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If you haven't had time to read the AI Index Report for 2021 , which clocks in at 222 pages, don't worry—we've got you covered. The massive document, produced by the Stanford Institute for Human-Centered Artificial Intelligence , is packed full of data and graphs, and we've plucked out 15 that provide a snapshot of the current state of AI.

Deeply interested readers can dive into the report to learn more; it contains chapters on R&D, technical performance, the economy, AI education, ethical challenges of AI applications, diversity in AI, and AI policy and national strategies.

1. We’re Living in an AI Summer

AI research is booming: More than 120,000 peer-reviewed AI papers were published in 2019. The report also notes that between 2000 and 2019, AI papers went from being 0.8 percent of all peer-reviewed papers to 3.8 percent in 2019.

2. China Takes Top Citation Honors

It's old news that Chinese researchers are publishing the most peer-reviewed papers on AI—China took that lead in 2017. The news this year is that, as of 2020, papers by Chinese researchers that were published in AI journals are receiving the largest share of citations.

Jack Clark , codirector of the AI Index Steering Committee, tells IEEE Spectrum that the data seems like "an indicator of academic success" for China, and is also a reflection of different AI ecosystems in different countries. "China has a stated policy of getting journal publications," he notes, and government agencies play a larger role in research, whereas in the United States, a good portion of R&D happens within corporations. "If you're an industry, you have less incentive to do journal articles," he says. "It's more of a prestige thing."

3. Faster Training = Better AI

This data comes from MLPerf , an effort to objectively rank the performance of machine learning systems. Image classifier systems from a variety of companies were trained on the standard ImageNet database, and ranked on the amount of time it took to train them. In 2018, it took 6.2 minutes to train the best system; in 2020 it took 47 seconds. This extraordinary improvement was enabled by the adoption of accelerator chips that are specifically designed for machine learning.

The report states the impact of this speedup: "Imagine the difference between waiting a few seconds for a system to train versus waiting a few hours, and what that difference means for the type and volume of ideas researchers explore and how risky they might be."

4. AI Doesn’t Understand Coffee Drinking

In the past years, AI has gotten really, really good at static image recognition; the next frontier in computer vision is video. Researchers are building systems that can recognize various activities from video clips, since that type of recognition could be broadly useful if ported over to the real world (think about self-driving cars, surveillance cameras, and so forth). One benchmark of performance is the ActivityNet data set, which contains nearly 650 hours of footage from a total of 20,000 videos. Of the 200 activities of daily life shown therein, AI systems had the toughest time recognizing the activity of coffee drinking in both 2019 and 2020. This seems like a major problem, since coffee drinking is the fundamental activity from which all other activities flow. Anyway, this is an area to watch over the coming years.

5. Language AI Is So Good, It Needs Harder Tests

The meteoric rise of natural language processing (NLP) seems to be following the trajectory of computer vision, which went from an academic subspecialty to widespread commercial deployment over the past decade. Today's NLP is also powered by deep learning, and Clark of the AI Index says it has inherited strategies from computer vision work, such as training on huge databases and fine-tuning for specific applications. "We're seeing these innovations flow through to another area of AI really quickly," he says.

Measuring the performance of NLP systems has become tricky: "Academics are coming up with metrics they think no one can beat, then a system comes along in six months and beats it," Clark says. This chart shows performance on two versions of a reading comprehension test called SQuAD , in which an AI language model has to answer multiple choice questions based on a paragraph of text. Version 2.0 made the task harder by incorporating unanswerable questions, which the model had to identify as such and abstain from answering. It took 25 months for a model to surpass human performance on the first version, but just 10 months for one to beat humans at the harder task.

6. A Huge Caveat

Yes, language models for tasks like speech recognition and text generation have gotten really good in general. But they have some specific failings that could derail commercial use unless addressed. Many have serious problems with harmful bias, such as performing poorly on a subset of people or generating text that reflects historical prejudice. The example here shows error rates in speech-recognition programs from leading companies.

There's a larger issue with bias here that bedevils all forms of AI, including computer vision and decision-support tools. Researchers test their systems for performance, but few test their systems for harmful bias.

