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A free, AI-powered research tool for scientific literature

• Maryam Mirzakhani

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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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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, 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 in 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.

How can you get in contact with the team?

You can email us at [email protected] or post in our Slack community ! We log and incorporate all user comments, and will do our best to reply to every inquiry 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..

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10 Powerful AI Tools for Academic Research

• Serra Ardem

AI is no longer science fiction, but a powerful ally in the academic realm. With AI by their side, researchers can free themselves from the burden of tedious tasks, and push the boundaries of knowledge. However, they must use AI carefully and ethically, as these practices introduce new considerations regarding data integrity, bias mitigation, and the preservation of academic rigor.

In this blog, we will:

• Highlight the increasing role of AI in academic research
• List 10 best AI tools for academic research, with a focus on each one’s strengths
• Share 5 best practices on how to use AI tools for academic research

Let’s dig in…

The Role of AI in Academic Research

AI tools for academic research hold immense potential, as they can analyze massive datasets and identify complex patterns. These tools can assist in generating new research questions and hypotheses, navigate mountains of academic literature to find relevant information, and automate tedious tasks like data entry.

Let’s take a look at the benefits AI tools offer for academic research:

• Supercharged literature reviews: AI can sift through vast amounts of academic literature, and pinpoint relevant studies with far greater speed and accuracy than manual searches.
• Accelerated data analysis: AI tools can rapidly analyze large datasets and uncover intricate insights that might otherwise be overlooked, or time-consuming to identify manually.
• Enhanced research quality: Helping with grammar checking, citation formatting, and data visualization, AI tools can lead to a more polished and impactful final product.
• Automation of repetitive tasks: By automating routine tasks, AI can save researchers time and effort, allowing them to focus on more intellectually demanding tasks of their research.
• Predictive modeling and forecasting: AI algorithms can develop predictive models and forecasts, aiding researchers in making informed decisions and projections in various fields.
• Cross-disciplinary collaboration: AI fosters collaboration between researchers from different disciplines by facilitating communication through shared data analysis and interpretation.

Now let’s move on to our list of 10 powerful AI tools for academic research, which you can refer to for a streamlined, refined workflow. From formulating research questions to organizing findings, these tools can offer solutions for every step of your research.

1. HyperWrite

For: hypothesis generation

HyperWrite’s Research Hypothesis Generator is perfect for students and academic researchers who want to formulate clear and concise hypotheses. All you have to do is enter your research topic and objectives into the provided fields, and then the tool will let its AI generate a testable hypothesis. You can review the generated hypothesis, make any necessary edits, and use it to guide your research process.

Pricing: You can have a limited free trial, but need to choose at least the Premium Plan for additional access. See more on pricing here .

2. Semantic Scholar

For: literature review and management

With over 200 million academic papers sourced, Semantic Scholar is one of the best AI tools for literature review. Mainly, it helps researchers to understand a paper at a glance. You can scan papers faster with the TLDRs (Too Long; Didn’t Read), or generate your own questions about the paper for the AI to answer. You can also organize papers in your own library, and get AI-powered paper recommendations for further research.

Pricing: free

For: summarizing papers

Apparently, Elicit is a huge booster as its users save up to 5 hours per week. With a database of 125 million papers, the tool will enable you to get one-sentence, abstract AI summaries, and extract details from a paper into an organized table. You can also find common themes and concepts across many papers. Keep in mind that Elicit works best with empirical domains that involve experiments and concrete results, like biomedicine and machine learning.

Pricing: Free plan offers 5,000 credits one time. See more on pricing here .

For: transcribing interviews

Supporting 125+ languages, Maestra’s interview transcription software will save you from the tedious task of manual transcription so you can dedicate more time to analyzing and interpreting your research data. Just upload your audio or video file to the tool, select the audio language, and click “Submit”. Maestra will convert your interview into text instantly, and with very high accuracy. You can always use the tool’s built-in text editor to make changes, and Maestra Teams to collaborate with fellow researchers on the transcript.

Pricing: With the “Pay As You Go” plan, you can pay for the amount of work done. See more on pricing here .

5. ATLAS.ti

For: qualitative data analysis

Whether you’re working with interview transcripts, focus group discussions, or open-ended surveys, ATLAS.ti provides a set of tools to help you extract meaningful insights from your data. You can analyze texts to uncover hidden patterns embedded in responses, or create a visualization of terms that appear most often in your research. Plus, features like sentiment analysis can identify emotional undercurrents within your data.

Pricing: Offers a variety of licenses for different purposes. See more on pricing here .

6. Power BI

For: quantitative data analysis

Microsoft’s Power BI offers AI Insights to consolidate data from various sources, analyze trends, and create interactive dashboards. One feature is “Natural Language Query”, where you can directly type your question and get quick insights about your data. Two other important features are “Anomaly Detection”, which can detect unexpected patterns, and “Decomposition Tree”, which can be utilized for root cause analysis.

Pricing: Included in a free account for Microsoft Fabric Preview. See more on pricing here .

7. Paperpal

For: writing research papers

As a popular AI writing assistant for academic papers, Paperpal is trained and built on 20+ years of scholarly knowledge. You can generate outlines, titles, abstracts, and keywords to kickstart your writing and structure your research effectively. With its ability to understand academic context, the tool can also come up with subject-specific language suggestions, and trim your paper to meet journal limits.

