how to read research papers using ai

The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my YouTube channel.

My Top AI Tools for Researchers and Academics – Tested and Reviewed!

There are many different tools now available on the market but there are only a handful that are specifically designed with researchers and academics as their primary user.

These are my recommendations that’ll cover almost everything that you’ll want to do:

Want to find out all of the tools that you could use?

Here they are, below:

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Litmaps –  https://www.litmaps.com
• Research rabbit – https://www.researchrabbit.ai/
• Connected Papers –  https://www.connectedpapers.com/
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Laser AI –  https://laser.ai/
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Consensus –  https://consensus.app/
• Iris AI –  https://iris.ai/
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Aetherbrain – https://aetherbrain.ai
• Explain Paper – https://www.explainpaper.com
• Chat PDF – https://www.chatpdf.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/
• Open Read –  https://www.openread.academy

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Jenny.AI – https://jenni.ai/ (20% off with code ANDY20)
• Yomu – https://www.yomu.ai
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• PaperPal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Best free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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A free, AI-powered research tool for scientific literature

• Meredith Clausen
• Metamorphic Rock

New & Improved API for Developers

Introducing semantic reader in beta.

Stay Connected With Semantic Scholar Sign Up What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.

Analyze research papers at superhuman speed

Search for research papers, get one sentence abstract summaries, select relevant papers and search for more like them, extract details from papers into an organized table.

Find themes and concepts across many papers

Don't just take our word for it.

Tons of features to speed up your research

Upload your own pdfs, orient with a quick summary, view sources for every answer, ask questions to papers, research for the machine intelligence age, pick a plan that's right for you, get in touch, enterprise and institutions, custom pricing, common questions. great answers., how do researchers use elicit.

Over 2 million researchers have used Elicit. Researchers commonly use Elicit to:

• Speed up literature review
• Find papers they couldn’t find elsewhere
• Automate systematic reviews and meta-analyses
• Learn about a new domain

Elicit tends to work best for empirical domains that involve experiments and concrete results. This type of research is common in biomedicine and machine learning.

What is Elicit not a good fit for?

Elicit does not currently answer questions or surface information that is not written about in an academic paper. It tends to work less well for identifying facts (e.g. “How many cars were sold in Malaysia last year?”) and theoretical or non-empirical domains.

What types of data can Elicit search over?

Elicit searches across 125 million academic papers from the Semantic Scholar corpus, which covers all academic disciplines. When you extract data from papers in Elicit, Elicit will use the full text if available or the abstract if not.

How accurate are the answers in Elicit?

A good rule of thumb is to assume that around 90% of the information you see in Elicit is accurate. While we do our best to increase accuracy without skyrocketing costs, it’s very important for you to check the work in Elicit closely. We try to make this easier for you by identifying all of the sources for information generated with language models.

What is Elicit Plus?

Elicit Plus is Elicit's subscription offering, which comes with a set of features, as well as monthly credits. On Elicit Plus, you may use up to 12,000 credits a month. Unused monthly credits do not carry forward into the next month. Plus subscriptions auto-renew every month.

What are credits?

Elicit uses a credit system to pay for the costs of running our app. When you run workflows and add columns to tables it will cost you credits. When you sign up you get 5,000 credits to use. Once those run out, you'll need to subscribe to Elicit Plus to get more. Credits are non-transferable.

How can you get in contact with the team?

Please email us at [email protected] or post in our Slack community if you have feedback or general comments! We log and incorporate all user comments. If you have a problem, please email [email protected] and we will try to help you as soon as possible.

What happens to papers uploaded to Elicit?

When you upload papers to analyze in Elicit, those papers will remain private to you and will not be shared with anyone else.

How accurate is Elicit?

Training our models on specific tasks, searching over academic papers, making it easy to double-check answers, save time, think more. try elicit for free..

• Research Guide
• Academic Writing
• Reference Management
• Data Visualization

5 AI Tools for Interacting with Research Papers

In this comprehensive blog post, I will delve into 5 AI tools designed and developed to facilitate interactions with research papers.

Artificial intelligence (AI) is rapidly transforming the way we research and learn. In the field of academic research, AI is being used to develop tools that help researchers find, understand, and cite research papers more effectively.

However, reading and interacting with research papers can be a daunting task, especially for those who are new to the field. Fortunately, there are many AI-backed tools available that make conversing with research papers more accessible and efficient than ever before.

Here begins the first part (Part-I) of  three-part blog series about the AI tools for interacting with research papers.

If you want to make your academic paper more interactive, you have come to the right place. The purpose of this blog post is to provide insights into a collection of 5 AI-powered tools for interacting with research papers.

Here are 5 AI tools for conversing with academic research papers.

No. #1 ChatPDF

ChatPDF is an AI tool that allows you to extract relevant information from your paper. The user-friendly web application lets you convert your research paper into an interactive chatbot in just a few minutes.

Using ChatPDF is as simple as uploading your research paper and customizing your chatbot.You do not need any coding experience or technical skills to do the same.

This tool is available to anyone, regardless of their technical skills. The AI-backed tool can help academic researchers to extract relevant information from PDF  academic papers.

This handy tool transforms the text within a paper into a format that is simple to search and analyze. This is a useful tool for researchers who need to quickly find specific information in a research paper.

In an earlier blog , I demonstrated elaborately how to use the ChatPDF to turn your academic paper into an interactive chatbot.

No. #2  Humata AI

Hum ata AI is an AI-powered tool that helps you research, learn, and create faster. It can summarize research papers, answer your questions about your paper, and generate new writing based on your documents.

You can access the AI-driven tool by entering the URL ( https://www.humata.ai ) into your browser’s address bar.

Upon accessing the website, the following page will be displayed:

After setting up your account, you can proceed to upload the research paper (PDF format) you want to analyze. Now, you locate the paper on your local machine and easily drag and drop it into the designated section on the Hum ata platform.

Once you have uploaded the paper, the sophisticated AI algorithm s will start analyzing and comprehending the content of your paper. It will take few seconds to process the paper.

Hum ata is based on OpenAI’s ChatGPT , and it can be used to interact with a variety of files, including PDFs, word documents, and spreadsheets.

Once the processing is finished, you can interact with its chatbot. It is located in the left pane of the interface.

