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Research Paper Generator by AcademicHelp

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Research Paper Generator for Finishing Your Papers

Writing a research paper is going to be the peak of your academic path. This work will show the knowledge and skills you gained throughout your studies. Undoubtedly, this will be one of the most serious steps you will take towards your future development and career goals. 

All of this puts a lot of pressure on you as a student. You might feel lost and therefore scared of starting to write this work. If this is you, then you are going to like the idea of using our Free Research Paper Writer.

Get Instant Help With Research Paper Title Generator

Don’t worry if you don’t know where to start your research paper. We have all been there – sitting in front of a blank document unable to even come up with the proper topic for our work. Fortunately, now you don’t have to struggle for days to come up with ideas. Just open the AI Research Paper Writer and jump-start the work process with its assistance. 

All you have to do is just state the subject of your writing and at least an approximate topic you may be interested in researching. In the special field for instructions, you can ask the tool to help you create the title and introduction to your paper. And just after 2 minutes, you will have the beginning on which you can build your article further. 

When You Can Turn to AI Research Paper Generator

Our Research paper Generator will prove beneficial to those who haven’t yet started their research. Yet, this is not the only case when you can use this service.

It can be a helpful addition to your academic toolkit even if you have already begun your writing. With its assistance, students can research new aspects of their topic and, as such, make their work more thorough. You can also use it to add new or organize the already existing parts of your paper. Just give it the paragraphs you already have and you will get an example of how they can be properly structured and edited with more details. 

In any case, you can use this Research Paper Generator for any part of your paper. No matter whether it is an introduction, the main body of the text, or the conclusion to all the research you’ve done – you will be able to get some valuable insight into how you can improve your writing for the better.

Prompt Ideas for Research Paper Thesis Generator

The key to effective work with AI text generators lies in prompting. Proper prompt structuring, however, takes time and practice, which we think, you don’t have much time for. And even if you do, giving you a few ideas on how good prompts would sound wouldn’t hurt, would it? 

So, we present to you our collection of possible prompts you can use to make our AI Research Paper Writer give you the best thesis statements:

• “Research Topic: The effects of climate change on agricultural productivity in the Midwest.
• Research Question: How does climate change impact agricultural productivity in the Midwest region?
• Key Concepts: Climate change, agricultural productivity, Midwest region.
• Relationship: Investigate the impact of climate change variables on crop yields.
• Research Method: Analyzing historical climate data and crop yield records.
• Significance: Understanding the implications of climate change for food security in the region.

Analyze the following details for my research paper and based on the information you learned provide a thesis statement for the said research paper”

“Examine the relationship between regular physical exercise and stress reduction in working adults. Focus on how different exercise routines impact stress levels. Use a comparative analysis of various exercise programs among a sample of working adults aged 25-45. Highlight the importance of this research for promoting workplace wellness programs.”

You should put in the relevant details regarding your paper in the said prompts. Note though, that these are just our suggestions. You are free to experiment and create your own prompts based on these recommendations. 

Where can I get a research paper written?

It’s a better idea to write your research paper yourself. However, you can use some outside help, for example, from online writing services, hiring a freelance writer, or seeking assistance from academic writing centers at universities. You can also use AI-powered platforms such as Academichelp Research Paper Generator, to help you gain and organize ideas in your chosen topic.

Who writes a research paper?

A research paper is typically written by students, researchers, or academics who want to present their findings or arguments on a specific topic. Professional writers or academic writing services can also be hired to write research papers, especially if the person who initially needs the paper lacks the time or skills to write it themselves. However, now, we also have various AI tools that can quickly help with that problem as well.

How much does it cost to have someone write a paper for you?

The cost for a research paper depends on the service you use to write it as well as the complexity of the topic, length, deadline, and the expertise of the writer. Prices can range from $10 to $50 per page for undergraduate-level writing. For more specialized or advanced writing, such as graduate-level or technical research papers, the cost can be higher. We advise you to get quotes from multiple services or writers to find a price that fits your budget.

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How to Write a Research Paper (+ Free AI Research Paper Writer)

Table of contents

Meredith Sell

Over the years, I've managed to vastly improve how I write research papers.

The three major game-changers for me, in terms of quality of the finished piece, have been:

• Following the research paper checklist (see below)
• Developing the thesis before starting to write
• And, more recently, using AI to improve my research paper draft

Let's break down each of these elements and produce the kind of research papers that get cited in magazines.

FREE AI research paper writer > FREE AI research paper writer >

What is a research paper, and how is it written differently?

Research papers are longer and more in-depth than essays. They require extensive research and evidence-based arguments. Research papers also typically have a more formal structure and require citations and references.When academics want to find a balanced and comprehensive view on a given topic, they usually seek a research paper.

Like most writing assignments, a research paper can be broken down into simple steps. Research papers follow the same basic writing process as explanatory or persuasive essays — but instead of making an argument or drawing greater meaning from the topic, the research paper is primarily concerned with concrete facts that may be analyzed, examined, or interpreted to better understand the paper’s central topic.

This is good news if you enjoy research: you’ll be doing a lot of it. The ultimate quality of your paper depends on you conducting thorough, complete research — and relying on reputable sources.

How to Properly Write a Research Paper Using AI

1. make a checklist based on the assignment description, and fill it out with ai.

Your professor has likely specified some criteria for your research paper:

• Length (in pages or words)
• Type of topic (the War of 1812, ancient Greece, agriculture, etc.)
• Elements that must be included, such as analysis, discussion, and comparison.
• Types of sources you must draw from (academic papers, encyclopedias, etc.)
• Source attribution style
• Formatting style

Go through the assignment description and create a checklist of those criteria. You can use this checklist throughout the research and writing process as well:

AI can really help you get some traction with your research paper in the preperation stage. This includes two main steps:

• Brainstorming paper topic idea
• Outlining based on your topic, basing the prompt on the assignment

2. Choose a topic you’re curious about, or use AI to help you with that

A sure way to write a boring research paper is to pick a topic you have no interest in, like summer temperatures in the desert or the life cycle of a flea. (Though someone’s probably interested in those things.)

Instead, follow your curiosity.

If your paper is for a writing class, you may have a lot of freedom to choose what you write about, so tap into your interests. Are you intrigued by the history of roller skating or the invention of the soccer cleat? Or how teen social dynamics have changed with evolving technology (think: home phones → online instant messaging → flip phones → smartphones)?

If you’re writing for a class in a subject like history, art, or science, you’ll probably have more restrictions on what you can write about — like a time period or type of art or science — but you can still use your curiosity to pick an interesting topic.

If you’re having a tough time, try brainstorming a list of things you’ve wondered about. Ask “ what’s up with… ” and see what comes to mind.

For example:

What’s up with traffic circles and why are they supposedly better for traffic patterns than a light or four-way stop?

What’s up with country music sounding more and more like hip-hop?

What’s up with people who have gluten allergies being able to eat bread in Europe but not the US?

Once you have a list, choose the topic you find most interesting (and appropriate for the assignment).

If your mind draws a blank, you can utilize AI to help you choose a topic. Let's say your course is about mid century art. You can go to a tool like Wotdtune and ask it to give you ideas for creative mid century art essays. See example below.

3. Develop your thesis (and guide your research) by asking a research question

Even though a research paper may not necessarily take a side on a topic, it still needs a thesis, aka a central idea or focus that drives the piece from beginning to end. 

We wrote a whole guide on writing thesis statements , so here we’ll just give you this tip:

Use a research question to develop your thesis

A research question is a variation on the “What’s up with…” questions from the last tip — but it will zoom in more specifically on the aspect of your topic that you’re investigating.

Why were the Irish so dependent on potatoes?

Did any women in ancient Greece enjoy relative freedom and autonomy?

You may already know the answer to these questions, or you may not. Either way, they give you a place to start in your research. Once you have your question, set out to:

• Find the initial answer.
• Gather more context (the who, what, when, where, why, how) around that answer.
• Revise your research question and turn it into your thesis.

This process helps tighten your focus from a broad topic that could fill books to a specific angle that can be meaningfully explored in the few pages of your paper.

Instead of the potato famine , write about why England was to blame for the potato famine’s devastating effects on the Irish.

Instead of ancient Greece or women in ancient Greece , write about how Spartan women’s lives differed from the lives of women in Athens.

4. Skim sources and use AI to perform research for your paper

Your research question can help you quickly determine whether information is relevant to your paper. As you gather initial sources, skim them — and then use your research question to decide whether to keep or discard the source. 

Does the source cover information relevant to my research question?

Yes: Keep to read later.

No: Discard and move on to the next source.

This approach will save you precious research time. You won’t waste limited hours reading sources that don’t have a single helpful fact.

If skimming is hard for you (as a deep reader, I get it), Wordtune can help. Paste the link to your online source, upload a scanned PDF, or copy the text, and the tool will scan and summarize for you. You can always come back later and closely read the most useful sources.

5. Make note of the most interesting facts you find

Along with taking detailed notes of your research (complete with all the source info you need to make proper citations), highlight the most interesting facts you come across. You could stick these in a section together or mark them in a way that makes them stand out.

Why should you do this?

Because later on, one of these fascinating factoids could have a direct connection to your thesis — and make a great hook for the start of your paper. Instead of digging through all of your notes to try to remember what that interesting tidbit was, you’ll be able to find it easily.

6. Organize your research

There are plenty of ways to organize your notes, but I suggest breaking them up into subtopics and categories.

• Subtopic: A topic related to your main topic or thesis that needs to be explained and understood by readers in order to understand your main topic or thesis. For example: Land ownership in Ireland under British rule.
• Category: An overarching concept that several subtopics fall under. For example: British restrictions on the Irish.

To start, I would focus on the subtopics and then group them into categories.

As you organize, use the formatting tools in your word processor to tag headings and subheadings. For example, all categories would be an H2 (Heading 2), while all subtopics would be an H3 (Heading 3). 

Tagging your categories and subtopics this way will help you develop your outline. Just organize your categories and subtopics in a logical order, and you’ll have a skeleton of an outline ready to go.