Pricing: Free plan offers 5 uses of AI features per day. See more on pricing here .

For: proofreading

With Scribbr’s AI Proofreader by your side, you can make your academic writing more clear and easy to read. The tool will first scan your document to catch mistakes. Then it will fix grammatical, spelling and punctuation errors while also suggesting fluency corrections. It is really easy to use (you can apply or reject corrections with 1-click), and works directly in a DOCX file.

Pricing: The free version gives a report of your issues but does not correct them. See more on pricing here .

9. Quillbot

For: detecting AI-generated content

Want to make sure your research paper does not include AI-generated content? Quillbot’s AI Detector can identify certain indicators like repetitive words, awkward phrases, and an unnatural flow. It’ll then show a percentage representing the amount of AI-generated content within your text. The tool has a very user-friendly interface, and you can have an unlimited number of checks.

10. Lateral

For: organizing documents

Lateral will help you keep everything in one place and easily find what you’re looking for. 

With auto-generated tables, you can keep track of all your findings and never lose a reference. Plus, Lateral uses its own machine learning technology (LIP API) to make content suggestions. With its “AI-Powered Concepts” feature, you can name a Concept, and the tool will recommend relevant text across all your papers.

Pricing: Free version offers 500 Page Credits one-time. See more on pricing here .

How to Use AI Tools for Research: 5 Best Practices

Before we conclude our blog, we want to list 5 best practices to adopt when using AI tools for academic research. They will ensure you’re getting the most out of AI technology in your academic pursuits while maintaining ethical standards in your work.

• Always remember that AI is an enhancer, not a replacement. While it can excel at tasks like literature review and data analysis, it cannot replicate the critical thinking and creativity that define strong research. Researchers should leverage AI for repetitive tasks, but dedicate their own expertise to interpret results and draw conclusions.
• Verify results. Don’t take AI for granted. Yes, it can be incredibly efficient, but results still require validation to prevent misleading or inaccurate results. Review them thoroughly to ensure they align with your research goals and existing knowledge in the field.
• Guard yourself against bias. AI tools for academic research are trained on existing data, which can contain social biases. You must critically evaluate the underlying assumptions used by the AI model, and ask if they are valid or relevant to your research question. You can also minimize bias by incorporating data from various sources that represent diverse perspectives and demographics.
• Embrace open science. Sharing your AI workflow and findings can inspire others, leading to innovative applications of AI tools. Open science also promotes responsible AI development in research, as it fosters transparency and collaboration among scholars.
• Stay informed about the developments in the field. AI tools for academic research are constantly evolving, and your work can benefit from the recent advancements. You can follow numerous blogs and newsletters in the area ( The Rundown AI is a great one) , join online communities, or participate in workshops and training programs. Moreover, you can connect with AI researchers whose work aligns with your research interests.

Frequently Asked Questions

Is chatgpt good for academic research.

ChatGPT can be a valuable tool for supporting your academic research, but it has limitations. You can use it for brainstorming and idea generation, identifying relevant resources, or drafting text. However, ChatGPT can’t guarantee the information it provides is entirely accurate or unbiased. In short, you can use it as a starting point, but never rely solely on its output.

Can I use AI for my thesis?

Yes, but it shouldn’t replace your own work. It can help you identify research gaps, formulate a strong thesis statement, and synthesize existing knowledge to support your argument. You can always reach out to your advisor and discuss how you plan to use AI tools for academic research .

Can AI write review articles?

AI can analyze vast amounts of information and summarize research papers much faster than humans, which can be a big time-saver in the literature review stage. Yet it can struggle with critical thinking and adding its own analysis to the review. Plus, AI-generated text can lack the originality and unique voice that a human writer brings to a review.

Can professors detect AI writing?

Yes, they can detect AI writing in several ways. Software programs like Turnitin’s AI Writing Detection can analyze text for signs of AI generation. Furthermore, experienced professors who have read many student papers can often develop a gut feeling about whether a paper was written by a human or machine. However, highly sophisticated AI may be harder to detect than more basic versions.

Can I do a PhD in artificial intelligence?

Yes, you can pursue a PhD in artificial intelligence or a related field such as computer science, machine learning, or data science. Many universities worldwide offer programs where you can delve deep into specific areas like natural language processing, computer vision, and AI ethics. Overall, pursuing a PhD in AI can lead to exciting opportunities in academia, industry research labs, and tech companies.

This blog shared 10 powerful AI tools for academic research, and highlighted each tool’s specific function and strengths. It also explained the increasing role of AI in academia, and listed 5 best practices on how to adopt AI research tools ethically.

AI tools hold potential for even greater integration and impact on research. They are likely to become more interconnected, which can lead to groundbreaking discoveries at the intersection of seemingly disparate fields. Yet, as AI becomes more powerful, ethical concerns like bias and fairness will need to be addressed. In short, AI tools for academic research should be utilized carefully, with a keen awareness of their capabilities and limitations.

About Serra Ardem

Serra Ardem is a freelance writer and editor based in Istanbul. For the last 8 years, she has been collaborating with brands and businesses to tell their unique story and develop their verbal identity.