The AI chatbot will prompt ly furnish you with clear and comprehensible answers in real-time.

Hum ata AI helps you to answer hard questions related to your research papers. It also summarizes long papers and extracting key points.

You can visit their website  here  to learn more about Hum ata AI and its features.

Overall, Hum ata AI is a powerful tool that leverages AI technology to assist users in various aspects of academic research, data analysis , and document management. It offers features that enhance efficiency, provide valuable insights, and simplify complex information.

No. #3 Perplexity AI

Have you ever wished you could chat with any scholarly content and ask questions about it in natural language? Well, now you can with Perplexity AI, a new AI chat tool that acts as an extremely powerful search engine.

Perplexity AI is an AI-powered tool that  lets you answer your questions in a comprehensive and informative way. It uses large language models and search engines to achieve this, allowing it to provide answers to a wide range of questions.

It is capable of understanding natural language inputs, as well as providing answers to more specific questions.

Perplexity AI is a web crawler that uses machine learning to generate general answers to your queries and then offer a series of website links. The links are to websites that the AI thinks are relevant to your query.

Perplexity AI offers a seamless experience by allowing you to ask any question using simple, everyday language. The beauty of this tool lies in its ability to provide you with comprehensive and informative answers.

It shows you the sources it used to answer your questions and encourages follow-up inquiries.

Sharing your questions and answers with others,  the tool promotes collaborative learning

You can use either of these two methods to access the AI-driven tool:

• Access the Perplexity website

2. Setup the Perplexity Chrome Extension

Perplexity AI is free to use and available on the web and as an app for iPhone users. To use Perplexity AI, you need to visit their website  here  or download their app  here .

You can enter your question in the box and the AI-assisted tool will provide you with an answer based on the ChatGPT .

In a previous blog , I demonstrated elaborately on how to use the perplexity AI search engine tool for academic research.

The Perplexity AI is a powerful tool that can help you find relevant information on any topic quickly and accurately.

Besides, you can use the Google Chrome extension to ask contextual questions about the website you are visiting.

No. #4  ChatDOC

Have you ever wished you could chat with any document and ask questions about it in natural language? Well, now you can with ChatDOC, a new AI tool that acts as an AI-powered file-reading assistant.

ChatDOc allows you chat with any paper and get instant answers with cited sources. It is a handy file-reading assistant powered by ChatGPT . 

It is great at quickly pulling out, finding, and summarizing information from various document formats—like .pdf, .docx, .md, and even scanned files.

ChatDOC is free to use and available on the web and as an app for Android users.

You can upload or paste any source of information into the tool and start asking questions. ChatDOC will provide you with responses based on the source and the ChatGPT model parameters.

Users can ask questions and get instant answers from ChatDOc, which can save time and effort. ChatDOc provides cited sources for its answers, which can help users verify the accuracy of the information.

With its versatile abilities, ChatDOC becomes an invaluable tool for efficiently analyzing documents and capturing essential insights.

ChatDOc can be a useful tool for anyone who needs to quickly find information in a document. It can be especially helpful for researchers who need to read and analyze large amounts of text. 

ChatDOC is a powerful tool that can save time and effort for individuals who frequently read and analyze documents. It provides a convenient way to extract information, locate specific details, and summarize content, ultimately enhancing the reading and learning experience

No. #5 PDF.ai

PDF documents are a ubiquitous format for storing and sharing information. However, they can be difficult to interact with, especially if they are large or complex.

PDF.ai is an AI-powered tool that can help you interact with PDF documents in a variety of ways.

With PDF.ai, you can understand the content of a research paper and answer your questions about it in plain English.

This can be helpful if you need to quickly get the main points of your paper.

The state-of-the-art tool can extract tables and data from your article for analysis. Besides, the tool can summarize the data in a table or spreadsheet.

The online application translates the research paper into different languages. This can be beneficial if you read a PDF document in a language you are unfamiliar with and need to read it.

The tool allows you to convert PDF paper to other formats, such as Word, Excel, and PowerPoint. It is capable of handling OCR-scanned research papers   that was scanned from a hard copy.

Upon visiting their website, the first step is to complete the signup process. Following this, a confirmation email will be sent to your provided email address.

Once you confirm your registration through the email link, you can access the tool’s features.

Here is a snapshot depicting the interface for your paper uploading:

Upon uploading your paper, you can begin interacting with the paper. In my case, I used a paper on Bitcoin sourced from their website.

The window below will be visible to you:

It is a powerful tool helps you save time and improve your productivity when working with your research documents.

If you need an affordable and powerful AI-powered tool for interacting with your academic paper, consider using PDF.ai.

You can add the PDF.ai extension to your Google Chrome browser to start interacting effortlessly with any academic papers.

AI tools are essential for enhancing your research skills and knowledge. These tools help you interact with research papers using natural language queries. The AI-powered tools can provide you with summaries, answers, and insights as you read the scholarly content.

You can also use these tools for various tasks such as literature review , citation analysis, text generation, and text synthesis. They have different features, accuracy rates, and prices, but they all provide reliable and useful services for interacting with research papers.

I hope you enjoyed this blog post and found it helpful for your research needs. If you liked this post, please share it with your friends and colleagues who might also benefit from it. Stay tuned for more posts on AI tools and research in the future.

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How to use Google Gemini AI for Research Paper Reading

How to use Google Gemini AI to Read Research Paper

Research papers, the backbone of academic progress, often pose a formidable challenge with their intricate language and complex data. However, the future of research reading might be undergoing a transformative shift, thanks to Google’s revolutionary AI, Gemin i. In this article, iLovePhD explores how Gemini could potentially revolutionize the way we engage with scientific literature.

How Google Gemini AI Could Revolutionize Research Paper Reading

1. effortless inquiry.

Problem: Navigating through dense jargon and lengthy papers.

Solution: Gemini enables users to ask questions, extract vital information, and provide concise summaries . No more hours spent skimming – ask and receive.

2. Visual Clarity

Problem: Difficulty in interpreting complex figures and tables.

Solution: Gemini decodes visual elements, making data visualization more accessible. Simply request a specific visualization, and Gemini generates it for you.

3. Code Generation

Problem: Tedious process of reproducing experiments through coding.

