exposure (auotmation and augmention)

360° on AI Regulations

Microsoft President Brad Smith joined Arati Prabhakar, the Director of the White House Office of Science and Technology Policy, Vera Jourová , Vice-President for Values and Transparency at the European Commission, and Josephine Teo , Singapore's Minister for Communications and Information, to discuss the future of AI governance.

There are diverse approaches to regulating AI, from the US, EU and multi-nationally to date, but Brad Smith said he expects more convergence in the future.

"We won't have a world without divergence, but people actually care about a lot of the same things and actually have similar approaches to addressing them."

Jourová said AI promises "a lot of fantastic benefits for people".

The regulation is the precondition to cover the risks, but the rest remains to be free for creativity and positive thinking - and in Europe we are well placed.

Ethics in the Age of AI

Philosopher Michael Sandel explored the ethical questions AI poses, beyond jobs, fairness, privacy and democracy to whether technology would affect what it means to be human.

If we can digitally de-age the actor Harrison Ford in the latest Indiana Jones movie, it is OK to bring back actors such as Humphrey Bogart from the dead?

Sandel showed the audience a video interview of him and director and actor Michael B Jordan discussing casting deceased actors.

It boiled down, he said, to a deep human value of authenticity and presence.

He concluded: "Will new technologies lead us, or are they already leading us and our children to confuse virtual communities and human connection for the real thing? Because if they do, then we may lose something precious about what it means to be human."

What to know from Day 4

Education Meets AI

AI has the potential to change education and the way we learn. Emilija Stojmenova Duh , Slovenia's Minister of Digital Transformation, joined UAE Minister of Education, Ahmad bin Abdullah Humaid Belhoul Al Falasi , Hadi Partovi , Founder and CEO, Code.org, and Jeffrey Tarr , CEO of Skillsoft to explore how we can adapt and adjust to take advantage.

Partovi said when people think about job losses due to AI, the risk isn't people losing their job to AI.

"It's losing their job to somebody else who knows how to use AI. That is going to be a much greater displacement. It's not that the worker gets replaced by just a robot or a machine in most cases, especially for desk jobs, it's that some better educated or more modernly educated worker can do that job because they can be twice as productive or three times as productive."

The imperative is to teach how AI tools work to every citizen, and especially to our young people.

Will copyright law enable or inhibit generative AI?

Gen AI: Boon or Bane for Creativity?

Generative AI presents a future where creativity and technology are more closely linked than ever before.

Neal Mohan , Chief Executive Officer of YouTube, joined Daren Tang , Director-General, World Intellectual Property Organization (WIPO), Almar Latour , CEO; Publisher, Wall Street Journal, Dow Jones & Company, and Contemporary Artist, Krista Kim , to explore whether prompts should be copyrighted and how we distinguish what is made by humans from machines.

We need to bring all these actors together to talk and share best practice. We will need some sort of interoperability - that's where the world is heading.

Technology in a Turbulent World

Davos 2024: Sam Altman on the future of AI

As technology plays an ever bigger role in our daily lives, questions of safety, trust and human interaction become increasingly important.

In a key and highly anticipated Davos session, OpenAI CEO Sam Altman joined Marc Benioff , Chair and CEO of Salesforce, Julie Sweet , Chair and CEO of Accenture, Jeremy Hunt , UK Chancellor of the Exchequer and Albert Bourla , CEO of Pfizer, to discuss these issues.

Hard Power of AI

From diplomacy to defence, AI is markedly changing geopolitics. Shifts in data ownership and infrastructure will transform some stakeholders while elevating others, reshaping sovereignty and influence.

Leo Varadkar , Taoiseach of Ireland, Dmytro Kuleba , Ukraine's Minister of Foreign Affairs, Karoline Edtstadler , Austria's Federal Minister for the EU and Constitution, Nick Clegg , President of Global Affairs at Meta Platforms and Mustafa Suleyman , Co-Founder and CEO, Inflection AI, explore how the landscape is evolving and what it means for the existing international architecture.

Clegg highlighted the importance of the political, societal and ethical debate happening "in parallel" as the technology is evolving.

Varadkar said AI had huge potential benefits for the future.

As a technology, I think it is going to be transformative. I think it's going to change our world as much as the internet has - and maybe even the printing press.

Reports to read on AI and technology

Global Cybersecurity Outlook 2024

The latest Global Cybersecurity Outlook warns about the threat to cyber resilient from emerging technologies, such as generative AI.