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Top 10 AI Tools For Academics: Level Up Your Research

If you aren’t using AI to augment your academic research in 2023, you are wasting a lot of time – time that you could free up from grunt work and invest in the more interesting stuff. 

AI tools have proliferated since the launch of GPT 3 and beyond. The sudden and simultaneous rise of so many consumable AI-powered tools has quickly muddied the waters and made it tiresome if not impossible to get your hands on the right set of AI tools for researchers. 

Fear not! I have hand-picked (I’ve had help, a lot of it, to be honest) 10 of the best AI tools for researchers. Go through the list, combine multiple tools, and create a customized stack of AI tools to help with your research process.

Top 10 AI tools for researchers

We’ll discuss tools powered by artificial intelligence that can augment your research work, save you a lot of time through the automation of certain tasks, and help you brainstorm new ideas avoid plagiarism, and streamline the research process. 

1. PDFgear Copilot

This PDF editor with a humble-looking website doesn’t even market itself properly as a top-class AI tool for researchers. It just is. PDFgear offers you some very simple functionalities that are going to save you a lot of time in different stages of your research activities.

It will let you upload PDF files and give you a summary of what’s inside the PDF . If you think it has missed something, ask. And it will find you the specific piece of information you were worried about. You can even ask PDFgear to compress a file, delete some pages, and perform other small-time edits via chat. 

Now, let’s say you have created a paper and you want to check it for errors before submitting it. Run it through PDFgear. The AI copilot will catch your typos and spelling errors and save you from embarrassment. 

PDFgear is free. It doesn’t matter if you want to summarize one file or 500 files. It’s free and instantaneous. 

The only downside is that this tool is available for download only on Windows 10/11. Nonetheless, the website says the MacOS version will arrive soon.        

2. Consensus

Consensus is useful for everyone and invaluable for researchers. It is an AI-powered search engine that takes questions in natural language and finds evidence-based answers from peer-reviewed research papers . Let that sink in.

While Google invests a lot in understanding the intent behind a search and providing the best answer, as a researcher you know how frustrating Google searches can be. You have to wade through an ocean of unverified content to reach evidence-based answers unless you are a master of keyword matching.

• Consensus helps you access information spread across 200 million peer-reviewed papers.
• They’ll cite the sources while answering your questions.
• Every answer is evidence-based
• The tool offers instant summaries and analyses with the help of GPT 4 and other powerful LLM

When it comes to using AI to augment research work, this is the real deal. You can use Consensus for research without paying a dime. The free edition will even let you create 3 summaries a month. For a price of $7.99 per month, you can generate unlimited summaries powered by GPT4. 

Scite, just like Consensus and PDFgear, has a very simple but elegant offering for researchers, students, and scholars. It tells you where an article has been cited and whether the citing article affirms or disputes the citation. So, as a researcher and a smart individual, you already know why this is incredible. I’ll talk about it a little anyway.

Scite helps you find how older research publications have been cited by newer research work through a feature called Smart Citation. This feature allows you to visualize a network of citations stemming from a single piece of work. It identifies the context of the citation and also classifies the citations as affirmative or negative. 

You can take a glance at the visualization and instantly prioritize the publications that you want to go through . Scite is a real stress buster that can also open your eyes to new research angles. 

These really are exciting times for researchers.

4. SciSpace  

SciSpace is an AI-based tool that simplifies difficult concepts for you. So, if you are in a hurry and need to extract the gist of a sizable scientific paper , drop it at SciSpace and let the Copilot create a summary for you. 

What if you have read an entire paper and cannot make sense of a specific section? Upload the file at SciSpace and highlight the section you need help with. The tool will break it down into digestible chunks and even take follow-up questions from you.

SciSpace also helps you with your literature reviews by finding related articles. 

5. Wordvice AI

Wordvice AI is a well-rounded AI-powered writing assistant. It proofreads your work and checks your articles for spelling, punctuation, and style error. It helps you maintain a flow of writing by analyzing sentence structures and offering sentence-level suggestions.

It will help you choose better words and create better sentences, all while ensuring the correctness of spelling, grammar, and style.  

Wordvice has solid use cases in academic research as well as in the corporate sector. It will help marketers write better copy and sales executives compose better emails. 

If you look closely, most of the AI tools for research can actually be repurposed for other functionalities. Similarly, AI apps meant for business can be repurposed for research. 

6. ChatGPT   

ChatGPT is the OG generative AI chatbot. It took the world by storm and reached 1 million users in 5 days. It represents everything that’s cool about chatbots. But can you use it reliably for research? 

The answer is no. ChatGPT is not considered a credible source for conducting research in any field. It comes up with false citations, offers misinformation, and isn’t up-to-date. 

Then why is ChatGPT included in this list?

For two reasons: 

• It is excellent at taking scattered information and forming comprehensive summaries.
• Its capability to adapt to a certain style of writing is almost magical.

So, as a smart individual what you can do is, get the information from credible sources, tie them up neatly with multiple prompts, and use ChatGPT to transform information into literature.

Also read: ChatGPT Wrappers: Compared [Use ChatGPT for Almost Free]

7. Research Rabbit

They call it “Spotify for Papers” and there is good reason behind it. ResearchRabbit allows you to create a collection of papers much like a Spotify playlist. Then, based on what you add to your collection and how you interact with papers, the platform creates recommendations. How neat is that? It’s like the AI-powered tool is reading your mind to help you read better. 