7. Write with your research document open

No one can remember everything they found while researching — you’ll need to reference your research document throughout the writing process. No question there.

But you can make this easier (and keep your writing process efficient) by:

Keeping your research document open and in clear view.

I like to put my draft document and my research document side by side on my screen, so I can see them both at the same time. 

Another approach would be to paste the information you need directly into your draft document — in the order you’ll need it. (Your outline will help you know what you need.)

8. Steal the TK trick from journalists

In the middle of drafting your paper, you find that you’re missing a fact. 

You neglected to write down how many Irish people starved due to the potato famine.

You don’t know what age Spartan women were able to own property.

Instead of derailing your writing and searching for that information, write the sentence you want to write and stick a “TK” where the missing fact should go.

“TK” stands for “to come” (don't ask us why) and is a placeholder used by journalists to mark missing information they’ll fill in later. Using TK allows you to keep writing without getting off track every time you discover your research didn’t cover everything.

A whopping TK Irish people starved, thanks to the combination of famine and British oppression.

At age TK , Spartan girls became women who were able to own property, a right that their sisters in Athens did not enjoy.

9. Revise, explain, paraphrase with AI as your research/writing assistant

Using the right researching tools can get you a lot way.

If you’re ever at a loss for words — writing clunky, clumsy sentences, struggling to explain a concept, or having a hard time paraphrasing a source — Wordtune can serve as your AI sidekick.  

Simply highlight the sentence in question and browse Wordtune’s suggestion for a better wording.

You can also use Wordtune Spices to come up with examples and counter arguments for whatever you're writing about or even find stats and facts, complete with source citations

Wordtune doesn’t do all of the writing for you, but it can help you sharpen your ideas on the sentence level, so you can hand in a research paper with good writing that’s still very much your own.

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The academic writing revolution is here; with the emergence of new research paper writing tools, content creation has never been easier. Whether your objective is tenure, a research grant, a book deal, or just an improved reputation in your department, a string of high-profile journal articles are the building blocks you need to fast-track your academic career. As a researcher, what better way of communicating your research than by showcasing it in a well-written article that is published in a top journal? However, the process of journal submission and multiple research paper grammar checks to ensure the language is of the highest quality isn’t without its challenges. A global Editage study in 2018 found that nearly half of the authors surveyed faced challenges in preparing a manuscript for journal submission and found peer review to be a daunting process. Paperpal, with its AI-powered tools for researchers, is the key to making this process a simpler, faster one for authors everywhere.

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There’s more to academic writing than simply knowing what to write. But delivering a strong, well-written manuscript is not easy and even ground-breaking research papers risk desk rejection because they are hard to follow or because of avoidable errors like poor grammar, spelling, and punctuation. By the time you finish the first draft, you would have already invested a significant amount of time and effort in communicating your findings accurately. This is then followed by multiple rounds of editing and research paper grammar checks to refine your work for submission, which can delay the publication of possibly time-sensitive results. Although academic writing isn’t easy, scientific writing tools for researchers powered by artificial intelligence and machine learning are transforming the experience. Paperpal for Word for instance provides real-time suggestions to improve your grammar, spelling, punctuation, clarity and structure, empowering you to enhance and speed up the writing process from the first draft itself.

Simplifies and optimizes scientific editing for researchers

It’s not just academic writing, but high-quality English language editing plays a key role in improving your writing style and ensuring clarity. Finding an online tool that acts as a basic spelling, grammar, and sentence corrector is straightforward, but finding thesis writing and scientific editing tools that are tailored for researchers and academic writing is not so easy. There is a need for precise scientific editing, which includes accurate research paper grammar checks, using the right language, understanding and applying the necessary academic writing conventions, and getting the style and structure right. This is where Paperpal can help. In a 2022 University of Cambridge study of seven English editing tools for researchers, Paperpal stood out as the preferred English editing and scientific writing tool for researchers. It suggested a high number of accepted edits, providing alternative words and phrases that were in line with those made by human editors to enhance the language and readability of text. Paperpal was also the simpler tool for researchers to use for editing and research paper grammar checks.

Streamlines journal submissions with comprehensive checks

Imagine if after all the work you put in preparing your manuscript for submission, you’re rejected because it fails the basic technical checks. Being rejected for avoidable snags like a missing conflict of interest statement or ethics statement is not only disappointing, it further delays research publication. Most journals have a core set of submission requirements that need to be followed in order for a manuscript to be considered. But with so many things to check and do, researchers often stumble at this stage. This is where Paperpal for Manuscript, which checks your research paper across key language and technical parameters, is the perfect tool for researchers to check their submission readiness. Upload your ready manuscript and for just $29 you can download a Word file with all the suggestions included in mark-ups or comments. Accept or reject suggestions with a few clicks, and recheck your work unlimited times to create the best version of your manuscript. Premium editing has never been simpler.

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English has long been established as the language of academic publications. And even then, different journals prefer a specific variation, such as British or American English. While this can trip up even native English-speaking researchers, it makes the task of creating a compelling high-quality manuscript for publication even more challenging for those with English as a second language. Paperpal’s AI-powered research paper grammar check and scientific writing tools were built with a vision to democratize scholarly publishing by ensuring your academic writing meets the highest publication standards. Where you are in the world doesn’t matter. We’re part of an academic writing revolution that empowers anyone, anywhere to polish their academic writing skills with access to our precise research paper writing tools for researchers. Paperpal differentiates between British and American English and offers appropriate suggestions in terms of spellings, vocabulary, pronunciation, and grammar. Moreover, our AI is trained on millions of corrections made by professional editors across 1,300 subject areas, which means you get tailored suggestions to enhance your research writing and boost your chance of publication success.

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Research Paper Outline Generator

🤖 research outline generator 101.

• 📝 Template & Format
• 🤩 Outline Tips

🔗 References

Outlining an academic assignment serves several purposes:

• It lets students establish connections between the thesis and their ideas.
• A template also allows them to structure papers and identify gaps in their knowledge.
• An outline makes their writing more substantial and lets them know if they understand the topic well enough.

Our outline generator for research papers speeds up this process in several ways:

📝 Outline Generator for Research Paper: Template and Format

Our tool can help you cope with the first step in any academic work, whether it's a high school or college tasks. But what should you do next? This part of the article details the components of the research paper format. It will give you a better understanding of how your work should look.

Popular Academic Formats

The format of your research paper largely depends on the style your school uses. Three main styles are widely used in the academic setting: APA, Chicago, and MLA. Each has its features we’ll discuss briefly.

Good Example of a Template

In this section, we have detailed all the components of a comprehensive research paper. Knowing these elements will make it easy for you to structure your work.

🤩 Best Outline Tips: Research Papers & Academic Essays

We want to wrap things up by offering helpful tips for an effective outline. It doesn’t matter if you’re writing an academic essay, a research work, or a personal statement. These pieces of advice can be helpful in all types of assignments.

• Plan and brainstorm . Make a list of ideas that can become good topics for your essay. By the way, having study buddies is also a fantastic idea! Try to brainstorm topics with fellow students. This way, you can better plan and find impactful research subjects.
• Organize your ideas . Next, organize the ideas into separate categories. Discard bad ones and incorporate good ones in your writing. Narrow them down until you choose the main one to write a good thesis statement.
• Use bullet points . Utilize a list format to keep your outline organized. Use headings to easily navigate the template and structure the paper logically.
• Use online tools . If you need help, turn to online platforms for assistance. For example, our research paper outline generator can make this process faster.
• Write in your mother tongue . If you struggle writing in English , make notes in your native language. It’s also better to research this topic in your mother tongue. You can translate your findings later.
• Remove redundant points . Revise all the paragraphs of your finished essay template. Make sure that all the sections match the purpose of your paper and reveal the main idea. If you find too many inappropriate points, try rephrasing them to fit the topic.

We hope that our outline generator for research papers helped in your work. If you have any questions left, check out the FAQ section below. Besides we also recommend you take a look at our research guide .

❓ Research Outline Generator – FAQ

Updated: Dec 14th, 2023

• Writing a Research Paper. – Arrendale Library, Piedmont University
• How to Write an APA Research Paper – Hamilton College
• How To Write a Research Paper Outline (With Examples and Tips) – Indeed
• Chicago Style Guide – Menlo School
• MLA General Format – The On-Campus Writing Lab & The OWL at Purdue and Purdue University
• Formatting an Academic Paper – Augsburg University
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Learn everything there is to know about our awesome research paper outline generator. This instrument is guaranteed to save you lots of time and energy by creating the perfect plan for your assignment in seconds. Simply set the parameters and get to writing!

What’s Included: Research Paper Template

If you’re preparing to write an academic research paper, our free research paper template is the perfect starting point. In the template, we cover every section step by step, with clear, straightforward explanations and examples .

The template’s structure is based on the tried and trusted best-practice format for formal academic research papers. The template structure reflects the overall research process, ensuring your paper will have a smooth, logical flow from chapter to chapter.

The research paper template covers the following core sections:

• The title page/cover page
• Abstract (sometimes also called the executive summary)
• Section 1: Introduction 
• Section 2: Literature review 
• Section 3: Methodology
• Section 4: Findings /results
• Section 5: Discussion
• Section 6: Conclusion
• Reference list

Each section is explained in plain, straightforward language , followed by an overview of the key elements that you need to cover within each section. We’ve also included links to free resources to help you understand how to write each section.

The cleanly formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

FAQs: Research Paper Template

What format is the template (doc, pdf, ppt, etc.).

The research paper template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

What types of research papers can this template be used for?

The template follows the standard best-practice structure for formal academic research papers, so it is suitable for the vast majority of degrees, particularly those within the sciences.

Some universities may have some additional requirements, but these are typically minor, with the core structure remaining the same. Therefore, it’s always a good idea to double-check your university’s requirements before you finalise your structure.

Is this template for an undergrad, Masters or PhD-level research paper?

This template can be used for a research paper at any level of study. It may be slight overkill for an undergraduate-level study, but it certainly won’t be missing anything.

How long should my research paper be?

This depends entirely on your university’s specific requirements, so it’s best to check with them. We include generic word count ranges for each section within the template, but these are purely indicative. 