Global Lighthouse Network: Adopting AI at Speed and Scale

This whitepaper explores the impact of machine learning on manufacturing through the lens of the Global Lighthouse Network’s 153 Lighthouses.

Jobs of Tomorrow: Large Language Models and Jobs – A Business Toolkit

How can businesses respond to the changes brought about by large language models on jobs? This white paper, produced in collaboration with Accenture, offers a toolkit for businesses to help their workforces reskill, adapt and take advantage of the potential of the technology.

AI Governance Alliance: Briefing Paper Series

Views from the Manufacturing Front Line: Workers’ Insights on How to Introduce New Technology

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Controlled diffusion model can change material properties in images

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Three icons of a hand holding a wand transform three images into new pictures. In one, a Baby Yoda toy becomes transparent; in another, a brown purse becomes rougher in texture; and in the last, a goldfish turns white.

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Researchers from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Google Research may have just performed digital sorcery — in the form of a diffusion model that can change the material properties of objects in images. Dubbed Alchemist , the system allows users to alter four attributes of both real and AI-generated pictures: roughness, metallicity, albedo (an object’s initial base color), and transparency. As an image-to-image diffusion model, one can input any photo and then adjust each property within a continuous scale of -1 to 1 to create a new visual. These photo editing capabilities could potentially extend to improving the models in video games, expanding the capabilities of AI in visual effects, and enriching robotic training data.

The magic behind Alchemist starts with a denoising diffusion model: In practice, researchers used Stable Diffusion 1.5, which is a text-to-image model lauded for its photorealistic results and editing capabilities. Previous work built on the popular model to enable users to make higher-level changes, like swapping objects or altering the depth of images. In contrast, CSAIL and Google Research’s method applies this model to focus on low-level attributes, revising the finer details of an object’s material properties with a unique, slider-based interface that outperforms its counterparts. While prior diffusion systems could pull a proverbial rabbit out of a hat for an image, Alchemist could transform that same animal to look translucent. The system could also make a rubber duck appear metallic, remove the golden hue from a goldfish, and shine an old shoe. Programs like Photoshop have similar capabilities, but this model can change material properties in a more straightforward way. For instance, modifying the metallic look of a photo requires several steps in the widely used application.

“When you look at an image you’ve created, often the result is not exactly what you have in mind,” says Prafull Sharma, MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead author on a new paper describing the work. “You want to control the picture while editing it, but the existing controls in image editors are not able to change the materials. With Alchemist, we capitalize on the photorealism of outputs from text-to-image models and tease out a slider control that allows us to modify a specific property after the initial picture is provided.”

Precise control

“Text-to-image generative models have empowered everyday users to generate images as effortlessly as writing a sentence. However, controlling these models can be challenging,” says Carnegie Mellon University Assistant Professor Jun-Yan Zhu, who was not involved in the paper. “While generating a vase is simple, synthesizing a vase with specific material properties such as transparency and roughness requires users to spend hours trying different text prompts and random seeds. This can be frustrating, especially for professional users who require precision in their work. Alchemist presents a practical solution to this challenge by enabling precise control over the materials of an input image while harnessing the data-driven priors of large-scale diffusion models, inspiring future works to seamlessly incorporate generative models into the existing interfaces of commonly used content creation software.”

Alchemist’s design capabilities could help tweak the appearance of different models in video games. Applying such a diffusion model in this domain could help creators speed up their design process, refining textures to fit the gameplay of a level. Moreover, Sharma and his team’s project could assist with altering graphic design elements, videos, and movie effects to enhance photorealism and achieve the desired material appearance with precision.

The method could also refine robotic training data for tasks like manipulation. By introducing the machines to more textures, they can better understand the diverse items they’ll grasp in the real world. Alchemist can even potentially help with image classification, analyzing where a neural network fails to recognize the material changes of an image.

Sharma and his team’s work exceeded similar models at faithfully editing only the requested object of interest. For example, when a user prompted different models to tweak a dolphin to max transparency, only Alchemist achieved this feat while leaving the ocean backdrop unedited. When the researchers trained comparable diffusion model InstructPix2Pix on the same data as their method for comparison, they found that Alchemist achieved superior accuracy scores. Likewise, a user study revealed that the MIT model was preferred and seen as more photorealistic than its counterpart.