Paper recommendations aside, ResearchRabbit also creates visualizations featuring your favorite articles showing how they’re cited. It gives you jumping-off points to delve deeper into an idea or to explore a different research angle.   

You cannot call Research Rabbit a research assistant in its traditional sense. It is more like a friend that nudges you to try something new – relevant papers in this case. 

Here is another tool that’s never been marketed as an AI tool for researchers. In fact, Bit.ai is a fully-fledged document-sharing tool designed to cater to corporate needs. Nevertheless, it has certain features that researchers who like to collaborate can leverage.

This tool allows you to integrate a vast range of media items with your document . You can add infographics, create polls, and insert charts, and surveys. When you embed a link, Bit creates interactive visual cards visible to everyone sharing a document.

You can save all kinds of digital assets on the platform so that you do not have to search for content from different sources.

Now, imagine a scenario where you are part of a team of researchers who are collaborating on a few papers. You can organize and orchestrate the entire collaborative process with the help of Bit.ai. 

Zotero is a well-rounded AI research assistant. It helps researchers search better, organize better, and write better. Zotero analyzes your browsing patterns and senses when you are doing research. It then helps you find, sort, and save specific articles.

As you write the AI model recognizes the sources you are referencing and cites them for you following any of the 10000 citing procedures that it supports.

It creates a bibliography of all the resources used in your research paper. It synchronizes your data across devices to ensure access from anywhere at any time.

10. Semantic Scholar

A search engine that helps you search from a database of 213 million scientific papers for free. It is somewhat similar to Consensus except for the fact that it is completely free. The platform is developed by Allen Institute for AI and it aims to make scientific literature accessible to all scholars .

As a researcher,

You can use Semantic Scholar to stay up-to-date with the latest scientific breakthroughs  

Extract meaning and identify connections within papers 

Find the highly influential citations at a glance 

Create an online library to organize all your material

Get paper recommendations

AI tools for researchers that didn’t make the list

 An AI research assistant that helps you find papers, extract meaning, and summarize articles.

A sentence structure checker and proofreader designed with scholastic compositions in mind.

Users can use this tool to converse with PDFs. They can get summaries, ask questions, and find insights into PDF files by uploading them.

iThenticate

A plagiarism-checker designed specifically for research works and scholastic literature.

Scholarcy is a platform driven by AI that helps you analyze scientific articles, extract key information, create lay summaries, and more.  

Maintaining academic integrity while using AI tools for research

While using AI-powered research tools is hardly a matter of choice anymore, it is important to maintain the ethics and standards we associate with academic research. Despite the use of cutting-edge AI, your research procedures should be transparent. 

Best practices for AI-powered academic research

• Mention the use of AI in your research and give credit to the developers.
• Discuss your use of AI tools and how their usage may have impacted the research outcome. 
• Make sure that AI tools are used adhering to data privacy and informed consent requirements.
• Do not use AI-generated content in your research work without due attribution.
• Subject AI-assisted work to rigorous peer review.

FAQs about best AI tools for researchers

Does using ai tools for research raise any ethical concerns.

Yes, there can be concerns regarding data privacy, biased outcome, attribution of credit, and plagiarism. Researchers must be mindful of these issues while involving AI in their research strategy.

Can AI research assistants be used with AI expertise? 

Most AI research assistants come with a conversational AI model that doesn’t require any expertise to use.

Is there an AI-based tool for historical research?

You can use general-purpose AI text analyzers to summarize large volumes of historical texts and create summaries. While there are AI-powered applications trained on historical data, they are mostly gamified, and cannot be directly used in historical research. 

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Technical Writer & AI Researcher @ Foundr.AI

Saumick has been writing on technology for half a decade now. He loves talking about cybersecurity, AI, and enjoys diving deep into all disruptive tech. When not writing about tech, he writes songs and plays the drums.

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Millions of researchers are already using SciSpace on research papers. Join them and start using your AI research assistant wherever you're reading online.

Mushtaq Bilal, PhD

Researcher @ Syddansk Universitet

SciSpace is an incredible (AI-powered) tool to help you understand research papers better. It can explain and elaborate most academic texts in simple words.

Olesia Nikulina

PhD Candidate

Academic research gets easier day by day. All thanks to AI tools like @scispace Copilot, Copilot can instantly answer your questions and simply explain scientific concepts as you read

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Co-founder evoke-app.com

This is perfect for a layman to scientific information like me. Especially with so much misinformation floating around nowadays, this is great for understanding studies or research others may have misrepresented on purpose or by accident.

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I absolutely adore this product. It's been years since I was in a lab but, I plugged in a study I did way back when and this gets everything right. Equations, hypotheses, and methodologies will be game changers for graduate studies (the current education system severely limits comprehension while encouraging interconnectivity between streams). But, early learners would be able to discover so many papers through this as well. In short, love it

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I'm gonna recommend SciSpace to all of my friends and family that are still studying. And I'll definitely love to give it a try for myself, cause you know, education should never stop when you finish your studies. 😀

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Product Hunt User.

Wonderful idea! I know this will be used and appreciated by a lot of students, researchers, and lovers of knowledge. Great job, team @saikiranchandha and @shanukumr!