What about the research proposal?

If you’re still working on your research proposal, we’ve got a template for that here .

We’ve also got loads of proposal-related guides and videos over on the Grad Coach blog .

How do I write a literature review?

We have a wealth of free resources on the Grad Coach Blog that unpack how to write a literature review from scratch. You can check out the literature review section of the blog here.

How do I create a research methodology?

We have a wealth of free resources on the Grad Coach Blog that unpack research methodology, both qualitative and quantitative. You can check out the methodology section of the blog here.

Can I share this research paper template with my friends/colleagues?

Yes, you’re welcome to share this template. If you want to post about it on your blog or social media, all we ask is that you reference this page as your source.

Can Grad Coach help me with my research paper?

Within the template, you’ll find plain-language explanations of each section, which should give you a fair amount of guidance. However, you’re also welcome to consider our private coaching services .

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Using Litmaps for my research papers has significantly improved my workflow. Typically, I start with a single paper related to my topic. Whenever I find an interesting work, I add it to my search. From there, I can quickly cover my entire Related Work section.

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How to Write a Research Paper 

• Smodin Editorial Team
• Updated: May 17, 2024

Most students hate writing research papers. The process can often feel long, tedious, and sometimes outright boring. Nevertheless, these assignments are vital to a student’s academic journey. Want to learn how to write a research paper that captures the depth of the subject and maintains the reader’s interest? If so, this guide is for you.

Today, we’ll show you how to assemble a well-organized research paper to help you make the grade. You can transform any topic into a compelling research paper with a thoughtful approach to your research and a persuasive argument.

In this guide, we’ll provide seven simple but practical tips to help demystify the process and guide you on your way. We’ll also explain how AI tools can expedite the research and writing process so you can focus on critical thinking.

By the end of this article, you’ll have a clear roadmap for tackling these essays. You will also learn how to tackle them quickly and efficiently. With time and dedication, you’ll soon master the art of research paper writing.

Ready to get started?

What Is a Research Paper?

A research paper is a comprehensive essay that gives a detailed analysis, interpretation, or argument based on your own independent research. In higher-level academic settings, it goes beyond a simple summarization and includes a deep inquiry into the topic or topics.

The term “research paper” is a broad term that can be applied to many different forms of academic writing. The goal is to combine your thoughts with the findings from peer-reviewed scholarly literature.

By the time your essay is done, you should have provided your reader with a new perspective or challenged existing findings. This demonstrates your mastery of the subject and contributes to ongoing scholarly debates.

7 Tips for Writing a Research Paper

Often, getting started is the most challenging part of a research paper. While the process can seem daunting, breaking it down into manageable steps can make it easier to manage. The following are seven tips for getting your ideas out of your head and onto the page.

1. Understand Your Assignment

It may sound simple, but the first step in writing a successful research paper is to read the assignment. Sit down, take a few moments of your time, and go through the instructions so you fully understand your assignment.

Misinterpreting the assignment can not only lead to a significant waste of time but also affect your grade. No matter how patient your teacher or professor may be, ignoring basic instructions is often inexcusable.

If you read the instructions and are still confused, ask for clarification before you start writing. If that’s impossible, you can use tools like Smodin’s AI chat to help. Smodin can help highlight critical requirements that you may overlook.

This initial investment ensures that all your future efforts will be focused and efficient. Remember, thinking is just as important as actually writing the essay, and it can also pave the wave for a smoother writing process.

2. Gather Research Materials

Now comes the fun part: doing the research. As you gather research materials, always use credible sources, such as academic journals or peer-reviewed papers. Only use search engines that filter for accredited sources and academic databases so you can ensure your information is reliable.

To optimize your time, you must learn to master the art of skimming. If a source seems relevant and valuable, save it and review it later. The last thing you want to do is waste time on material that won’t make it into the final paper.

To speed up the process even more, consider using Smodin’s AI summarizer . This tool can help summarize large texts, highlighting key information relevant to your topic. By systematically gathering and filing research materials early in the writing process, you build a strong foundation for your thesis.

3. Write Your Thesis

Creating a solid thesis statement is the most important thing you can do to bring structure and focus to your research paper. Your thesis should express the main point of your argument in one or two simple sentences. Remember, when you create your thesis, you’re setting the tone and direction for the entire paper.

Of course, you can’t just pull a winning thesis out of thin air. Start by brainstorming potential thesis ideas based on your preliminary research. And don’t overthink things; sometimes, the most straightforward ideas are often the best.

You want a thesis that is specific enough to be manageable within the scope of your paper but broad enough to allow for a unique discussion. Your thesis should challenge existing expectations and provide the reader with fresh insight into the topic. Use your thesis to hook the reader in the opening paragraph and keep them engaged until the very last word.

4. Write Your Outline

An outline is an often overlooked but essential tool for organizing your thoughts and structuring your paper. Many students skip the outline because it feels like doing double work, but a strong outline will save you work in the long run.

Here’s how to effectively structure your outline.

• Introduction: List your thesis statement and outline the main questions your essay will answer.
• Literature Review: Outline the key literature you plan to discuss and explain how it will relate to your thesis.
• Methodology: Explain the research methods you will use to gather and analyze the information.
• Discussion: Plan how you will interpret the results and their implications for your thesis.
• Conclusion: Summarize the content above to elucidate your thesis fully.

To further streamline this process, consider using Smodin’s Research Writer. This tool offers a feature that allows you to generate and tweak an outline to your liking based on the initial input you provide. You can adjust this outline to fit your research findings better and ensure that your paper remains well-organized and focused.

5. Write a Rough Draft

Once your outline is in place, you can begin the writing process. Remember, when you write a rough draft, it isn’t meant to be perfect. Instead, use it as a working document where you can experiment with and rearrange your arguments and evidence.

Don’t worry too much about grammar, style, or syntax as you write your rough draft. Focus on getting your ideas down on paper and flush out your thesis arguments. You can always refine and rearrange the content the next time around.

Follow the basic structure of your outline but with the freedom to explore different ways of expressing your thoughts. Smodin’s Essay Writer offers a powerful solution for those struggling with starting or structuring their drafts.

After you approve the outline, Smodin can generate an essay based on your initial inputs. This feature can help you quickly create a comprehensive draft, which you can then review and refine. You can even use the power of AI to create multiple rough drafts from which to choose.

6. Add or Subtract Supporting Evidence

Once you have a rough draft, but before you start the final revision, it’s time to do a little cleanup. In this phase, you need to review all your supporting evidence. You want to ensure that there is nothing redundant and that you haven’t overlooked any crucial details.

Many students struggle to make the required word count for an essay and resort to padding their writing with redundant statements. Instead of adding unnecessary content, focus on expanding your analysis to provide deeper insights.

A good essay, regardless of the topic or format, needs to be streamlined. It should convey clear, convincing, relevant information supporting your thesis. If you find some information doesn’t do that, consider tweaking your sources.

Include a variety of sources, including studies, data, and quotes from scholars or other experts. Remember, you’re not just strengthening your argument but demonstrating the depth of your research.

If you want comprehensive feedback on your essay without going to a writing center or pestering your professor, use Smodin. The AI Chat can look at your draft and offer suggestions for improvement.

7. Revise, Cite, and Submit

The final stages of crafting a research paper involve revision, citation, and final review. You must ensure your paper is polished, professionally presented, and plagiarism-free. Of course, integrating Smodin’s AI tools can significantly streamline this process and enhance the quality of your final submission.

Start by using Smodin’s Rewriter tool. This AI-powered feature can help rephrase and refine your draft to improve overall readability. If a specific section of your essay just “doesn’t sound right,” the AI can suggest alternative sentence structures and word choices.

Proper citation is a must for all academic papers. Thankfully, thanks to Smodin’s Research Paper app, this once tedious process is easier than ever. The AI ensures all sources are accurately cited according to the required style guide (APA, MLA, Chicago, etc.).

Plagiarism Checker:

All students need to realize that accidental plagiarism can happen. That’s why using a Plagiarism Checker to scan your essay before you submit it is always useful. Smodin’s Plagiarism Checker can highlight areas of concern so you can adjust accordingly.

Final Submission

After revising, rephrasing, and ensuring all citations are in order, use Smodin’s AI Content Detector to give your paper one last review. This tool can help you analyze your paper’s overall quality and readability so you can make any final tweaks or improvements.

Mastering Research Papers

Mastering the art of the research paper cannot be overstated, whether you’re in high school, college, or postgraduate studies. You can confidently prepare your research paper for submission by leveraging the AI tools listed above.

Research papers help refine your abilities to think critically and write persuasively. The skills you develop here will serve you well beyond the walls of the classroom. Communicating complex ideas clearly and effectively is one of the most powerful tools you can possess.

With the advancements of AI tools like Smodin , writing a research paper has become more accessible than ever before. These technologies streamline the process of organizing, writing, and revising your work. Write with confidence, knowing your best work is yet to come!

Stop doing your research essay wrong way!

Writing a research paper: devil in details..

Another task from a professor made you devastated and lost? Of course, because it's something that no one likes to do. Especially a young student who obviously have dozens of other important things to do. What task we are talking about? The monotonous and long, time consuming and confusing one. Yes, it's all about writing a research paper! Why students don't like it? Well, let's see.

First, the job requires a lot of time necessary for searching, reading, comparing, analyzing and then put together. Finding a topic and the data to do a research is just 30% of the task. The major part is to put everything in a proper format. For example, create a suitable and captivating research paper introduction to arouse your professor interest.

Second, it's the efforts to make everything correct and according to requirements. You don't write make a composition with your thoughts and conclusions in chaotic way. You need to make APA research paper to show your knowledge and proficiency. It takes time as well.

Third, it's the amount of workload. Looking for data, then finding the necessary parts, trying to put everything in a proper order and format. Isn't it easier just to have someone doing it for you?

Research essay: 5 steps to success!

Everyone wants to be a great student. Taking major part in college life, getting straight As, being helpful and easy-going. But what if you need help yourself? For instance, with your research essay. How to get it done and not to be exhausted? Here are some tips to help you!