Solution: Gemini analyzes the paper, extracts data, and generates the code for experiment replication. Effortless verification with just a few clicks.

4. Text and Figure Integration

Problem: Isolated understanding of text and figures.

Solution: Gemini allows users to reference figures while asking questions, fostering a holistic comprehension of research. It bridges the gap between textual information and visual representations.

5. Collaboration Enhancement

Problem: Collaboration hurdles in sharing and building upon research.

Solution: Gemini facilitates collaboration by generating reports and presentations summarizing key points. It also suggests related research, streamlining the exploration of relevant themes.

While Gemini’s access is currently limited, the potential it holds is groundbreaking. Regardless of their field, researchers may soon benefit from an AI companion that enhances efficiency and understanding in navigating the vast landscape of scientific literature.

This AI-driven future could unlock new realms of knowledge, propelling scientific progress to unprecedented heights. Stay tuned for the unfolding chapters of this transformative journey – the era of exploration and enlightenment in research might be closer than we think, thanks to Gemini. Are you ready to embrace the future of research reading?

Useful Terms:

• Research Reading AI
• Gemini AI for Research
• Scientific Literature Transformation
• Google Gemini Revolution
• AI in Academic Reading
• Future of Research Papers
• Enhanced Research Comprehension
• Gemini AI Features
• Academic Collaboration with AI
• Revolutionizing Scientific Discovery
• Google’s Gemini Impact
• Simplified Research Paper Analysis
• AI-driven Research Insights
• Visualizing Scientific Data with Gemini
• Collaborative Research with AI
• Research Paper Efficiency Boost
• Gemini AI for Researchers
• Academic Progress with AI
• Unlocking Knowledge with Gemini
• Navigating Scientific Literature with AI
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• Artificial intelligence

A new use for AI: summarizing scientific research for seven-year-olds

Tl;dr papers shows ai’s potential to condense research.

By James Vincent , a senior reporter who has covered AI, robotics, and more for eight years at The Verge.

Share this story

Academic writing often has a reputation for being hard to follow, but what if you could use machine learning to summarize arguments in scientific papers so that even a seven-year-old could understand them? That’s the idea behind tl;dr papers — a project that leverages recent advances in AI language processing to simplify science.

Work on the site began two years ago by university friends Yash Dani and Cindy Wu as a way to “learn more about software development,” Dani tells The Verge, but the service went viral on Twitter over the weekend when academics started sharing AI summaries of their research . The AI-generated results are sometimes inaccurate or simplified to the point of idiocy. But just as often, they are satisfyingly and surprisingly concise, cutting through academic jargon to deliver what could be mistaken for child-like wisdom.

Take this summary of a paper by Professor Michelle Ryan, director of the Global Institute for Women’s Leadership at the Australian National University. Ryan has written on the concept of the “ glass cliff ,” a form of gender discrimination in which women are placed in leadership roles at times when institutions are at their greatest risk of failure. The AI summary of her work? “The glass cliff is a place where a lot of women get put. It’s a bad place to be.”

“It is just excellent,” as Ryan put it.

Ryan tells The Verge the summary was “accurate and pithy,” though it did elide a lot of nuances around the concept. In part, this is because of a crucial caveat: tl;dr papers only analyzes the abstract of a scientific paper, which is itself a condensed version of a researcher’s argument. (Being able to condense an entire paper would be a much greater challenge, though it’s something machine learning researchers are already working on.)

Ryan says that although tl;dr papers is undoubtedly a very fun tool, it also offers “a good illustration of what good science communication should look like.” “I think many of us could write in a way that is more reader-friendly,” she says. “And the target audience of a second-grader is a good place to start.”

Zane Griffin Talley Cooper, a PhD candidate at the Annenberg School for Communication at the University of Pennsylvania, described the AI summaries as “refreshingly transparent.” He used the site to condense a paper he’d written on “ data peripheries ,” which traces the physical history of materials essential to big data infrastructure. Or, as tl;dr papers put it:

“Big data is stored on hard disk drives. These hard disk drives are made of very small magnets. The magnets are mined out of the ground.“

Cooper says although the tool is a “joke on the surface,” systems like this could have serious applications in teaching and study. AI summarizers could be used by students as a way into complex papers, or they could be incorporated into online journals, automatically producing simplified abstracts for public consumption. “Of course,” says Cooper, this should be only done “if framed properly and with discussion of limitations and what it means (both practically and ethically) to use machine learning as a writing tool.”

AI language tools have been incorporated into software from Microsoft and Google

These limitations are still being explored by the companies that make these AI systems, even as the software is incorporated into ever-more mainstream tools. tl;dr papers itself was run on GPT-3, which is one of the best-known AI writing tools and is made by OpenAI, a combined research lab and commercial startup that works closely with Microsoft.

Microsoft has used GPT-3 and its ilk to build tools like autocomplete software for coders and recently began offering businesses access to the system as part of its cloud suite. The company says GPT-3 can be used to analyze the sentiment of text, generate ideas for businesses, and — yes — condense documents like the transcripts of meetings or email exchanges. And already, tools similar to GPT-3 are being used in popular services like Google’s Gmail and Docs, which offer AI-powered autocomplete features to users.

But the deployment of these AI-language systems is controversial. Time and time again, it’s been shown that these tools encode and amplify harmful language based on their training data (which is usually just vast volumes of text scraped off the internet). They repeat racist and sexist stereotypes and slurs and may be biased in more subtle ways, too.

A different set of worries stems from the inaccuracy of these systems. These tools only manipulate language on a statistical level: they have no human-equivalent understanding of what they’re “reading,” and this can lead to some very basic mistakes. In one notorious example that surfaced last year, Google search — which uses AI to summarize search topics — provided misleading medical advice to a query asking what to do if someone suffers a seizure. While last December, Amazon’s Alexa responded to a child asking for a fun challenge to do by telling them to touch a penny to the exposed prongs of a plug socket .

The specific danger to life posed by these scenarios is unusual, but they offer vivid illustrations of the structural weaknesses of these models. Jathan Sadowski, a senior research fellow in the Emerging Technologies Research Lab at Monash University, was another academic entertained by tl;dr papers’ summary of his research. He says AI systems like this should be handled with care, but they can serve a purpose in the right context.