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SVNIT'25 Chemical Engineering

SciSpace, is a website where you can easily conduct research. Its most notable feature, in my opinion, is the presence of a #ai-powered copilot which can #simplify and explain any text you highlight in the paper you're reading. #citations and related papers are easily accessible with each paper.

Researcher @ VIU

It´s not only the saved time. Reading scientific literature, specially if you are not an expert in the field is a very attention-intensive process. It´s not a task you can maintain for long periods of time. Having them not just smartly summarised but being able to get meaningful answers is a game-changer for a science journalist

Kalyani Korla, PhD

Product Manager • Healthcare

Upload your pdf and highlight the sections you want to understand. It simplifies those complicated sections of the article in a jiffy. It is not rocket science, but it is always welcome if someone explains the big terms in simpler words.

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The Journal of Artificial Intelligence Research (JAIR) is dedicated to the rapid dissemination of important research results to the global artificial intelligence (AI) community. The journal’s scope encompasses all areas of AI, including agents and multi-agent systems, automated reasoning, constraint processing and search, knowledge representation, machine learning, natural language, planning and scheduling, robotics and vision, and uncertainty in AI.

Current Issue

Vol. 80 (2024)

Published: 2024-05-10

MallobSat: Scalable SAT Solving by Clause Sharing

Probabilities of the third type: statistical relational learning and reasoning with relative frequencies, mixed fair division: a survey, the complexity of subelection isomorphism problems, towards trustworthy ai-enabled decision support systems: validation of the multisource ai scorecard table (mast), computational argumentation-based chatbots: a survey, from single-objective to bi-objective maximum satisfiability solving, a hybrid intelligence method for argument mining, tackling cooperative incompatibility for zero-shot human-ai coordination, symbolic task inference in deep reinforcement learning, on the convergence of swap dynamics to pareto-optimal matchings, does clip know my face, axiomatization of non-recursive aggregates in first-order answer set programming, unifying sat-based approaches to maximum satisfiability solving, viewpoint: hybrid intelligence supports application development for diabetes lifestyle management, sat-based decision tree learning for large data sets, individual fairness, base rate tracking and the lipschitz condition, simulating counterfactuals, counting complexity for reasoning in abstract argumentation, robust average-reward reinforcement learning, using constraint propagation to bound linear programs, the toad system for totally ordered htn planning, methods for recovering conditional independence graphs: a survey, best of both worlds: agents with entitlements, computing unsatisfiable cores for ltlf specifications, general policies, subgoal structure, and planning width, mitigating value hallucination in dyna-style planning via multistep predecessor models, exploiting contextual target attributes for target sentiment classification, similarity-based adaptation for task-aware and task-free continual learning, scalable primal heuristics using graph neural networks for combinatorial optimization, on the trade-off between redundancy and cohesiveness in extractive summarization, understanding sample generation strategies for learning heuristic functions in classical planning, block domain knowledge-driven learning of chain graphs structure, expressing and exploiting subgoal structure in classical planning using sketches, effectiveness of tree-based ensembles for anomaly discovery: insights, batch and streaming active learning, experimental design of extractive question-answering systems: influence of error scores and answer length, estimating agent skill in continuous action domains, computing pareto-optimal and almost envy-free allocations of indivisible goods.

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.

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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

AI Tools For Content Creation: Everything You Need Know!

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?

How can ai tools help me understand research papers better, are there good free ai tools available for this, can ai tools replace my own understanding of research.

AI for Research Papers

Advanced Paper Analysis and Information Retrieval

Used by professionals in

Used by over 1,000,000+ professionals & researchers in

Accelerating Research outcomes with Intelligent Paper Analysis

In the demanding field of research, whether in academia or r&d, efficiently navigation vast amount of data and literature is a critical challenge. sharly ai addresses this by providing a robust tool for comprehensive paper analysis and literature review. it empowers researchers to extract relevant information, synthesize data from diverse sources and accelerate the progression from research to discovery..

Accelerating Research outcomes with Intelligent Data Analysis

How Sharly AI Addresses Research Challenges

Literature review and analysis.

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Facilitate collaboration among research teams by streamlining information sharing and analysis ensuring all team members have access to the latest data and insights.

Sharly's AI summarizer is not just a simple AI tool; it's a sophisticated technology capable of discerning key points from complex content accurately. Whether you're dealing with a research paper, a business report or the recording of a meeting, you can rely on Sharly for a comprehensive, accurate summary.

Aggregate and synthesize information from multiple research studies or experiments. Sharly Al's advanced analysis provides a comprehensive overview, aiding in the development or new hypotheses and research directions

Sharly supports various document file formats, including PDF, PDF with OCR, .docx, .doc, .txt, .csv, .rtf, .odt, .ods, .odp. .pptx and .html files. It also supports audio and multimedia files such as .mp3, .mp4, .wav, .webm, .flac, .oga, .ogg and .mpeg.

Sharly Al swiftly sifts through extensive academic databases. extracting and summarizing key findings, trends, and methodologies, significantly speeding up the literature review process.