• Calculate you time right. You'll never be able to finish a task overnight, so don't attempt to make a worthy writing when the deadline is tomorrow. Usually the task to write a paper is given long before the actual deadline.
• Choose the topic correctly. You won't be enjoying your work if you don't like the subject. In every task, even the most boring one, there might be something interesting for you. Be creative. Think outside the box. It might be the key to tremendous success!
• Acknowledge that it's time to give in. If you tried and then you failed, there might be other solutions. Like typing in Google "write my paper for me" and be relieved of the burden of the task.
• Divide the whole work into small tasks and steps. Let's face the truth, no one is able to compose the whole essay at one try. Little by little you'll get to the end of the task, but doing it gradually. And keep in mind the advise number 1!
• Rest! Don't stay in front of your PC until your eyes are red, brain not working, coffee not helping. Have a little walk outside, distract yourself from constant work. After that you'll be able to come back to the task with a new wave of energy.

Always check your paper.

No matter what you decide - to make the writing yourself or use professional service to do the task - keep in mind one general rule! Always check the essay to make sure there are no mistakes. There would be a possibility to make edits after you hand the paper to your professor. Look for spelling mistakes, compare the topic and content, check the format.

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• Open access
• Published: 13 May 2024

Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial

• Donna H. Ryan 1 ,
• Ildiko Lingvay   ORCID: orcid.org/0000-0001-7006-7401 2 ,
• John Deanfield 3 ,
• Steven E. Kahn 4 ,
• Eric Barros   ORCID: orcid.org/0000-0001-6613-4181 5 ,
• Bartolome Burguera 6 ,
• Helen M. Colhoun   ORCID: orcid.org/0000-0002-8345-3288 7 ,
• Cintia Cercato   ORCID: orcid.org/0000-0002-6181-4951 8 ,
• Dror Dicker 9 ,
• Deborah B. Horn 10 ,
• G. Kees Hovingh 5 ,
• Ole Kleist Jeppesen 5 ,
• Alexander Kokkinos 11 ,
• A. Michael Lincoff   ORCID: orcid.org/0000-0001-8175-2121 12 ,
• Sebastian M. Meyhöfer 13 ,
• Tugce Kalayci Oral 5 ,
• Jorge Plutzky   ORCID: orcid.org/0000-0002-7194-9876 14 ,
• André P. van Beek   ORCID: orcid.org/0000-0002-0335-8177 15 ,
• John P. H. Wilding   ORCID: orcid.org/0000-0003-2839-8404 16 &
• Robert F. Kushner 17  

Nature Medicine ( 2024 ) Cite this article

30k Accesses

1 Citations

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• Health care
• Medical research

In the SELECT cardiovascular outcomes trial, semaglutide showed a 20% reduction in major adverse cardiovascular events in 17,604 adults with preexisting cardiovascular disease, overweight or obesity, without diabetes. Here in this prespecified analysis, we examined effects of semaglutide on weight and anthropometric outcomes, safety and tolerability by baseline body mass index (BMI). In patients treated with semaglutide, weight loss continued over 65 weeks and was sustained for up to 4 years. At 208 weeks, semaglutide was associated with mean reduction in weight (−10.2%), waist circumference (−7.7 cm) and waist-to-height ratio (−6.9%) versus placebo (−1.5%, −1.3 cm and −1.0%, respectively; P  < 0.0001 for all comparisons versus placebo). Clinically meaningful weight loss occurred in both sexes and all races, body sizes and regions. Semaglutide was associated with fewer serious adverse events. For each BMI category (<30, 30 to <35, 35 to <40 and ≥40 kg m − 2 ) there were lower rates (events per 100 years of observation) of serious adverse events with semaglutide (43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo). Semaglutide was associated with increased rates of trial product discontinuation. Discontinuations increased as BMI class decreased. In SELECT, at 208 weeks, semaglutide produced clinically significant weight loss and improvements in anthropometric measurements versus placebo. Weight loss was sustained over 4 years. ClinicalTrials.gov identifier: NCT03574597 .

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Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial

Two-year effects of semaglutide in adults with overweight or obesity: the STEP 5 trial

What is the pipeline for future medications for obesity?

The worldwide obesity prevalence, defined by body mass index (BMI) ≥30 kg m − 2 , has nearly tripled since 1975 (ref. 1 ). BMI is a good surveillance measure for population changes over time, given its strong correlation with body fat amount on a population level, but it may not accurately indicate the amount or location of body fat at the individual level 2 . In fact, the World Health Organization defines clinical obesity as ‘abnormal or excessive fat accumulation that may impair health’ 1 . Excess abnormal body fat, especially visceral adiposity and ectopic fat, is a driver of cardiovascular (CV) disease (CVD) 3 , 4 , 5 , and contributes to the global chronic disease burden of diabetes, chronic kidney disease, cancer and other chronic conditions 6 , 7 .

Remediating the adverse health effects of excess abnormal body fat through weight loss is a priority in addressing the global chronic disease burden. Improvements in CV risk factors, glycemia and quality-of-life measures including personal well-being and physical functioning generally begin with modest weight loss of 5%, whereas greater weight loss is associated with more improvement in these measures 8 , 9 , 10 . Producing and sustaining durable and clinically significant weight loss with lifestyle intervention alone has been challenging 11 . However, weight-management medications that modify appetite can make attaining and sustaining clinically meaningful weight loss of ≥10% more likely 12 . Recently, weight-management medications, particularly those comprising glucagon-like peptide-1 receptor agonists, that help people achieve greater and more sustainable weight loss have been developed 13 . Once-weekly subcutaneous semaglutide 2.4 mg, a glucagon-like peptide-1 receptor agonist, is approved for chronic weight management 14 , 15 , 16 and at doses of up to 2.0 mg is approved for type 2 diabetes treatment 17 , 18 , 19 . In patients with type 2 diabetes and high CV risk, semaglutide at doses of 0.5 mg and 1.0 mg has been shown to significantly lower the risk of CV events 20 . The SELECT trial (Semaglutide Effects on Heart Disease and Stroke in Patients with Overweight or Obesity) studied patients with established CVD and overweight or obesity but without diabetes. In SELECT, semaglutide was associated with a 20% reduction in major adverse CV events (hazard ratio 0.80, 95% confidence interval (CI) 0.72 to 0.90; P  < 0.001) 21 . Data derived from the SELECT trial offer the opportunity to evaluate the weight loss efficacy, in a geographically and racially diverse population, of semaglutide compared with placebo over 208 weeks when both are given in addition to standard-of-care recommendations for secondary CVD prevention (but without a focus on targeting weight loss). Furthermore, the data allow examination of changes in anthropometric measures such as BMI, waist circumference (WC) and waist-to-height ratio (WHtR) as surrogates for body fat amount and location 22 , 23 . The diverse population can also be evaluated for changes in sex- and race-specific ‘cutoff points’ for BMI and WC, which have been identified as anthropometric measures that predict cardiometabolic risk 8 , 22 , 23 .

This prespecified analysis of the SELECT trial investigated weight loss and changes in anthropometric indices in patients with established CVD and overweight or obesity without diabetes, who met inclusion and exclusion criteria, within a range of baseline categories for glycemia, renal function and body anthropometric measures.

Study population

The SELECT study enrolled 17,604 patients (72.3% male) from 41 countries between October 2018 and March 2021, with a mean (s.d.) age of 61.6 (8.9) years and BMI of 33.3 (5.0) kg m − 2 (ref. 21 ). The baseline characteristics of the population have been reported 24 . Supplementary Table 1 outlines SELECT patients according to baseline BMI categories. Of note, in the lower BMI categories (<30 kg m − 2 (overweight) and 30 to <35 kg m − 2 (class I obesity)), the proportion of Asian individuals was higher (14.5% and 7.4%, respectively) compared with the proportion of Asian individuals in the higher BMI categories (BMI 35 to <40 kg m − 2 (class II obesity; 3.8%) and ≥40 kg m − 2 (class III obesity; 2.2%), respectively). As the BMI categories increased, the proportion of women was higher: in the class III BMI category, 45.5% were female, compared with 20.8%, 25.7% and 33.0% in the overweight, class I and class II categories, respectively. Lower BMI categories were associated with a higher proportion of patients with normoglycemia and glycated hemoglobin <5.7%. Although the proportions of patients with high cholesterol and history of smoking were similar across BMI categories, the proportion of patients with high-sensitivity C-reactive protein ≥2.0 mg dl −1 increased as the BMI category increased. A high-sensitivity C-reactive protein >2.0 mg dl −1 was present in 36.4% of patients in the overweight BMI category, with a progressive increase to 43.3%, 57.3% and 72.0% for patients in the class I, II and III obesity categories, respectively.

Weight and anthropometric outcomes

Percentage weight loss.

The average percentage weight-loss trajectories with semaglutide and placebo over 4 years of observation are shown in Fig. 1a (ref. 21 ). For those in the semaglutide group, the weight-loss trajectory continued to week 65 and then was sustained for the study period through week 208 (−10.2% for the semaglutide group, −1.5% for the placebo group; treatment difference −8.7%; 95% CI −9.42 to −7.88; P  < 0.0001). To estimate the treatment effect while on medication, we performed a first on-treatment analysis (observation period until the first time being off treatment for >35 days). At week 208, mean weight loss in the semaglutide group analyzed as first on-treatment was −11.7% compared with −1.5% for the placebo group (Fig. 1b ; treatment difference −10.2%; 95% CI −11.0 to −9.42; P  < 0.0001).

a , b , Observed data from the in-trial period ( a ) and first on-treatment ( b ). The symbols are the observed means, and error bars are ±s.e.m. Numbers shown below each panel represent the number of patients contributing to the means. Analysis of covariance with treatment and baseline values was used to estimate the treatment difference. Exact P values are 1.323762 × 10 −94 and 9.80035 × 10 −100 for a and b , respectively. P values are two-sided and are not adjusted for multiplicity. ETD, estimated treatment difference; sema, semaglutide.