“Maybe one day [this technology will] be so sophisticated that it can be this automated research assistant who is going and providing you a perfect, accurate, high quality annotated bibliography of academic literature while you sleep. But we are extremely far from that point right now,” Sadowski told The Verge . “The real, immediate usefulness from the tool is — first and foremost — as a novelty and joke. But more practically, I could see it as a creativity catalyst. Something that provides you this alien perspective on your work.”

“I could see it as a creativity catalyst. Something that provides you this alien perspective on your work.”

Sadowski says the summaries provided by tl;dr papers often have a sort of “accidental wisdom” to them — a byproduct, perhaps, of machine learning’s inability to fully understand language. In other scenarios, artists have used these AI tools to write books and music , and Sadowski says a machine’s perspective could be useful for academics who’ve burrowed too deep in their subject. “It can give you artificial distance from a thing you’ve spent a lot of time really close to, that way you can maybe see it in a different light,” he says.

In this way, AI systems like tl;dr papers might even find a place similar to tools designed to promote creativity. Take, for example, “Oblique Strategies,” a deck of cards created by Brian Eno and Peter Schmidt. It offers pithy advice to struggling artists like “ask your body” or “try faking it!” Are these words of wisdom imbued with deep intelligence? Maybe, maybe not. But their primary role is to provoke the reader into new patterns of thinking. AI could offer similar services, and indeed, some companies already sell AI creative writing assistants.

Unfortunately, although tl;dr papers has had a rapturous reception among the academic world, its time in the spotlight looks limited. After going viral this weekend, the website has been labeled “under maintenance,” and the site’s creators say they have no plans to maintain it in the future. (They also mention that other tools have been built that perform the same task .)

Dani told The Verge that tl;dr papers “was designed to be an experiment to see if we can make learning about science a little easier, more fun, and engaging.” He says: “I appreciate all of the attention the app has received and thank all of the people who have tried it out [but] given this was always intended to be an educational project, I plan to sunset tl;dr papers in the coming days to focus on exploring new things.”

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7 Best Free AI Tools For Research Paper Understanding

Remember that time you found an incredibly promising research paper, but it felt like it was written in another language?  The complicated terms, the long, winding sentences. 

it can make you want to give up before you even start.  But imagine if you had a super-smart friend who could break it all down for you – explain the ideas in simpler terms, point out the most important parts, and even show you how it connects to other stuff you already know. 

That’s basically what some amazing new tools out there can do! If your curious about this ai tools let me show you the list of the best free AI tools for research paper understanding.

SciSpace (formerly known as Scispace)

Connected papers, research rabbit, semantic scholar, explainpaper, tldr papers, faq: free ai tools for research paper understanding.

Summary: SciSpace is a powerful tool that simplifies the research process. It distills complex research papers into easy-to-understand summaries, provides clear explanations of key concepts, and helps you visualize connections within the broader field.

• Why It Stands Out: SciSpace uses its own machine learning models specifically trained on scientific text, making it highly accurate when analyzing research papers.
• Saves significant time understanding dense research
• Helps build a solid foundation in a specific field or topic
• Excellent for students and researchers in all disciplines
• May not be as effective with highly niche or emerging topics where AI training data is limited
• The free version has certain usage restriction.

Summary: Connected Papers is a visual tool that reveals connections between research papers. It helps you discover relevant research, identify seminal works in your field, and explore how a paper relates to a broader body of knowledge.

• Why It Stands Out: Connected Papers uses citation relationships between papers to build its visual maps. This lets you discover research you might have otherwise missed, even if it doesn’t directly cite the paper you started with.
• Quickly discover a wealth of relevant research
• Helps see the “big picture” of a research area
• Great for finding foundational papers or seeing where a specific idea developed
• Can be overwhelming if your starting paper is too broad
• Relies on citation data, so very recent papers may not be well-represented

Summary: Research Rabbit helps you navigate the vast landscape of academic literature. It creates visual maps connecting related papers, authors, and publications, letting you quickly explore different avenues of your research topic.

• Why It Stands Out:   Research Rabbit’s multiple search methods (paper, author, keywords) make it incredibly flexible. This allows you to start your exploration from different points and uncover hidden connections.
• Visually appealing interface helps you organize research
• Great for both broad exploration and targeted searches
• Lets you see how authors and institutions are connected
• Can have a slight learning curve to master all the features
• Might not be the best tool for highly specialized niche topics

Summary: Semantic Scholar is an AI-powered search engine designed specifically for academic research. It understands the context of research papers, helping you find the most relevant results and providing summaries that highlight key findings.

• Why It Stands Out: Semantic Scholar uses natural language processing to go beyond simple keyword matching. It can identify similar research papers, even if they don’t use the exact same terminology.
• Helps you break out of “keyword bubbles” to find truly relevant research
• Provides summaries and figures to quickly assess potential papers
• Excellent for staying current in your field and uncovering new discoveries
• Coverage within some specialized disciplines may still be developing
• Less flashy visual interface compared to some other tools

Summary: Explainpaper focuses on clarifying the often-dense language used in research papers. It allows you to highlight confusing sections of a paper and receive clear, plain-language explanations to aid your understanding.

• Why It Stands Out: Explainpaper’s direct approach to simplifying research paper text sets it apart.  It’s ideal for tackling specific jargon or complex sentence structures.
• Provides targeted help when you’re stuck on a specific concept or phrase
• The ability to ask questions about the paper adds to its usefulness
• Great for those who learn well through direct explanations
• Requires you to first identify the parts you don’t understand
• May not be as helpful for broader understanding of a paper’s entire scope

INK AI Review: Is This Worth Your Money?

Summary: TLDR Papers aims to deliver exactly what its name implies: extremely concise summaries of research papers. It gives you the most important findings in a few sentences, perfect for getting the gist of a paper quickly.

• Why It Stands Out: TLDR Papers is focused on efficiency. If your main goal is to know whether a paper is worth a deep dive,  it’s the perfect tool for the job.
• Quickly skim through a lot of papers and weed out the less relevant ones
• Excellent for getting a sense of the latest research trends
• Great for those with limited time who still want to stay informed
• Doesn’t replace reading the full paper if you need in-depth understanding
• Summaries may sometimes oversimplify complex findings

Summary: Elicit is a research tool that helps you ask the right questions about academic papers. It uses natural language processing to analyze a paper and generate thought-provoking questions to deepen your understanding.