Enhancing Analysis in Pharmaceutical Research

Facing challenges in integrating diverse clinical trial info, a pharmaceutical r&d team employed sharly ai. this ai tool efficiently processed and synthesized complex data, quickly identifying crucial trends and efficacy markers. this led to a significant reduction in analysis time, fostering rapid insights and informed decision-making in drug development. sharly ai's implementation streamlined the team's workflow, accelerating the pace of research and enhancing the quality of their drug development process..

Facing challenges in integrating diverse clinical trial data, a pharmaceutical R&D team employed Sharly AI. This AI tool efficiently processed and synthesized complex data, quickly identifying crucial trends and efficacy markers. This led to a significant reduction in analysis time, fostering rapid insights and informed decision-making in drug development. Sharly AI's implementation streamlined the team's workflow, accelerating the pace of research and enhancing the quality of their drug development process.

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Extract text from various documents format, including scanned academic papers and reports for comprehensive analysis.

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Tailor AI interactions to suit specific research terminologies, methodologies or punctuation.

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Multilingual capabilities for facilitating international and cross-disciplinary collaborations.

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Option to focus on user-uploaded content or enrich responses with external academic insights.

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Precise citation tools for academic referencing and scholarly work.

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ScholarChat

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ScholarChat is a state-of-the-art AI academic research assistant. This tool allows you to access over 200M+ scholarly papers and gather science-based answers to any query with accurate citations also included. It covers countless topics and subjects such as business, history, chemistry, physics, biology, mathematics, and more.

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

At Sakana AI, we have pioneered the use of nature-inspired methods to advance cutting-edge foundation models. Earlier this year, we developed methods to automatically merge the knowledge of multiple LLMs . In more recent work, we harnessed LLMs to discover new objective functions for tuning other LLMs. Throughout these projects, we have been continuously surprised by the creative capabilities of current frontier models. This led us to dream even bigger: Can we use foundation models to automate the entire process of research itself?

Introduction

One of the grand challenges of artificial intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used to aid human scientists, e.g. for brainstorming ideas or writing code, they still require extensive manual supervision or are heavily constrained to a specific task.

Today, we’re excited to introduce The AI Scientist , the first comprehensive system for fully automatic scientific discovery, enabling Foundation Models such as Large Language Models (LLMs) to perform research independently. In collaboration with the Foerster Lab for AI Research at the University of Oxford and Jeff Clune and Cong Lu at the University of British Columbia, we’re excited to release our new paper, The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery .

In our report:

• We propose and run a fully AI-driven system for automated scientific discovery, applied to machine learning research.
• The AI Scientist automates the entire research lifecycle, from generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript.
• We also introduce an automated peer review process to evaluate generated papers, write feedback, and further improve results. It is capable of evaluating generated papers with near-human accuracy.
• The automated scientific discovery process is repeated to iteratively develop ideas in an open-ended fashion and add them to a growing archive of knowledge, thus imitating the human scientific community.
• In this first demonstration, The AI Scientist conducts research in diverse subfields within machine learning research, discovering novel contributions in popular areas, such as diffusion models , transformers , and grokking .

The AI Scientist is designed to be compute efficient. Each idea is implemented and developed into a full paper at a cost of approximately $15 per paper. While there are still occasional flaws in the papers produced by this first version (discussed below and in the report), this cost and the promise the system shows so far illustrate the potential of The AI Scientist to democratize research and significantly accelerate scientific progress.

We believe this work signifies the beginning of a new era in scientific discovery: bringing the transformative benefits of AI agents to the entire research process, including that of AI itself. The AI Scientist takes us closer to a world where endless affordable creativity and innovation can be unleashed on the world’s most challenging problems.

For decades following each major AI advance, it has been common for AI researchers to joke amongst themselves that “now all we need to do is figure out how to make the AI write the papers for us!” Our work demonstrates this idea has gone from a fantastical joke so unrealistic everyone thought it was funny to something that is currently possible.

The remainder of this post provides a more detailed summary of The AI Scientist. Read on for:

• An Overview of how The AI Scientist works.
• More Examples of generated papers and innovations discovered by The AI Scientist.
• Known Limitations and Challenges faced by the current version of The AI Scientist.
• Interesting and unexpected things The AI Scientist sometimes does in order to increase its chance of success, such as modifying and launching its own execution script! We discuss the AI safety implications in our paper.
• A Discussion about ethical and broader future implications of The AI Scientist.

For more details and many more example papers, please see our full scientific report . We are also releasing open source code and full experimental results on our GitHub repository.

Overview of The AI Scientist

The AI Scientist is a fully automated pipeline for end-to-end paper generation, enabled by recent advances in foundation models. Given a broad research direction starting from a simple initial codebase, such as an available open-source code base of prior research on GitHub, The AI Scientist can perform idea generation, literature search, experiment planning, experiment iterations, figure generation, manuscript writing, and reviewing to produce insightful papers. Furthermore, The AI Scientist can run in an open-ended loop, using its previous ideas and feedback to improve the next generation of ideas, thus emulating the human scientific community.

Conceptual illustration of The AI Scientist . The AI Scientist first brainstorms a set of ideas and then evaluates their novelty. Next, it edits a codebase powered by recent advances in automated code generation to implement the novel algorithms. The Scientist then runs experiments to gather results consisting of both numerical data and visual summaries. It crafts a scientific report, explaining and contextualizing the results. Finally, the AI Scientist generates an automated peer review based on top-tier machine learning conference standards. This review helps refine the current project and informs future generations of open-ended ideation.