Categorical weight loss and individual body weight change

Among in-trial (intention-to-treat principle) patients at week 104, weight loss of ≥5%, ≥10%, ≥15%, ≥20% and ≥25% was achieved by 67.8%, 44.2%, 22.9%, 11.0% and 4.9%, respectively, of those treated with semaglutide compared with 21.3%, 6.9%, 1.7%, 0.6% and 0.1% of those receiving placebo (Fig. 2a ). Individual weight changes at 104 weeks for the in-trial populations for semaglutide and placebo are depicted in Fig. 2b and Fig. 2c , respectively. These waterfall plots show the variation in weight-loss response that occurs with semaglutide and placebo and show that weight loss is more prominent with semaglutide than placebo.

a , Categorical weight loss from baseline at week 104 for semaglutide and placebo. Data from the in-trial period. Bars depict the proportion (%) of patients receiving semaglutide or placebo who achieved ≥5%, ≥10%, ≥15%, ≥20% and ≥25% weight loss. b , c , Percentage change in body weight for individual patients from baseline to week 104 for semaglutide ( b ) and placebo ( c ). Each patient’s percentage change in body weight is plotted as a single bar.

Change in WC

WC change from baseline to 104 weeks has been reported previously in the primary outcome paper 21 . The trajectory of WC change mirrored that of the change in body weight. At week 208, average reduction in WC was −7.7 cm with semaglutide versus −1.3 cm with placebo, with a treatment difference of −6.4 cm (95% CI −7.18 to −5.61; P  < 0.0001) 21 .

WC cutoff points

We analyzed achievement of sex- and race-specific cutoff points for WC by BMI <35 kg m − 2 or ≥35 kg m − 2 , because for BMI >35 kg m − 2 , WC is more difficult technically and, thus, less accurate as a risk predictor 4 , 25 , 26 . Within the SELECT population with BMI <35 kg m − 2 at baseline, 15.0% and 14.3% of the semaglutide and placebo groups, respectively, were below the sex- and race-specific WC cutoff points. At week 104, 41.2% fell below the sex- and race-specific cutoff points for the semaglutide group, compared with only 18.0% for the placebo group (Fig. 3 ).

WC cutoff points; Asian women <80 cm, non-Asian women <88 cm, Asian men <88 cm, non-Asian men <102 cm.

Waist-to-height ratio

At baseline, mean WHtR was 0.66 for the study population. The lowest tertile of the SELECT population at baseline had a mean WHtR <0.62, which is higher than the cutoff point of 0.5 used to indicate increased cardiometabolic risk 27 , suggesting that the trial population had high WCs. At week 208, in the group randomized to semaglutide, there was a relative reduction of 6.9% in WHtR compared with 1.0% in placebo (treatment difference −5.87% points; 95% CI −6.56 to −5.17; P  < 0.0001).

BMI category change

At week 104, 52.4% of patients treated with semaglutide achieved improvement in BMI category compared with 15.7% of those receiving placebo. Proportions of patients in the BMI categories at baseline and week 104 are shown in Fig. 4 , which depicts in-trial patients receiving semaglutide and placebo. The BMI category change reflects the superior weight loss with semaglutide, which resulted in fewer patients being in the higher BMI categories after 104 weeks. In the semaglutide group, 12.0% of patients achieved a BMI <25 kg m − 2 , which is considered the healthy BMI category, compared with 1.2% for placebo; per study inclusion criteria, no patients were in this category at baseline. The proportion of patients with obesity (BMI ≥30 kg m − 2 ) fell from 71.0% to 43.3% in the semaglutide group versus 71.9% to 67.9% in the placebo group.

In the semaglutide group, 12.0% of patients achieved normal weight status at week 104 (from 0% at baseline), compared with 1.2% (from 0% at baseline) for placebo. BMI classes: healthy (BMI <25 kg m − 2 ), overweight (25 to <30 kg m − 2 ), class I obesity (30 to <35 kg m − 2 ), class II obesity (35 to <40 kg m − 2 ) and class III obesity (BMI ≥40 kg m − 2 ).

Weight and anthropometric outcomes by subgroups

The forest plot illustrated in Fig. 5 displays mean body weight percentage change from baseline to week 104 for semaglutide relative to placebo in prespecified subgroups. Similar relationships are depicted for WC changes in prespecified subgroups shown in Extended Data Fig. 1 . The effect of semaglutide (versus placebo) on mean percentage body weight loss as well as reduction in WC was found to be heterogeneous across several population subgroups. Women had a greater difference in mean weight loss with semaglutide versus placebo (−11.1% (95% CI −11.56 to −10.66) versus −7.5% in men (95% CI −7.78 to −7.23); P  < 0.0001). There was a linear relationship between age category and degree of mean weight loss, with younger age being associated with progressively greater mean weight loss, but the actual mean difference by age group is small. Similarly, BMI category had small, although statistically significant, associations. Those with WHtR less than the median experienced slightly lower mean body weight change than those above the median, with estimated treatment differences −8.04% (95% CI −8.37 to −7.70) and −8.99% (95% CI −9.33 to −8.65), respectively ( P  < 0.0001). Patients from Asia and of Asian race experienced slightly lower mean weight loss (estimated treatment difference with semaglutide for Asian race −7.27% (95% CI −8.09 to −6.46; P  = 0.0147) and for Asia −7.30 (95% CI −7.97 to −6.62; P  = 0.0016)). There was no difference in weight loss with semaglutide associated with ethnicity (estimated treatment difference for Hispanic −8.53% (95% CI −9.28 to −7.76) or non-Hispanic −8.52% (95% CI −8.77 to 8.26); P  = 0.9769), glycemic status (estimated treatment difference for prediabetes −8.53% (95% CI −8.83 to −8.24) or normoglycemia −8.48% (95% CI −8.88 to −8.07; P  = 0.8188) or renal function (estimated treatment difference for estimated glomerular filtration rate (eGFR) <60 or ≥60 ml min −1  1.73 m − 2 being −8.50% (95% CI −9.23 to −7.76) and −8.52% (95% CI −8.77 to −8.26), respectively ( P  = 0.9519)).

Data from the in-trial period. N  = 17,604. P values represent test of no interaction effect. P values are two-sided and are not adjusted for multiplicity. The dots show estimated treatment differences, and the error bars show 95% CIs. Details of the statistical models are available in Methods . ETD, estimated treatment difference; HbA1c, glycated hemoglobin; MI, myocardial infarction; PAD, peripheral artery disease; sema, semaglutide.

Safety and tolerability according to baseline BMI category

We reported in the primary outcome of the SELECT trial that adverse events (AEs) leading to permanent discontinuation of the trial product occurred in 1,461 patients (16.6%) in the semaglutide group and 718 patients (8.2%) in the placebo group ( P  < 0.001) 21 . For this analysis, we evaluated the cumulative incidence of AEs leading to trial product discontinuation by treatment assignment and by BMI category (Fig. 6 ). For this analysis, with death modeled as a competing risk, we tracked the proportion of in-trial patients for whom drug was withdrawn or interrupted for the first time (Fig. 6 , left) or cumulative discontinuations (Fig. 6 , right). Both panels of Fig. 6 depict a graded increase in the proportion discontinuing semaglutide, but not placebo. For lower BMI classes, discontinuation rates are higher in the semaglutide group but not the placebo group.

Data are in-trial from the full analysis set. sema, semaglutide.

We reported in the primary SELECT analysis that serious adverse events (SAEs) were reported by 2,941 patients (33.4%) in the semaglutide arm and by 3,204 patients (36.4%) in the placebo arm ( P  < 0.001) 21 . For this study, we analyzed SAE rates by person-years of treatment exposure for BMI classes (<30 kg m − 2 , 30 to <35 kg m − 2 , 35 to <40 kg m − 2 , and ≥40 kg m − 2 ) and provide these data in Supplementary Table 2 . We also provide an analysis of the most common categories of SAEs. Semaglutide was associated with lower SAEs, primarily driven by CV event and infections. Within each obesity class (<30 kg m − 2 , 30 to <35 kg m − 2 , 35 to <40 kg m − 2 , and ≥40 kg m − 2 ), there were fewer SAEs in the group receiving semaglutide compared with placebo. Rates (events per 100 years of observation) of SAEs were 43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo, with no evidence of heterogeneity. There was no detectable difference in hepatobiliary or gastrointestinal SAEs comparing semaglutide with placebo in any of the four BMI classes we evaluated.

The analyses of weight effects of the SELECT study presented here reveal that patients assigned to once-weekly subcutaneous semaglutide 2.4 mg lost significantly more weight than those receiving placebo. The weight-loss trajectory with semaglutide occurred over 65 weeks and was sustained up to 4 years. Likewise, there were similar improvements in the semaglutide group for anthropometrics (WC and WHtR). The weight loss was associated with a greater proportion of patients receiving semaglutide achieving improvement in BMI category, healthy BMI (<25 kg m − 2 ) and falling below the WC cutoff point above which increased cardiometabolic risk for the sex and race is greater 22 , 23 . Furthermore, both sexes, all races, all body sizes and those from all geographic regions were able to achieve clinically meaningful weight loss. There was no evidence of increased SAEs based on BMI categories, although lower BMI category was associated with increased rates of trial product discontinuation, probably reflecting exposure to a higher level of drug in lower BMI categories. These data, representing the longest clinical trial of the effects of semaglutide versus placebo on weight, establish the safety and durability of semaglutide effects on weight loss and maintenance in a geographically and racially diverse population of adult men and women with overweight and obesity but not diabetes. The implications of weight loss of this degree in such a diverse population suggests that it may be possible to impact the public health burden of the multiple morbidities associated with obesity. Although our trial focused on CV events, many chronic diseases would benefit from effective weight management 28 .