7. The AI Job Market Is Global

Data from LinkedIn shows that Brazil, India, Canada, Singapore, and South Africa had the highest growth in AI hiring from 2016 to 2020. That doesn't mean those countries have the most jobs in absolute terms (the United States and China continue to hold the top spots there), but it will be interesting to see what emerges from those countries pushing hard on AI. LinkedIn found that the global pandemic did not put a dent in AI hiring in 2020.

It's worth noting that a smaller percentage of the workforce in both India and China have profiles on LinkedIn, so data from those countries may not be fully representative.

8. Corporate Investment Can’t Stop, Won’t Stop

The money continues to pour in. Global corporate investment in AI soared to nearly $68 billion in 2020, an increase of 40 percent over the year before.

9. The Startup Frenzy Is Over

The previous graph showed that private investment is still increasing year over year, but at a slower pace. This graph shows that the money is being channeled into fewer AI startups. While the pandemic may have had an impact on startup activity, this decline in the number of startups is a clear trend that began in 2018. It seems to be a signal of a maturing industry.

10. The COVID Effect

While many trends in AI were largely unaffected by the global pandemic, this chart shows that private investment in 2020 skewed toward certain sectors that have played big roles in the world's response to COVID-19. The boom in investment from pharma-related companies is the most obvious, but it also seems possible that the increased funding for edtech and gaming has something to do with the fact that students and adults alike have spent a lot of the last year in front of their computers.

11. Risks? There are Risks?

Corporations are steadily increasing their adoption of AI tools in such industries as telecom, financial services, and automotive. Yet most companies seem unaware or unconcerned about the risks accompanying this new technology. When asked in a McKinsey survey what risks they considered relevant, only cybersecurity had registered with more than half of respondents. Ethical concerns related to AI, such as privacy and fairness, are one of the hottest topics in AI research today, but apparently business hasn't yet gotten the memo.

12. Ph.D.’s Hear the Siren Call of Industry

To be fair, there are only so many academic jobs. While universities have increased the number of AI-related courses on both the undergraduate and graduate level, and the number of tenure-track faculty jobs has increased accordingly, academia still can't absorb the growing number of fresh AI Ph.D.'s released into the world each year. This chart, which only represents Ph.D. graduates in North America, shows that the large majority of those graduates are getting industry jobs.

13. Ethics Matter

Corporations may not care about AI ethics yet, but researchers increasingly do. Many groups are working on issues such as opaque decision making by AI systems (called the explainability problem), embedded bias and discrimination, and privacy intrusion. The chart above shows the rise in ethics-related papers at AI conferences, which the AI Index's Clark sees as an encouraging sign. Since so many students take part in conferences, he notes, "in a few years, there will be a load of people going into industry that have come up in this milieu."

Beyond the increase in conference papers, however, there's not much to measure. The report stresses that quantitative tests of bias in AI systems are only beginning to emerge. Creating these evaluations "feels like a new part of the AI scientific field," Clark says.

14. The Diversity Problem, Part 1

One way to work on embedded bias and discrimination in AI systems is to ensure diversity in the groups that are building them. This is hardly a radical notion. Yet in both academia and industry, the AI workforce "remains predominantly male and lacking in diversity," the report states. This graph, with data from the Computer Research Association's annual survey , shows that women make up only about 20 percent of graduates from AI-related Ph.D. programs in North America.

15. The Diversity Problem, Part 2

Data from that same survey tells a similar story about race/ethnic identity. What's to be done? Well, given that the problem seems quite apparent at the level of graduating Ph.D.s, it probably makes sense to look further up the pipeline. There are any number of excellent STEM programs that focus on girls and underrepresented minorities. AI4ALL comes to mind. Maybe sling them a few dollars, or get involved in some way?

• 12 Graphs That Explain the State of AI in 2022 ›
• 10 Graphs That Sum Up the State of AI in 2023 - IEEE Spectrum ›

Eliza Strickland is a senior editor at IEEE Spectrum , where she covers AI, biomedical engineering, and other topics. She holds a master’s degree in journalism from Columbia University.

Language is so good it needs a harder test? Put the text of the Bible into AI and let it answer questions to see if it can make sense of what is often poorly written and often seems contradictory.

Thank you, Author! For caring about the article information quality.

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Artificial Intelligence in CyberSecurity

Submission Deadline: 30 July 2019

IEEE Access invites manuscript submissions in the area of Artificial Intelligence in CyberSecurity.