Keeping it real with synthetic data

According to the researchers, collecting real data was impractical. Instead, they trained their model on a synthetic dataset, randomly editing the material attributes of 1,200 materials applied to 100 publicly available, unique 3D objects in Blender, a popular computer graphics design tool. “The control of generative AI image synthesis has so far been constrained by what text can describe,” says Frédo Durand, the Amar Bose Professor of Computing in the MIT Department of Electrical Engineering and Computer Science (EECS) and CSAIL member, who is a senior author on the paper. “This work opens new and finer-grain control for visual attributes inherited from decades of computer-graphics research.” "Alchemist is the kind of technique that's needed to make machine learning and diffusion models practical and useful to the CGI community and graphic designers,” adds Google Research senior software engineer and co-author Mark Matthews. “Without it, you're stuck with this kind of uncontrollable stochasticity. It's maybe fun for a while, but at some point, you need to get real work done and have it obey a creative vision."

Sharma’s latest project comes a year after he led research on Materialistic , a machine-learning method that can identify similar materials in an image. This previous work demonstrated how AI models can refine their material understanding skills, and like Alchemist, was fine-tuned on a synthetic dataset of 3D models from Blender.

Still, Alchemist has a few limitations at the moment. The model struggles to correctly infer illumination, so it occasionally fails to follow a user’s input. Sharma notes that this method sometimes generates physically implausible transparencies, too. Picture a hand partially inside a cereal box, for example — at Alchemist’s maximum setting for this attribute, you’d see a clear container without the fingers reaching in. The researchers would like to expand on how such a model could improve 3D assets for graphics at scene level. Also, Alchemist could help infer material properties from images. According to Sharma, this type of work could unlock links between objects' visual and mechanical traits in the future.

MIT EECS professor and CSAIL member William T. Freeman is also a senior author, joining Varun Jampani, and Google Research scientists Yuanzhen Li PhD ’09, Xuhui Jia, and Dmitry Lagun. The work was supported, in part, by a National Science Foundation grant and gifts from Google and Amazon. The group’s work will be highlighted at CVPR in June.

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Technique enables real-time rendering of scenes in 3D

Based on shadows that an out-of-view video casts on nearby objects, MIT researchers can estimate the contents of the unseen video. In the top row, researchers used this method to recreate visual elements in an out-of-view video; the original elements are shown in the bottom row.

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AI Is Making Economists Rethink the Story of Automation

Walter Frick

Economists have traditionally believed that new technology lifts all boats. But in the case of AI, some are asking: Will some employees get left behind?

Will artificial intelligence take our jobs? As AI raises new fears about a jobless future, it’s helpful to consider how economists’ understanding of technology and labor has evolved. For decades, economists were relatively optimistic, and pointed out that previous waves of technology had not led to mass unemployment. But as income inequality rose in much of the world, they began to revise their theories. Newer models of technology’s affects on the labor market account for the fact that it absolutely can displace workers and lower wages. In the long run, technology does tend to raise living standards. But how soon and how broadly? That depends on two factors: Whether technologies create new jobs for people to do and whether workers have a voice in technology’s deployment.

Is artificial intelligence about to put vast numbers of people out of a job? Most economists would argue the answer is no: If technology permanently puts people out of work then why, after centuries of new technologies, are there still so many jobs left ? New technologies, they claim, make the economy more productive and allow people to enter new fields — like the shift from agriculture to manufacturing. For that reason, economists have historically shared a general view that whatever upheaval might be caused by technological change, it is “somewhere between benign and benevolent.”

Walter Frick is a contributing editor at Harvard Business Review , where he was formerly a senior editor and deputy editor of HBR.org. He is the founder of Nonrival , a newsletter where readers make crowdsourced predictions about economics and business. He has been an executive editor at Quartz as well as a Knight Visiting Fellow at Harvard’s Nieman Foundation for Journalism and an Assembly Fellow at Harvard’s Berkman Klein Center for Internet & Society. He has also written for The Atlantic , MIT Technology Review , The Boston Globe , and the BBC, among other publications.

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How Should Medicare Pay for Artificial Intelligence?

1 University of Chicago Booth School of Business, Chicago, Illinois
2 Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
3 Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis

Artificial intelligence (AI) has the potential to transform many aspects of the health care system. 1 , 2 One manifestation of this has been the rapid introduction of AI-enabled clinical services, which integrate AI at the point of care to help clinicians provide diagnoses for, treat, or triage patients. More than 500 such services have been approved by the US Food and Drug Administration, 3 and payers have begun reimbursing clinicians for their use. However, the existing fee-for-service payment system is not well suited for these services, and payers must balance a desire for innovation and diffusion of high-value AI-enabled clinical services with a concern about their association with spending and potential overuse.

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