There were variations in the weight-loss response. Individual changes in body weight with semaglutide and placebo were striking; still, 67.8% achieved 5% or more weight loss and 44.2% achieved 10% weight loss with semaglutide at 2 years, compared with 21.3% and 6.9%, respectively, for those receiving placebo. Our first on-treatment analysis demonstrated that those on-drug lost more weight than those in-trial, confirming the effect of drug exposure. With semaglutide, lower BMI was associated with less percentage weight loss, and women lost more weight on average than men (−11.1% versus −7.5% treatment difference from placebo); however, in all cases, clinically meaningful mean weight loss was achieved. Although Asian patients lost less weight on average than patients of other races (−7.3% more than placebo), Asian patients were more likely to be in the lowest BMI category (<30 kg m − 2 ), which is known to be associated with less weight loss, as discussed below. Clinically meaningful weight loss was evident in the semaglutide group within a broad range of baseline categories for glycemia and body anthropometrics. Interestingly, at 2 years, a significant proportion of the semaglutide-treated group fell below the sex- and race-specific WC cutoff points, especially in those with BMI <35 kg m − 2 , and a notable proportion (12.0%) fell below the BMI cutoff point of 25 kg m − 2 , which is deemed a healthy BMI in those without unintentional weight loss. As more robust weight loss is possible with newer medications, achieving and maintaining these cutoff point targets may become important benchmarks for tracking responses.

The overall safety profile did not reveal any new signals from prior studies, and there were no BMI category-related associations with AE reporting. The analysis did reveal that tolerability may differ among specific BMI classes, since more discontinuations occurred with semaglutide among lower BMI classes. Potential contributors may include a possibility of higher drug exposure in lower BMI classes, although other explanations, including differences in motivation and cultural mores regarding body size, cannot be excluded.

Is the weight loss in SELECT less than expected based on prior studies with the drug? In STEP 1, a large phase 3 study of once-weekly subcutaneous semaglutide 2.4 mg in individuals without diabetes but with BMI >30 kg m − 2 or 27 kg m − 2 with at least one obesity-related comorbidity, the mean weight loss was −14.9% at week 68, compared with −2.4% with placebo 14 . Several reasons may explain the observation that the mean treatment difference was −12.5% in STEP 1 and −8.7% in SELECT. First, SELECT was designed as a CV outcomes trial and not a weight-loss trial, and weight loss was only a supportive secondary endpoint in the trial design. Patients in STEP 1 were desirous of weight loss as a reason for study participation and received structured lifestyle intervention (which included a −500 kcal per day diet with 150 min per week of physical activity). In the SELECT trial, patients did not enroll for the specific purpose of weight loss and received standard of care covering management of CV risk factors, including medical treatment and healthy lifestyle counseling, but without a specific focus on weight loss. Second, the respective study populations were quite different, with STEP 1 including a younger, healthier population with more women (73.1% of the semaglutide arm in STEP 1 versus 27.7% in SELECT) and higher mean BMI (37.8 kg m − 2 versus 33.3 kg m − 2 , respectively) 14 , 21 . Third, major differences existed between the respective trial protocols. Patients in the semaglutide treatment arm of STEP 1 were more likely to be exposed to the medication at the full dose of 2.4 mg than those in SELECT. In SELECT, investigators were allowed to slow, decrease or pause treatment. By 104 weeks, approximately 77% of SELECT patients on dose were receiving the target semaglutide 2.4 mg weekly dose, which is lower than the corresponding proportion of patients in STEP 1 (89.6% were receiving the target dose at week 68) 14 , 21 . Indeed, in our first on-treatment analysis at week 208, weight loss was greater (−11.7% for semaglutide) compared with the in-trial analysis (−10.2% for semaglutide). Taken together, all these issues make less weight loss an expected finding in SELECT, compared with STEP 1.

The SELECT study has some limitations. First, SELECT was not a primary prevention trial, and the data should not be extrapolated to all individuals with overweight and obesity to prevent major adverse CV events. Although the data set is rich in numbers and diversity, it does not have the numbers of individuals in racial subgroups that may have revealed potential differential effects. SELECT also did not include individuals who have excess abnormal body fat but a BMI <27 kg m − 2 . Not all individuals with increased CV risk have BMI ≥27 kg m − 2 . Thus, the study did not include Asian patients who qualify for treatment with obesity medications at lower BMI and WC cutoff points according to guidelines in their countries 29 . We observed that Asian patients were less likely to be in the higher BMI categories of SELECT and that the population of those with BMI <30 kg m − 2 had a higher percentage of Asian race. Asian individuals would probably benefit from weight loss and medication approaches undertaken at lower BMI levels in the secondary prevention of CVD. Future studies should evaluate CV risk reduction in Asian individuals with high CV risk and BMI <27 kg m − 2 . Another limitation is the lack of information on body composition, beyond the anthropometric measures we used. It would be meaningful to have quantitation of fat mass, lean mass and muscle mass, especially given the wide range of body size in the SELECT population.

An interesting observation from this SELECT weight loss data is that when BMI is ≤30 kg m − 2 , weight loss on a percentage basis is less than that observed across higher classes of BMI severity. Furthermore, as BMI exceeds 30 kg m − 2 , weight loss amounts are more similar for class I, II and III obesity. This was also observed in Look AHEAD, a lifestyle intervention study for weight loss 30 . The proportion (percentage) of weight loss seems to be less, on average, in the BMI <30 kg m − 2 category relative to higher BMI categories, despite their receiving of the same treatment and even potentially higher exposure to the drug for weight loss 30 . Weight loss cannot continue indefinitely. There is a plateau of weight that occurs after weight loss with all treatments for weight management. This plateau has been termed the ‘set point’ or ‘settling point’, a body weight that is in harmony with the genetic and environmental determinants of body weight and adiposity 31 . Perhaps persons with BMI <30 kg m − 2 are closer to their settling point and have less weight to lose to reach it. Furthermore, the cardiometabolic benefits of weight loss are driven by reduction in the abnormal ectopic and visceral depots of fat, not by reduction of subcutaneous fat stores in the hips and thighs. The phenotype of cardiometabolic disease but lower BMI (<30 kg m − 2 ) may be one where reduction of excess abnormal and dysfunctional body fat does not require as much body mass reduction to achieve health improvement. We suspect this may be the case and suggest further studies to explore this aspect of weight-loss physiology.

In conclusion, this analysis of the SELECT study supports the broad use of once-weekly subcutaneous semaglutide 2.4 mg as an aid to CV event reduction in individuals with overweight or obesity without diabetes but with preexisting CVD. Semaglutide 2.4 mg safely and effectively produced clinically significant weight loss in all subgroups based on age, sex, race, glycemia, renal function and anthropometric categories. Furthermore, the weight loss was sustained over 4 years during the trial.

Trial design and participants

The current work complies with all relevant ethical regulations and reports a prespecified analysis of the randomized, double-blind, placebo-controlled SELECT trial ( NCT03574597 ), details of which have been reported in papers describing study design and rationale 32 , baseline characteristics 24 and the primary outcome 21 . SELECT evaluated once-weekly subcutaneous semaglutide 2.4 mg versus placebo to reduce the risk of major adverse cardiac events (a composite endpoint comprising CV death, nonfatal myocardial infarction or nonfatal stroke) in individuals with established CVD and overweight or obesity, without diabetes. The protocol for SELECT was approved by national and institutional regulatory and ethical authorities in each participating country. All patients provided written informed consent before beginning any trial-specific activity. Eligible patients were aged ≥45 years, with a BMI of ≥27 kg m − 2 and established CVD defined as at least one of the following: prior myocardial infarction, prior ischemic or hemorrhagic stroke, or symptomatic peripheral artery disease. Additional inclusion and exclusion criteria can be found elsewhere 32 .

Human participants research

The trial protocol was designed by the trial sponsor, Novo Nordisk, and the academic Steering Committee. A global expert panel of physician leaders in participating countries advised on regional operational issues. National and institutional regulatory and ethical authorities approved the protocol, and all patients provided written informed consent.

Study intervention and patient management

Patients were randomly assigned in a double-blind manner and 1:1 ratio to receive once-weekly subcutaneous semaglutide 2.4 mg or placebo. The starting dose was 0.24 mg once weekly, with dose increases every 4 weeks (to doses of 0.5, 1.0, 1.7 and 2.4 mg per week) until the target dose of 2.4 mg was reached after 16 weeks. Patients who were unable to tolerate dose escalation due to AEs could be managed by extension of dose-escalation intervals, treatment pauses or maintenance at doses below the 2.4 mg per week target dose. Investigators were allowed to reduce the dose of study product if tolerability issues arose. Investigators were provided with guidelines for, and encouraged to follow, evidence-based recommendations for medical treatment and lifestyle counseling to optimize management of underlying CVD as part of the standard of care. The lifestyle counseling was not targeted at weight loss. Additional intervention descriptions are available 32 .

Sex, race, body weight, height and WC measurements

Sex and race were self-reported. Body weight was measured without shoes and only wearing light clothing; it was measured on a digital scale and recorded in kilograms or pounds (one decimal with a precision of 0.1 kg or lb), with preference for using the same scale throughout the trial. The scale was calibrated yearly as a minimum unless the manufacturer certified that calibration of the weight scales was valid for the lifetime of the scale. Height was measured without shoes in centimeters or inches (one decimal with a precision of 0.1 cm or inches). At screening, BMI was calculated by the electronic case report form. WC was defined as the abdominal circumference located midway between the lower rib margin and the iliac crest. Measures were obtained in a standing position with a nonstretchable measuring tape and to the nearest centimeter or inch. The patient was asked to breathe normally. The tape touched the skin but did not compress soft tissue, and twists in the tape were avoided.

The following endpoints relevant to this paper were assessed at randomization (week 0) to years 2, 3 and 4: change in body weight (%); proportion achieving weight loss ≥5%, ≥10%, ≥15% and ≥20%; change in WC (cm); and percentage change in WHtR (cm cm −1 ). Improvement in BMI category (defined as being in a lower BMI class) was assessed at week 104 compared with baseline according to BMI classes: healthy (BMI <25 kg m − 2 ), overweight (25 to <30 kg m − 2 ), class I obesity (30 to <35 kg m − 2 ), class II obesity (35 to <40 kg m − 2 ) and class III obesity (≥40 kg m − 2 ). The proportions of individuals with BMI <35 or ≥35 kg m − 2 who achieved sex- and race-specific cutoff points for WC (indicating increased metabolic risk) were evaluated at week 104. The WC cutoff points were as follows: Asian women <80 cm, non-Asian women <88 cm, Asian men <88 cm and non-Asian men <102 cm.