• Why It Stands Out: Elicit goes beyond summarization and focuses on critical thinking. It encourages you to engage actively with the research, promoting deeper learning.
• Helps uncover hidden assumptions or implications in a paper
• Great for students learning to analyze research critically
• Encourages a more active reading approach
• Requires effort on your part to answer the generated questions thoroughly
• Might be less useful for highly descriptive papers where critical analysis is less central

Why is it so hard to understand some research papers?

Research papers are often written for a specialized audience, using technical terms and complex sentence structures that can be overwhelming for non-experts. This can be frustrating when you find a promising paper but struggle to grasp its significance.

How can AI tools help me understand research papers better?

AI tools designed for research paper analysis can be incredibly helpful. They can distill complex papers into easy-to-understand summaries, break down technical jargon into plain language, and even reveal connections between the paper and the broader field of research. It’s like having a knowledgeable friend guide you through the paper.

Are there good free AI tools available for this?

Yes! Several excellent free AI tools can help you tackle research papers. Some popular choices include SciSpace, Connected Papers, Research Rabbit, Semantic Scholar, Explainpaper, TLDR Papers, and Elicit. Each has its own strengths, so explore to find what works best for you.

Can AI tools replace my own understanding of research?

AI tools are incredibly powerful, but they are meant to support your understanding, not replace it. They can save you time, clarify confusing points, and uncover connections you might miss. However, you’ll still need to apply your critical thinking skills to fully analyze and integrate research findings into your own work.

How to Read Research Papers: A Pragmatic Approach for ML Practitioners

Is it necessary for data scientists or machine-learning experts to read research papers?

The short answer is yes. And don’t worry if you lack a formal academic background or have only obtained an undergraduate degree in the field of machine learning.

Reading academic research papers may be intimidating for individuals without an extensive educational background. However, a lack of academic reading experience should not prevent Data scientists from taking advantage of a valuable source of information and knowledge for machine learning and AI development .

This article provides a hands-on tutorial for data scientists of any skill level to read research papers published in academic journals such as NeurIPS , JMLR , ICML, and so on.

Before diving wholeheartedly into how to read research papers, the first phases of learning how to read research papers cover selecting relevant topics and research papers.

Step 1: Identify a topic

The domain of machine learning and data science is home to a plethora of subject areas that may be studied. But this does not necessarily imply that tackling each topic within machine learning is the best option.

Although generalization for entry-level practitioners is advised, I’m guessing that when it comes to long-term machine learning, career prospects, practitioners, and industry interest often shifts to specialization.

Identifying a niche topic to work on may be difficult, but good. Still, a rule of thumb is to select an ML field in which you are either interested in obtaining a professional position or already have experience.

Deep Learning is one of my interests, and I’m a Computer Vision Engineer that uses deep learning models in apps to solve computer vision problems professionally. As a result, I’m interested in topics like pose estimation, action classification, and gesture identification.

Based on roles, the following are examples of ML/DS occupations and related themes to consider.

For this article, I’ll select the topic Pose Estimation to explore and choose associated research papers to study.

Step 2: Finding research papers

One of the most excellent tools to use while looking at machine learning-related research papers, datasets, code, and other related materials is PapersWithCode .

We use the search engine on the PapersWithCode website to get relevant research papers and content for our chosen topic, “Pose Estimation.” The following image shows you how it’s done.

The search results page contains a short explanation of the searched topic, followed by a table of associated datasets, models, papers, and code. Without going into too much detail, the area of interest for this use case is the “Greatest papers with code”. This section contains the relevant papers related to the task or topic. For the purpose of this article, I’ll select the DensePose: Dense Human Pose Estimation In The Wild .

Step 3: First pass (gaining context and understanding)

At this point, we’ve selected a research paper to study and are prepared to extract any valuable learnings and findings from its content.

It’s only natural that your first impulse is to start writing notes and reading the document from beginning to end, perhaps taking some rest in between. However, having a context for the content of a study paper is a more practical way to read it. The title, abstract, and conclusion are three key parts of any research paper to gain an understanding.

The goal of the first pass of your chosen paper is to achieve the following:

• Assure that the paper is relevant.
• Obtain a sense of the paper’s context by learning about its contents, methods, and findings.
• Recognize the author’s goals, methodology, and accomplishments.

The title is the first point of information sharing between the authors and the reader. Therefore, research papers titles are direct and composed in a manner that leaves no ambiguity.

The research paper title is the most telling aspect since it indicates the study’s relevance to your work. The importance of the title is to give a brief perception of the paper’s content.

In this situation, the title is “DensePose: Dense Human Pose Estimation in the Wild.” This gives a broad overview of the work and implies that it will look at how to provide pose estimations in environments with high levels of activity and realistic situations properly.

The abstract portion gives a summarized version of the paper. It’s a short section that contains 300-500 words and tells you what the paper is about in a nutshell. The abstract is a brief text that provides an overview of the article’s content, researchers’ objectives, methods, and techniques.

When reading an abstract of a machine-learning research paper, you’ll typically come across mentions of datasets, methods, algorithms, and other terms. Keywords relevant to the article’s content provide context. It may be helpful to take notes and keep track of all keywords at this point.

For the paper: “ DensePose: Dense Human Pose Estimation In The Wild “, I identified in the abstract the following keywords: pose estimation, COCO dataset, CNN, region-based models, real-time.

It’s not uncommon to experience fatigue when reading the paper from top to bottom at your first initial pass, especially for Data Scientists and practitioners with no prior advanced academic experience. Although extracting information from the later sections of a paper might seem tedious after a long study session, the conclusion sections are often short. Hence reading the conclusion section in the first pass is recommended.

The conclusion section is a brief compendium of the work’s author or authors and/or contributions and accomplishments and promises for future developments and limitations.

Before reading the main content of a research paper, read the conclusion section to see if the researcher’s contributions, problem domain, and outcomes match your needs.

Following this particular brief first pass step enables a sufficient understanding and overview of the research paper’s scope and objectives, as well as a context for its content. You’ll be able to get more detailed information out of its content by going through it again with laser attention.