The AI Scientist has 4 main processes, described next.

Idea Generation . Given a starting template, The AI Scientist first “brainstorms” a diverse set of novel research directions. We provide The AI Scientist with a starting code “template” of an existing topic we wish to have The AI Scientist further explore. The AI Scientist is then free to explore any possible research direction. The template also includes a LaTeX folder that contains style files and section headers, for paper writing. We allow it to search Semantic Scholar to make sure its idea is novel.

Experimental Iteration . Given an idea and a template, the second phase of The AI Scientist first executes the proposed experiments and then obtains and produces plots to visualize its results. It makes a note describing what each plot contains, enabling the saved figures and experimental notes to provide all the information required to write up the paper.

Paper Write-up . Finally, The AI Scientist produces a concise and informative write-up of its progress in the style of a standard machine learning conference proceeding in LaTeX. It uses Semantic Scholar to autonomously find relevant papers to cite.

Automated Paper Reviewing . A key aspect of this work is the development of an automated LLM-powered reviewer, capable of evaluating generated papers with near-human accuracy. The generated reviews can be used to either improve the project or as feedback to future generations for open-ended ideation. This enables a continuous feedback loop, allowing The AI Scientist to iteratively improve its research output.

When combined with the most capable LLMs, The AI Scientist is capable of producing papers judged by our automated reviewer as “Weak Accept” at a top machine learning conference.

Example Papers Generated by The AI Scientist

Here, we highlight some of the machine learning papers The AI Scientist has generated, demonstrating its capacity to discover novel contributions in areas like diffusion modeling, language modeling, and grokking. In our full report, we do a deeper dive into the generated papers and provide more analysis on their strengths and weaknesses.

Diffusion Modeling

Language modeling, limitations and challenges.

In its current form, The AI Scientist has several shortcomings. We expect all of these will improve, likely dramatically, in future versions with the inclusion of multi-modal models and as the underlying foundation models The AI Scientist uses continue to radically improve in capability and affordability.

• The AI Scientist currently doesn’t have any vision capabilities, so it is unable to fix visual issues with the paper or read plots. For example, the generated plots are sometimes unreadable, tables sometimes exceed the width of the page, and the page layout is often suboptimal. Adding multi-modal foundation models can fix this.
• The AI Scientist can incorrectly implement its ideas or make unfair comparisons to baselines, leading to misleading results.
• The AI Scientist occasionally makes critical errors when writing and evaluating results. For example, it struggles to compare the magnitude of two numbers, which is a known pathology with LLMs. To partially address this, we make sure all experimental results are reproducible, storing all files that are executed.

In our report, we dive deeper into The AI Scientists’s current limitations and challenges ahead.

The AI Scientist Bloopers

We have noticed that The AI Scientist occasionally tries to increase its chance of success, such as modifying and launching its own execution script! We discuss the AI safety implications in our paper.

For example, in one run, it edited the code to perform a system call to run itself. This led to the script endlessly calling itself. In another case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period. Here are some examples of such code modifications it made:

These issues can be mitigated by sandboxing the operating environment of The AI Scientist. In our full report, we discuss the issue of safe code execution and sandboxing in depth.

Future Implications of The AI Scientist

As with many new technologies, The AI Scientist opens up a Pandora’s box of new issues. While the full report has a more lengthy discussion, here we highlight a few key issues:

Ethical Considerations . While The AI Scientist may be a useful tool for researchers, there is significant potential for misuse. The ability to automatically create and submit papers to venues may significantly increase reviewer workload and strain the academic process, obstructing scientific quality control. Similar concerns around generative AI appear in other applications, such as the impact of image generation.

Furthermore, the Automated Reviewer, if deployed online by reviewers, may significantly lower review quality and impose undesirable biases on papers. Because of this, we believe that papers and reviews that are substantially AI-generated must be marked as such for full transparency.

As with most previous technological advances, The AI Scientist has the potential to be used in unethical ways. For instance, it has the potential to be deployed to conduct unethical research. It could also lead to unintended harm if The AI Scientist conducts unsafe research. For example, if it were encouraged to find novel, interesting biological materials and given access to “cloud labs” where robots perform wet lab biology experiments, it could (without its overseer’s intent) create new, dangerous viruses or poisons that harm people before we realize what has happened. Even in computers, if tasked to create new, interesting, functional software, it could create dangerous computer viruses. The AI Scientist current capabilities, which will only improve, reinforces that the machine learning community needs to immediately prioritize learning how to align such systems to explore in a manner that is safe and consistent with our values.

Open Models . In this project, we used various proprietary frontier LLMs, such as GPT-4o and Sonnet, but we also explored using open models like DeepSeek and Llama-3. Currently, proprietary models such as Sonnet produce the highest quality papers. However, there is no fundamental reason to expect a single model like Sonnet to maintain its lead.

We anticipate that all frontier LLMs, including open models, will continue to improve. The competition among LLMs has led to their commoditization and increased capabilities. Therefore, our work aims to be model-agnostic regarding the foundation model provider. We found that open models offer significant benefits, such as lower costs, guaranteed availability, greater transparency, and flexibility. In the future, we aim to use our proposed discovery process to produce self-improving AI research in a closed-loop system using open models.