Overall, 97.1% of the semaglutide group and 96.8% of the placebo group completed the trial. During the study, 30.6% of those assigned to semaglutide did not complete drug treatment, compared with 27.0% for placebo.

Statistical analysis

The statistical analyses for the in-trial period were based on the intention-to-treat principle and included all randomized patients irrespective of adherence to semaglutide or placebo or changes to background medications. Continuous endpoints were analyzed using an analysis of covariance model with treatment as a fixed factor and baseline value of the endpoint as a covariate. Missing data at the landmark visit, for example, week 104, were imputed using a multiple imputation model and done separately for each treatment arm and included baseline value as a covariate and fit to patients having an observed data point (irrespective of adherence to randomized treatment) at week 104. The fit model is used to impute values for all patients with missing data at week 104 to create 500 complete data sets. Rubin’s rules were used to combine the results. Estimated means are provided with s.e.m., and estimated treatment differences are provided with 95% CI. Binary endpoints were analyzed using logistic regression with treatment and baseline value as a covariate, where missing data were imputed by first using multiple imputation as described above and then categorizing the imputed data according to the endpoint, for example, body weight percentage change at week 104 of <0%. Subgroup analyses for continuous and binary endpoints also included the subgroup and interaction between treatment and subgroup as fixed factors. Because some patients in both arms continued to be followed but were off treatment, we also analyzed weight loss by first on-treatment group (observation period until first time being off treatment for >35 days) to assess a more realistic picture of weight loss in those adhering to treatment. CIs were not adjusted for multiplicity and should therefore not be used to infer definitive treatment effects. All statistical analyses were performed with SAS software, version 9.4 TS1M5 (SAS Institute).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data will be shared with bona fide researchers who submit a research proposal approved by the independent review board. Individual patient data will be shared in data sets in a deidentified and anonymized format. Information about data access request proposals can be found at https://www.novonordisk-trials.com/ .

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Acknowledgements

Editorial support was provided by Richard Ogilvy-Stewart of Apollo, OPEN Health Communications, and funded by Novo Nordisk A/S, in accordance with Good Publication Practice guidelines ( www.ismpp.org/gpp-2022 ).

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Donna H. Ryan

Department of Internal Medicine/Endocrinology and Peter O’ Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA

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Contributions

D.H.R., I.L. and S.E.K. contributed to the study design. D.B.H., I.L., D.D., A.K., S.M.M., A.P.v.B., C.C. and J.P.H.W. were study investigators. D.B.H., I.L., D.D., A.K., S.M.M., A.P.v.B., C.C. and J.P.H.W. enrolled patients. D.H.R. was responsible for data analysis and manuscript preparation. All authors contributed to data interpretation, review, revisions and final approval of the manuscript.

Corresponding author

Correspondence to Donna H. Ryan .

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Competing interests.

D.H.R. declares having received consulting honoraria from Altimmune, Amgen, Biohaven, Boehringer Ingelheim, Calibrate, Carmot Therapeutics, CinRx, Eli Lilly, Epitomee, Gila Therapeutics, IFA Celtics, Novo Nordisk, Pfizer, Rhythm, Scientific Intake, Wondr Health and Zealand Pharma; she declares she received stock options from Calibrate, Epitomee, Scientific Intake and Xeno Bioscience. I.L. declares having received research funding (paid to institution) from Novo Nordisk, Sanofi, Mylan and Boehringer Ingelheim. I.L. received advisory/consulting fees and/or other support from Altimmune, AstraZeneca, Bayer, Biomea, Boehringer Ingelheim, Carmot Therapeutics, Cytoki Pharma, Eli Lilly, Intercept, Janssen/Johnson & Johnson, Mannkind, Mediflix, Merck, Metsera, Novo Nordisk, Pharmaventures, Pfizer, Regeneron, Sanofi, Shionogi, Structure Therapeutics, Target RWE, Terns Pharmaceuticals, The Comm Group, Valeritas, WebMD and Zealand Pharma. J.D. declares having received consulting honoraria from Amgen, Boehringer Ingelheim, Merck, Pfizer, Aegerion, Novartis, Sanofi, Takeda, Novo Nordisk and Bayer, and research grants from British Heart Foundation, MRC (UK), NIHR, PHE, MSD, Pfizer, Aegerion, Colgate and Roche. S.E.K. declares having received consulting honoraria from ANI Pharmaceuticals, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk and Oramed, and stock options from AltPep. B.B. declares having received honoraria related to participation on this trial and has no financial conflicts related to this publication. H.M.C. declares being a stockholder and serving on an advisory panel for Bayer; receiving research grants from Chief Scientist Office, Diabetes UK, European Commission, IQVIA, Juvenile Diabetes Research Foundation and Medical Research Council; serving on an advisory board and speaker’s bureau for Novo Nordisk; and holding stock in Roche Pharmaceuticals. C.C. declares having received consulting honoraria from Novo Nordisk, Eli Lilly, Merck, Brace Pharma and Eurofarma. D.D. declares having received consulting honoraria from Novo Nordisk, Eli Lilly, Boehringer Ingelheim and AstraZeneca, and received research grants through his affiliation from Novo Nordisk, Eli Lilly, Boehringer Ingelheim and Rhythm. D.B.H. declares having received research grants through her academic affiliation from Novo Nordisk and Eli Lilly, and advisory/consulting honoraria from Novo Nordisk, Eli Lilly and Gelesis. A.K. declares having received research grants through his affiliation from Novo Nordisk and Pharmaserve Lilly, and consulting honoraria from Pharmaserve Lilly, Sanofi-Aventis, Novo Nordisk, MSD, AstraZeneca, ELPEN Pharma, Boehringer Ingelheim, Galenica Pharma, Epsilon Health and WinMedica. A.M.L. declares having received honoraria from Novo Nordisk, Eli Lilly, Akebia Therapeutics, Ardelyx, Becton Dickinson, Endologix, FibroGen, GSK, Medtronic, Neovasc, Provention Bio, ReCor, BrainStorm Cell Therapeutics, Alnylam and Intarcia for consulting activities, and research funding to his institution from AbbVie, Esperion, AstraZeneca, CSL Behring, Novartis and Eli Lilly. S.M.M. declares having received consulting honoraria from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Daichii-Sankyo, esanum, Gilead, Ipsen, Eli Lilly, Novartis, Novo Nordisk, Sandoz and Sanofi; he declares he received research grants from AstraZeneca, Eli Lilly and Novo Nordisk. J.P. declares having received consulting honoraria from Altimmune, Amgen, Esperion, Merck, MJH Life Sciences, Novartis and Novo Nordisk; he has received a grant, paid to his institution, from Boehringer Ingelheim and holds the position of Director, Preventive Cardiology, at Brigham and Women’s Hospital. A.P.v.B. is contracted via the University of Groningen (no personal payment) to undertake consultancy for Novo Nordisk, Eli Lilly and Boehringer Ingelheim. J.P.H.W. is contracted via the University of Liverpool (no personal payment) to undertake consultancy for Altimmune, AstraZeneca, Boehringer Ingelheim, Cytoki, Eli Lilly, Napp, Novo Nordisk, Menarini, Pfizer, Rhythm Pharmaceuticals, Sanofi, Saniona, Tern Pharmaceuticals, Shionogi and Ysopia. J.P.H.W. also declares personal honoraria/lecture fees from AstraZeneca, Boehringer Ingelheim, Medscape, Napp, Menarini, Novo Nordisk and Rhythm. R.F.K. declares having received consulting honoraria from Novo Nordisk, Weight Watchers, Eli Lilly, Boehringer Ingelheim, Pfizer, Structure and Altimmune. E.B., G.K.H., O.K.J. and T.K.O. are employees of Novo Nordisk A/S.

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Extended data

Extended data fig. 1 effect of semaglutide treatment or placebo on waist circumference from baseline to week 104 by subgroups..

Data from the in-trial period. N  = 17,604. P values represent test of no interaction effect. P values are two-sided and not adjusted for multiplicity. The dots show estimated treatment differences and the error bars show 95% confidence intervals. Details of the statistical models are available in Methods . BMI, body mass index; CI, confidence interval; CV, cardiovascular; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; ETD, estimated treatment difference; HbA1c, glycated hemoglobin; MI, myocardial infarction; PAD, peripheral artery disease; sema, semaglutide.

Supplementary information

Reporting summary, supplementary tables 1 and 2.

Supplementary Table 1. Baseline characteristics by BMI class. Data are represented as number and percentage of patients. Renal function categories were based on the eGFR as per Chronic Kidney Disease Epidemiology Collaboration. Albuminuria categories were based on UACR. Smoking was defined as smoking at least one cigarette or equivalent daily. The category ‘Other’ for CV inclusion criteria includes patients where it is unknown if the patient fulfilled only one or several criteria and patients who were randomized in error and did not fulfill any criteria. Supplementary Table 2. SAEs according to baseline BMI category. P value: two-sided P value from Fisher’s exact test for test of no difference.

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Ryan, D.H., Lingvay, I., Deanfield, J. et al. Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial. Nat Med (2024). https://doi.org/10.1038/s41591-024-02996-7

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DOI : https://doi.org/10.1038/s41591-024-02996-7

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Better Siri is coming: what Apple’s research says about its AI plans

Apple hasn’t talked too much about ai so far — but it’s been working on stuff. a lot of stuff..

By David Pierce , editor-at-large and Vergecast co-host with over a decade of experience covering consumer tech. Previously, at Protocol, The Wall Street Journal, and Wired.

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It would be easy to think that Apple is late to the game on AI. Since late 2022, when ChatGPT took the world by storm, most of Apple’s competitors have fallen over themselves to catch up. While Apple has certainly talked about AI and even released some products with AI in mind, it seemed to be dipping a toe in rather than diving in headfirst.