Step 4: Second pass (content familiarization)

Content familiarization is a process that’s relevant to the initial steps. The systematic approach to reading the research paper presented in this article. The familiarity process is a step that involves the introduction section and figures within the research paper.

As previously mentioned, the urge to plunge straight into the core of the research paper is not required because knowledge acclimatization provides an easier and more comprehensive examination of the study in later passes.

Introduction

Introductory sections of research papers are written to provide an overview of the objective of the research efforts. This objective mentions and explains problem domains, research scope, prior research efforts, and methodologies.

It’s normal to find parallels to past research work in this area, using similar or distinct methods. Other papers’ citations provide the scope and breadth of the problem domain, which broadens the exploratory zone for the reader. Perhaps incorporating the procedure outlined in Step 3 is sufficient at this point.

Another aspect of the benefit provided by the introduction section is the presentation of requisite knowledge required to approach and understand the content of the research paper.

Graph, diagrams, figures

Illustrative materials within the research paper ensure that readers can comprehend factors that support problem definition or explanations of methods presented. Commonly, tables are used within research papers to provide information on the quantitative performances of novel techniques in comparison to similar approaches.

Generally, the visual representation of data and performance enables the development of an intuitive understanding of the paper’s context. In the Dense Pose paper mentioned earlier, illustrations are used to depict the performance of the author’s approach to pose estimation and create. An overall understanding of the steps involved in generating and annotating data samples.

In the realm of deep learning, it’s common to find topological illustrations depicting the structure of artificial neural networks. Again this adds to the creation of intuitive understanding for any reader. Through illustrations and figures, readers may interpret the information themselves and gain a fuller perspective of it without having any preconceived notions about what outcomes should be.

Step 5: Third pass (deep reading)

The third pass of the paper is similar to the second, though it covers a greater portion of the text. The most important thing about this pass is that you avoid any complex arithmetic or technique formulations that may be difficult for you. During this pass, you can also skip over any words and definitions that you don’t understand or aren’t familiar with. These unfamiliar terms, algorithms, or techniques should be noted to return to later.

During this pass, your primary objective is to gain a broad understanding of what’s covered in the paper. Approach the paper, starting again from the abstract to the conclusion, but be sure to take intermediary breaks in between sections. Moreover, it’s recommended to have a notepad, where all key insights and takeaways are noted, alongside the unfamiliar terms and concepts.

The Pomodoro Technique is an effective method of managing time allocated to deep reading or study. Explained simply, the Pomodoro Technique involves the segmentation of the day into blocks of work, followed by short breaks.

What works for me is the 50/15 split, that is, 50 minutes studying and 15 minutes allocated to breaks. I tend to execute this split twice consecutively before taking a more extended break of 30 minutes. If you are unfamiliar with this time management technique, adopt a relatively easy division such as 25/5 and adjust the time split according to your focus and time capacity.

Step 6: Forth pass (final pass)

The final pass is typically one that involves an exertion of your mental and learning abilities, as it involves going through the unfamiliar terms, terminologies, concepts, and algorithms noted in the previous pass. This pass focuses on using external material to understand the recorded unfamiliar aspects of the paper.

In-depth studies of unfamiliar subjects have no specified time length, and at times efforts span into the days and weeks. The critical factor to a successful final pass is locating the appropriate sources for further exploration.

 Unfortunately, there isn’t one source on the Internet that provides the wealth of information you require. Still, there are multiple sources that, when used in unison and appropriately, fill knowledge gaps. Below are a few of these resources.

• The Machine Learning Subreddit
• The Deep Learning Subreddit
• PapersWithCode
• Top conferences such as NIPS , ICML , ICLR
• Research Gate
• Machine Learning Apple

The Reference sections of research papers mention techniques and algorithms. Consequently, the current paper either draws inspiration from or builds upon, which is why the reference section is a useful source to use in your deep reading sessions.

Step 7: Summary (optional)

In almost a decade of academic and professional undertakings of technology-associated subjects and roles, the most effective method of ensuring any new information learned is retained in my long-term memory through the recapitulation of explored topics. By rewriting new information in my own words, either written or typed, I’m able to reinforce the presented ideas in an understandable and memorable manner.

To take it one step further, it’s possible to publicize learning efforts and notes through the utilization of blogging platforms and social media. An attempt to explain the freshly explored concept to a broad audience, assuming a reader isn’t accustomed to the topic or subject, requires understanding topics in intrinsic details.

Undoubtedly, reading research papers for novice Data Scientists and ML practitioners can be daunting and challenging; even seasoned practitioners find it difficult to digest the content of research papers in a single pass successfully.

The nature of the Data Science profession is very practical and involved. Meaning, there’s a requirement for its practitioners to employ an academic mindset, more so as the Data Science domain is closely associated with AI, which is still a developing field.

To summarize, here are all of the steps you should follow to read a research paper:

• Identify A Topic.
• Finding associated Research Papers
• Read title, abstract, and conclusion to gain a vague understanding of the research effort aims and achievements.
• Familiarize yourself with the content by diving deeper into the introduction; including the exploration of figures and graphs presented in the paper.
• Use a deep reading session to digest the main content of the paper as you go through the paper from top to bottom.
• Explore unfamiliar terms, terminologies, concepts, and methods using external resources.
• Summarize in your own words essential takeaways, definitions, and algorithms.

Thanks for reading!

Related resources

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• 31 October 2022

Could AI help you to write your next paper?

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Matthew Hutson is a science writer based in New York City.

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You know that text autocomplete function that makes your smartphone so convenient — and occasionally frustrating — to use? Well, now tools based on the same idea have progressed to the point that they are helping researchers to analyse and write scientific papers, generate code and brainstorm ideas.

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doi: https://doi.org/10.1038/d41586-022-03479-w

Chen, M. et al. Preprint at https://arxiv.org/abs/2107.03374 (2021).

Cachola, I., Lo, K., Cohan, A. & Weld, D. S. In Findings of the Association for Computational Linguistics 4766–4777 (2020).

Dasigi, P. et al. In Proc. 2021 Conference of the North American Chapter of the Association of Computational Linguistics 4599–4610 (2021).

Beltagy, I., Peters, M. E. & Cohan, A. Preprint at https://arxiv.org/abs/2004.05150 (2020).

Hope, T. et al. Preprint at https://arxiv.org/abs/2205.02007 (2022).