Recent studies show that Artificial Intelligence (AI) has resulted in advances in many scientific and technological fields, i.e., AI-based medicine, AI-based transportation, and AI-based finance. It can be imagined that the era of AI will be coming to us soon. The Internet has become the largest man-made system in human history, which has a great impact on people’s daily life and work. Security is one of the most significant concerns in the development of a sustainable, resilient and prosperous Internet ecosystem. Cyber security faces many challenging issues, such as intrusion detection, privacy protection, proactive defense, anomalous behaviors, advanced threat detection and so on. What’s more, many threat variations emerge and spread continuously. Therefore, AI-assisted, self-adaptable approaches are expected to deal with these security issues. Joint consideration of the interweaving nature between AI and cyber security is a key factor for driving future secure Internet.

The use of AI in cybersecurity creates new frontiers for security research. Specifically, the AI analytic tools, i.e., reinforcement learning, big data, machine learning and game theory, make learning increasingly important for real-time analysis and decision making for quick reactions to security attacks. On the other hand, AI technology itself also brings some security issues that need to be solved. For example, data mining and machine learning create a wealth of privacy issues due to the abundance and accessibility of data. AI-based cyber security has a great impact on different industrial applications if applied in appropriate ways, such as self-driving security, secure vehicular networks, industrial control security, smart grid security, etc. This Special Section in IEEE Access will focus on AI technologies in cybersecurity and related issues. We also welcome research on AI-related theory analysis for security and privacy.

The topics of interest include, but are not limited to:

• Reinforcement learning for cybersecurity
• Machine learning for proactive defense
• Big data analytics for security
• Big data anonymization
• Big data-based hacking incident forecasting
• Big data analytics for secure network management
• AI-based intrusion detection and prevention
• AI approaches to trust and reputation
• AI-based anomalous behavior detection
• AI-based privacy protection
• AI for self-driving security
• AI for IoT security
• AI for industrial control security
• AI for smart grid security
• AI for security in innovative networking
• AI security applications

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor:    Chi-Yuan Chen, National Ilan University, Taiwan

Guest Editors:

• Wei Quan, Beijing Jiaotong University, China
• Nan Cheng, University of Toronto, Canada
• Shui Yu, Deakin University, Australia
• Jong-Hyouk Lee, Sangmyung University, Republic of Korea
• Gregorio Martinez Perez, University of Murcia (UMU), Spain
• Hongke Zhang, Beijing Jiaotong University, China
• Shiuhpyng Shieh, National Chiao Tung University, Taiwan

Relevant IEEE Access Special Sections:

• Artificial Intelligence and Cognitive Computing for Communications and Networks
• Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
• Cyber-Physical Systems

IEEE Access Editor-in-Chief:   Prof. Derek Abbott, University of Adelaide

Paper submission: Contact Associate Editor and submit manuscript to: http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  [email protected] .

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Computer Science > Robotics

Title: embodied neuromorphic artificial intelligence for robotics: perspectives, challenges, and research development stack.

Abstract: Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environments, respond with correct/expected actions, and adapt to changes in the environments. Toward this, recent advances in neuromorphic computing with Spiking Neural Networks (SNN) have demonstrated the potential to enable the embodied intelligence for robotics through bio-plausible computing paradigm that mimics how the biological brain works, known as "neuromorphic artificial intelligence (AI)". However, the field of neuromorphic AI-based robotics is still at an early stage, therefore its development and deployment for solving real-world problems expose new challenges in different design aspects, such as accuracy, adaptability, efficiency, reliability, and security. To address these challenges, this paper will discuss how we can enable embodied neuromorphic AI for robotic systems through our perspectives: (P1) Embodied intelligence based on effective learning rule, training mechanism, and adaptability; (P2) Cross-layer optimizations for energy-efficient neuromorphic computing; (P3) Representative and fair benchmarks; (P4) Low-cost reliability and safety enhancements; (P5) Security and privacy for neuromorphic computing; and (P6) A synergistic development for energy-efficient and robust neuromorphic-based robotics. Furthermore, this paper identifies research challenges and opportunities, as well as elaborates our vision for future research development toward embodied neuromorphic AI for robotics.

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