But over the last few months, rumors and reports have suggested that Apple has, in fact, just been biding its time, waiting to make its move. There have been reports in recent weeks that Apple is talking to both OpenAI and Google about powering some of its AI features, and the company has also been working on its own model, called Ajax .

If you look through Apple’s published AI research, a picture starts to develop of how Apple’s approach to AI might come to life. Now, obviously, making product assumptions based on research papers is a deeply inexact science — the line from research to store shelves is windy and full of potholes. But you can at least get a sense of what the company is thinking about — and how its AI features might work when Apple starts to talk about them at its annual developer conference, WWDC, in June.

Smaller, more efficient models

I suspect you and I are hoping for the same thing here: Better Siri. And it looks very much like Better Siri is coming! There’s an assumption in a lot of Apple’s research (and in a lot of the tech industry, the world, and everywhere) that large language models will immediately make virtual assistants better and smarter. For Apple, getting to Better Siri means making those models as fast as possible — and making sure they’re everywhere.

In iOS 18, Apple plans to have all its AI features running on an on-device, fully offline model, Bloomberg recently reported . It’s tough to build a good multipurpose model even when you have a network of data centers and thousands of state-of-the-art GPUs — it’s drastically harder to do it with only the guts inside your smartphone. So Apple’s having to get creative.

In a paper called “ LLM in a flash: Efficient Large Language Model Inference with Limited Memory ” (all these papers have really boring titles but are really interesting, I promise!), researchers devised a system for storing a model’s data, which is usually stored on your device’s RAM, on the SSD instead. “We have demonstrated the ability to run LLMs up to twice the size of available DRAM [on the SSD],” the researchers wrote, “achieving an acceleration in inference speed by 4-5x compared to traditional loading methods in CPU, and 20-25x in GPU.” By taking advantage of the most inexpensive and available storage on your device, they found, the models can run faster and more efficiently. 

Apple’s researchers also created a system called EELBERT that can essentially compress an LLM into a much smaller size without making it meaningfully worse. Their compressed take on Google’s Bert model was 15 times smaller — only 1.2 megabytes — and saw only a 4 percent reduction in quality. It did come with some latency tradeoffs, though.

In general, Apple is pushing to solve a core tension in the model world: the bigger a model gets, the better and more useful it can be, but also the more unwieldy, power-hungry, and slow it can become. Like so many others, the company is trying to find the right balance between all those things while also looking for a way to have it all.

Siri, but good

A lot of what we talk about when we talk about AI products is virtual assistants — assistants that know things, that can remind us of things, that can answer questions, and get stuff done on our behalf. So it’s not exactly shocking that a lot of Apple’s AI research boils down to a single question: what if Siri was really, really, really good?

A group of Apple researchers has been working on a way to use Siri without needing to use a wake word at all; instead of listening for “Hey Siri” or “Siri,” the device might be able to simply intuit whether you’re talking to it. “This problem is significantly more challenging than voice trigger detection,” the researchers did acknowledge, “since there might not be a leading trigger phrase that marks the beginning of a voice command.” That might be why another group of researchers developed a system to more accurately detect wake words . Another paper trained a model to better understand rare words, which are often not well understood by assistants.

In both cases, the appeal of an LLM is that it can, in theory, process much more information much more quickly. In the wake-word paper, for instance, the researchers found that by not trying to discard all unnecessary sound but, instead, feeding it all to the model and letting it process what does and doesn’t matter, the wake word worked far more reliably.

Once Siri hears you, Apple’s doing a bunch of work to make sure it understands and communicates better. In one paper, it developed a system called STEER (which stands for Semantic Turn Extension-Expansion Recognition, so we’ll go with STEER) that aims to improve your back-and-forth communication with an assistant by trying to figure out when you’re asking a follow-up question and when you’re asking a new one. In another, it uses LLMs to better understand “ambiguous queries” to figure out what you mean no matter how you say it. “In uncertain circumstances,” they wrote, “intelligent conversational agents may need to take the initiative to reduce their uncertainty by asking good questions proactively, thereby solving problems more effectively.” Another paper aims to help with that, too: researchers used LLMs to make assistants less verbose and more understandable when they’re generating answers.

AI in health, image editors, in your Memojis

Whenever Apple does talk publicly about AI, it tends to focus less on raw technological might and more on the day-to-day stuff AI can actually do for you. So, while there’s a lot of focus on Siri — especially as Apple looks to compete with devices like the Humane AI Pin, the Rabbit R1, and Google’s ongoing smashing of Gemini into all of Android — there are plenty of other ways Apple seems to see AI being useful.

One obvious place for Apple to focus is on health: LLMs could, in theory, help wade through the oceans of biometric data collected by your various devices and help you make sense of it all. So, Apple has been researching how to collect and collate all of your motion data, how to use gait recognition and your headphones to identify you, and how to track and understand your heart rate data. Apple also created and released “the largest multi-device multi-location sensor-based human activity dataset” available after collecting data from 50 participants with multiple on-body sensors.

Apple also seems to imagine AI as a creative tool. For one paper, researchers interviewed a bunch of animators, designers, and engineers and built a system called Keyframer that “enable[s] users to iteratively construct and refine generated designs.” Instead of typing in a prompt and getting an image, then typing another prompt to get another image, you start with a prompt but then get a toolkit to tweak and refine parts of the image to your liking. You could imagine this kind of back-and-forth artistic process showing up anywhere from the Memoji creator to some of Apple’s more professional artistic tools.

In another paper , Apple describes a tool called MGIE that lets you edit an image just by describing the edits you want to make. (“Make the sky more blue,” “make my face less weird,” “add some rocks,” that sort of thing.) “Instead of brief but ambiguous guidance, MGIE derives explicit visual-aware intention and leads to reasonable image editing,” the researchers wrote. Its initial experiments weren’t perfect, but they were impressive.

We might even get some AI in Apple Music: for a paper called “ Resource-constrained Stereo Singing Voice Cancellation ,” researchers explored ways to separate voices from instruments in songs — which could come in handy if Apple wants to give people tools to, say, remix songs the way you can on TikTok or Instagram.

Over time, I’d bet this is the kind of stuff you’ll see Apple lean into, especially on iOS. Some of it Apple will build into its own apps; some it will offer to third-party developers as APIs. (The recent Journaling Suggestions feature is probably a good guide to how that might work.) Apple has always trumpeted its hardware capabilities, particularly compared to your average Android device; pairing all that horsepower with on-device, privacy-focused AI could be a big differentiator.

But if you want to see the biggest, most ambitious AI thing going at Apple, you need to know about Ferret . Ferret is a multi-modal large language model that can take instructions, focus on something specific you’ve circled or otherwise selected, and understand the world around it. It’s designed for the now-normal AI use case of asking a device about the world around you, but it might also be able to understand what’s on your screen. In the Ferret paper, researchers show that it could help you navigate apps, answer questions about App Store ratings, describe what you’re looking at, and more. This has really exciting implications for accessibility but could also completely change the way you use your phone — and your Vision Pro and / or smart glasses someday.

We’re getting way ahead of ourselves here, but you can imagine how this would work with some of the other stuff Apple is working on. A Siri that can understand what you want, paired with a device that can see and understand everything that’s happening on your display, is a phone that can literally use itself. Apple wouldn’t need deep integrations with everything; it could simply run the apps and tap the right buttons automatically. 

Again, all this is just research, and for all of it to work well starting this spring would be a legitimately unheard-of technical achievement. (I mean, you’ve tried chatbots — you know they’re not great.) But I’d bet you anything we’re going to get some big AI announcements at WWDC. Apple CEO Tim Cook even teased as much in February, and basically promised it on this week’s earnings call. And two things are very clear: Apple is very much in the AI race, and it might amount to a total overhaul of the iPhone. Heck, you might even start willingly using Siri! And that would be quite the accomplishment.

Sonos is teasing its ‘most requested product ever’ on Tuesday

Two students find security bug that could let millions do laundry for free, microsoft’s surface and windows ai event live blog: it’s arm time, the ai assistants are getting better fast, what to expect from microsoft’s surface event today.

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Super-efficient solar cells: 10 Breakthrough Technologies 2024

Solar cells that combine traditional silicon with cutting-edge perovskites could push the efficiency of solar panels to new heights.

• Emma Foehringer Merchant archive page

Beyond Silicon, Caelux, First Solar, Hanwha Q Cells, Oxford PV, Swift Solar, Tandem PV

3 to 5 years

In November 2023, a buzzy solar technology broke yet another world record for efficiency. The previous record had existed for only about five months—and it likely won’t be long before it too is obsolete. This astonishing acceleration in efficiency gains comes from a special breed of next-­generation solar technology: perovskite tandem solar cells. These cells layer the traditional silicon with materials that share a unique crystal structure.

In the decade that scientists have been toying with perovskite solar technology , it has continued to best its own efficiency records, which measure how much of the sunlight that hits the cell is converted into electricity. Perovskites absorb different wavelengths of light from those absorbed by silicon cells, which account for 95% of the solar market today. When silicon and perovskites work together in tandem solar cells, they can utilize more of the solar spectrum, producing more electricity per cell. 

Technical efficiency levels for silicon-­based cells top out below 30%, while perovskite-only cells have reached experimental efficiencies of around 26%. But perovskite tandem cells have already exceeded 33% efficiency in the lab. That is the technology’s tantalizing promise: if deployed on a significant scale, perovskite tandem cells could produce more electricity than the legacy solar cells at a lower cost. 

But perovskites have stumbled when it comes to actual deployment. Silicon solar cells can last for decades. Few perovskite tandem panels have even been tested outside. 

The electrochemical makeup of perovskites means they’re sensitive to sucking up water and degrading in heat, though researchers have been working to create better barriers around panels and shifting to more stable perovskite compounds. 

In May, UK-based Oxford PV said it had reached an efficiency of 28.6% for a commercial-size perovskite tandem cell, which is significantly larger than those used to test the materials in the lab, and it plans to deliver its first panels and ramp up manufacturing in 2024. Other companies could unveil products later this decade. 

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The hard lessons of Harvard’s failed geoengineering experiment

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