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AI-assisted writing is quietly booming in academic journals—here's why that's OK

by Julian Koplin, The Conversation

If you search Google Scholar for the phrase " as an AI language model ," you'll find plenty of AI research literature and also some rather suspicious results. For example, one paper on agricultural technology says,

"As an AI language model, I don't have direct access to current research articles or studies. However, I can provide you with an overview of some recent trends and advancements …"

Obvious gaffes like this aren't the only signs that researchers are increasingly turning to generative AI tools when writing up their research. A recent study examined the frequency of certain words in academic writing (such as "commendable," "meticulously" and "intricate"), and found they became far more common after the launch of ChatGPT—so much so that 1% of all journal articles published in 2023 may have contained AI-generated text.

(Why do AI models overuse these words? There is speculation it's because they are more common in English as spoken in Nigeria, where key elements of model training often occur.)

The aforementioned study also looks at preliminary data from 2024, which indicates that AI writing assistance is only becoming more common. Is this a crisis for modern scholarship, or a boon for academic productivity?

Who should take credit for AI writing?

Many people are worried by the use of AI in academic papers. Indeed, the practice has been described as " contaminating " scholarly literature.

Some argue that using AI output amounts to plagiarism. If your ideas are copy-pasted from ChatGPT, it is questionable whether you really deserve credit for them.

But there are important differences between "plagiarizing" text authored by humans and text authored by AI. Those who plagiarize humans' work receive credit for ideas that ought to have gone to the original author.

By contrast, it is debatable whether AI systems like ChatGPT can have ideas, let alone deserve credit for them. An AI tool is more like your phone's autocomplete function than a human researcher.

The question of bias

Another worry is that AI outputs might be biased in ways that could seep into the scholarly record. Infamously, older language models tended to portray people who are female, black and/or gay in distinctly unflattering ways, compared with people who are male, white and/or straight.

This kind of bias is less pronounced in the current version of ChatGPT.

However, other studies have found a different kind of bias in ChatGPT and other large language models : a tendency to reflect a left-liberal political ideology.

Any such bias could subtly distort scholarly writing produced using these tools.

The hallucination problem

The most serious worry relates to a well-known limitation of generative AI systems: that they often make serious mistakes.

For example, when I asked ChatGPT-4 to generate an ASCII image of a mushroom, it provided me with the following output.

It then confidently told me I could use this image of a "mushroom" for my own purposes.

These kinds of overconfident mistakes have been referred to as "AI hallucinations" and " AI bullshit ." While it is easy to spot that the above ASCII image looks nothing like a mushroom (and quite a bit like a snail), it may be much harder to identify any mistakes ChatGPT makes when surveying scientific literature or describing the state of a philosophical debate.

Unlike (most) humans, AI systems are fundamentally unconcerned with the truth of what they say. If used carelessly, their hallucinations could corrupt the scholarly record.

Should AI-produced text be banned?

One response to the rise of text generators has been to ban them outright. For example, Science—one of the world's most influential academic journals—disallows any use of AI-generated text .

I see two problems with this approach.

The first problem is a practical one: current tools for detecting AI-generated text are highly unreliable. This includes the detector created by ChatGPT's own developers, which was taken offline after it was found to have only a 26% accuracy rate (and a 9% false positive rate ). Humans also make mistakes when assessing whether something was written by AI.

It is also possible to circumvent AI text detectors. Online communities are actively exploring how to prompt ChatGPT in ways that allow the user to evade detection. Human users can also superficially rewrite AI outputs, effectively scrubbing away the traces of AI (like its overuse of the words "commendable," "meticulously" and "intricate").

The second problem is that banning generative AI outright prevents us from realizing these technologies' benefits. Used well, generative AI can boost academic productivity by streamlining the writing process. In this way, it could help further human knowledge. Ideally, we should try to reap these benefits while avoiding the problems.

The problem is poor quality control, not AI

The most serious problem with AI is the risk of introducing unnoticed errors, leading to sloppy scholarship. Instead of banning AI, we should try to ensure that mistaken, implausible or biased claims cannot make it onto the academic record.

After all, humans can also produce writing with serious errors, and mechanisms such as peer review often fail to prevent its publication.

We need to get better at ensuring academic papers are free from serious mistakes, regardless of whether these mistakes are caused by careless use of AI or sloppy human scholarship. Not only is this more achievable than policing AI usage, it will improve the standards of academic research as a whole.

This would be (as ChatGPT might say) a commendable and meticulously intricate solution.

Provided by The Conversation

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OpenAI’s new GPT-4o lets people interact using voice or video in the same model

The company’s new free flagship “omnimodel” looks like a supercharged version of assistants like Siri or Alexa.

• James O'Donnell archive page

OpenAI just debuted GPT-4o, a new kind of AI model that you can communicate with in real time via live voice conversation, video streams from your phone, and text. The model is rolling out over the next few weeks and will be free for all users through both the GPT app and the web interface, according to the company. Users who subscribe to OpenAI’s paid tiers, which start at $20 per month, will be able to make more requests. 

OpenAI CTO Mira Murati led the live demonstration of the new release one day before Google is expected to unveil its own AI advancements at its flagship I/O conference on Tuesday, May 14. 

GPT-4 offered similar capabilities, giving users multiple ways to interact with OpenAI’s AI offerings. But it siloed them in separate models, leading to longer response times and presumably higher computing costs. GPT-4o has now merged those capabilities into a single model, which Murati called an “omnimodel.” That means faster responses and smoother transitions between tasks, she said.

The result, the company’s demonstration suggests, is a conversational assistant much in the vein of Siri or Alexa but capable of fielding much more complex prompts.

“We’re looking at the future of interaction between ourselves and the machines,” Murati said of the demo. “We think that GPT-4o is really shifting that paradigm into the future of collaboration, where this interaction becomes much more natural.”

Barret Zoph and Mark Chen, both researchers at OpenAI, walked through a number of applications for the new model. Most impressive was its facility with live conversation. You could interrupt the model during its responses, and it would stop, listen, and adjust course. 

OpenAI showed off the ability to change the model’s tone, too. Chen asked the model to read a bedtime story “about robots and love,” quickly jumping in to demand a more dramatic voice. The model got progressively more theatrical until Murati demanded that it pivot quickly to a convincing robot voice (which it excelled at). While there were predictably some short pauses during the conversation while the model reasoned through what to say next, it stood out as a remarkably naturally paced AI conversation. 

The model can reason through visual problems in real time as well. Using his phone, Zoph filmed himself writing an algebra equation (3 x + 1 = 4) on a sheet of paper, having GPT-4o follow along. He instructed it not to provide answers, but instead to guide him much as a teacher would.

“The first step is to get all the terms with x on one side,” the model said in a friendly tone. “So, what do you think we should do with that plus one?”

Like previous generations of GPT, GPT-4o will store records of users’ interactions with it, meaning the model “has a sense of continuity across all your conversations,” according to Murati. Other new highlights include live translation, the ability to search through your conversations with the model, and the power to look up information in real time. 

As is the nature of a live demo, there were hiccups and glitches. GPT-4o’s voice might jump in awkwardly during the conversation. It appeared to comment on one of the presenters’ outfits even though it wasn’t asked to. But it recovered well when the demonstrators told the model it had erred. It seems to be able to respond quickly and helpfully across several mediums that other models have not yet merged as effectively. 

Previously, many of OpenAI’s most powerful features, like reasoning through image and video, were behind a paywall. GPT-4o marks the first time they’ll be opened up to the wider public, though it’s not yet clear how many interactions you’ll be able to have with the model before being charged. OpenAI says paying subscribers will “continue to have up to five times the capacity limits of our free users.” 

Additional reporting by Will Douglas Heaven.

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• Will Douglas Heaven archive page

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• Melissa Heikkilä archive page

An AI startup made a hyperrealistic deepfake of me that’s so good it’s scary

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Title: a comprehensive survey of research towards ai-enabled unmanned aerial systems in pre-, active-, and post-wildfire management.

Abstract: Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although some of the existing survey papers have explored various learning-based approaches, a comprehensive review emphasizing the application of AI-enabled UAV systems and their subsequent impact on multi-stage wildfire management is notably lacking. This survey aims to bridge these gaps by offering a systematic review of the recent state-of-the-art technologies, highlighting the advancements of UAV systems and AI models from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.

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Google DeepMind’s Groundbreaking AI for Protein Structure Can Now Model DNA

Google spent much of the past year hustling to build its Gemini chatbot to counter ChatGPT , pitching it as a multifunctional AI assistant that can help with work tasks or the digital chores of personal life. More quietly, the company has been working to enhance a more specialized artificial intelligence tool that is already a must-have for some scientists.

AlphaFold , software developed by Google’s DeepMind AI unit to predict the 3D structure of proteins, has received a significant upgrade. It can now model other molecules of biological importance, including DNA, and the interactions between antibodies produced by the immune system and the molecules of disease organisms. DeepMind added those new capabilities to AlphaFold 3 in part through borrowing techniques from AI image generators.

“This is a big advance for us,” Demis Hassabis , CEO of Google DeepMind, told WIRED ahead of Wednesday’s publication of a paper on AlphaFold 3 in the science journal Nature . “This is exactly what you need for drug discovery: You need to see how a small molecule is going to bind to a drug, how strongly, and also what else it might bind to.”

AlphaFold 3 can model large molecules such as DNA and RNA, which carry genetic code, but also much smaller entities, including metal ions. It can predict with high accuracy how these different molecules will interact with one another, Google’s research paper claims.

The software was developed by Google DeepMind and Isomorphic labs, a sibling company under parent Alphabet working on AI for biotech that is also led by Hassabis. In January, Isomorphic Labs announced that it would work with Eli Lilly and Novartis on drug development.

AlphaFold 3 will be made available via the cloud for outside researchers to access for free, but DeepMind is not releasing the software as open source the way it did for earlier versions of AlphaFold. John Jumper, who leads the Google DeepMind team working on the software, says it could help provide a deeper understanding of how proteins interact and work with DNA inside the body. “How do proteins respond to DNA damage; how do they find, repair it?” Jumper says. “We can start to answer these questions.”

Understanding protein structures used to require painstaking work using electron microscopes and a technique called x-ray crystallography. Several years ago, academic research groups began testing whether deep learning , the technique at the heart of many recent AI advances, could predict the shape of proteins simply from their constituent amino acids, by learning from structures that had been experimentally verified.

In 2018, Google DeepMind revealed it was working on AI software called AlphaFold to accurately predict the shape of proteins. In 2020, AlphaFold 2 produced results accurate enough to set off a storm of excitement in molecular biology. A year later, the company released an open source version of AlphaFold for anyone to use, along with 350,000 predicted protein structures, including for almost every protein known to exist in the human body. In 2022 the company released more than 2 million protein structures.

Gabrielle Caplan

The latest AlphaFold’s ability to model different proteins was improved in part through an algorithm called a diffusion model that helps AI image generators like Dall-E and Midjourney create weird and sometimes photo-real imagery. The diffusion model inside AlphaFold 3 sharpens the molecular structures the software generates. The diffusion model is able to generate plausible protein structures based on patterns it picked up from analyzing a collection of verified protein structures, much as an image generator learns from real photographs how to render realistic-looking snapshots.

AlphaFold 3 is not perfect, though, and offers a color-coded confidence scale for its predictions. Areas of a protein structure colored blue indicate high confidence, while red areas show less certainty.

David Baker , a professor at the University of Washington who leads a group working on techniques for protein design, has competed with AlphaFold. In 2021, before DeepMind open sourced its creation, his team released an independent protein-structure prediction inspired by AlphaFold. His own lab recently released a diffusion model to help model a wider range of molecular structures, but he concedes that AlphaFold 3 is more capable. “The structure prediction performance of AlphaFold 3 is very impressive,” Baker says.

Baker adds that it is a shame that the source code for AlphaFold 3 has not been released to the scientific community.

Hassabis, who leads all of Alphabet’s AI initiatives, has long taken a special interest in the potential for AI to accelerate scientific research . But he says the latest techniques being developed for AlphaFold, a highly specialized AI system, could prove useful for building more general systems that aim to exceed human capabilities on many dimensions.

If AI programs like Google’s Gemini become a lot more capable over the next decade, he says, “you could imagine them using things like AlphaFold as tools, to achieve some other goal.”

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