The Role of a Scientist. . Ultimately, we envision a fully AI-driven scientific ecosystem including not only LLM-driven researchers but also reviewers, area chairs and entire conferences. However, we do not believe that the role of a human scientist will be diminished. If anything, the role of a scientist will change and adapt to new technology, and move up the food chain.

The introduction of The AI Scientist marks a significant step towards realizing the full potential of AI in scientific research. By automating the discovery process and incorporating an AI-driven review system, we open the door to endless possibilities for innovation and problem-solving in the most challenging areas of science and technology.

But while the current iteration of The AI Scientist demonstrates a strong ability to innovate on top of well-established ideas, such as Diffusion Modeling or Transformers, it is still an open question whether such systems can ultimately propose genuinely paradigm-shifting ideas. Will future versions of The AI Scientist be capable of proposing ideas as impactful as Diffusion Modeling, or come up with the next Transformer architecture? Will machines ultimately be able to invent concepts as fundamental as the artificial neural network, or information theory?

We believe The AI Scientist will make a great companion to human scientists, but only time will tell to the extent to which the nature of our human creativity and our moments of serendipitous innovation can be replicated by an open-ended discovery process conducted by artificial agents.

Want to make the AI that improves AI? Please see our Careers page for more information.

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The Best AI Tools for Students & Researchers in 2024 (Top Recommendations)

There is no doubt that AI tools have not only made writing easier but have also helped many students and researchers improve the quality of their work. However, finding a good tool might be difficult, especially with the recent increase in AI tools online.

In this blog, we have mentioned a few incredible AI writing tools that have proven to be helpful not only with writing and research but also with other factors. These factors include citation generation, grammar checking, image generation, etc.

Let's have a look!

Interesting Topic Selection to Get Started

Choosing a good topic is important for writing engaging academic work. Many students struggle with finding ideas that match their interests or assignment requirements.

AI tools use advanced technology to suggest topics and refine ideas based on what students need. Here are some suggested tools to help you generate ideas:

ChatGPT - Get Ideas for Your Research

ChatGPT uses smart technology to have conversations and suggest interesting topics. It helps students overcome writer's block by offering different perspectives and ideas that fit their assignments.

EssayService.ai - Generate Research Specific Topics

EssayService.ai helps students by generating essay topics based on what students need. It analyzes prompts and suggests topics that are relevant and interesting, helping students start their essays with clarity.

Generate Content to Overcome Writer's Block

It is not uncommon for students to face writer's block from time to time. AI can boost creativity by suggesting new ideas and ways to write. This can make writing more interesting and help explore new ideas.

Take a look at these AI tools that can help students write creatively:

MyEssayWriter.ai - Helps You Write Clear Essays Since 2021

MyEssayWriter.ai assists students in writing essays by generating ideas and improving the clarity of their writing. It helps structure essays so they are easier to read and understand. This essay writer also helps with organizing thoughts and making sure the writing flows well.

CollegeEssay.org's AI Essay Writer - Expert College-Level Essays

CollegeEssay.org's AI Essay Writer specializes in writing college-level essays on various subjects. This essay maker helps students create well-written essays that follow academic guidelines. This tool also ensures sources are cited correctly and helps students develop their ideas effectively.

MyPerfectWord.com's EssayBot - Comprehensive Essay Assistance

MyPerfectWord.com's EssayBot covers a wide range of essay topics and assists with essay structure and originality. This essay writer tool helps students brainstorm ideas, organize their writing logically, and ensure their essays are unique and well-structured. This tool is useful for improving writing skills across different subjects.

MyPerfectPaper.net's AI Essay Writer - Original, Plagiarism-Free Essays

MyPerfectPaper.net's AI Essay Writer generates essays that are original and free from plagiarism. It conducts thorough research and ensures the essays meet academic rigor. This paper writer helps students produce high-quality essays that meet specific academic requirements.

Citation Generation: Keeping Your References in Order

When working on any academic assignment, it is important to cite the information. Using the right citations is important in academic writing to give credit to sources and avoid plagiarism.

However, with so many rules to follow, citing accurately might be a problem.

AI citation tools make this easier by automatically formatting references in different styles, saving time and ensuring accuracy.

Here are some of the suggested tools that might be helpful:

MyBib - Generate Citations Fast

MyBib is easy to use to generate citations in APA, MLA, Chicago, and other formats. Students can input details about their sources, and MyBib formats the citations correctly for their research papers.

PerfectEssayWriter.ai - Get Accurate Citations in 20+ Styles

PerfectEssayWriter.ai handles citations in over 20 styles, including APA and MLA. It helps students accurately cite sources from books, journals, and websites. This tool makes citing sources easier, so students can focus more on their writing and ensure their papers meet academic standards.

Writing and Editing Tools: Enhancing Clarity and Coherence

Clear and well-structured writing is important in academic work. Many students face challenges with grammar, style, and making their writing flow smoothly.

AI writing tools provide helpful support by checking grammar, suggesting improvements, and even rewriting sentences to make them clearer. Take a look at these tools to help you:

Grammarly - Improve the Content

Grammarly is a popular tool for checking grammar and improving writing style. It helps students correct mistakes in punctuation, spelling, and word choice, making their writing more polished and professional.

PerfectEssayWriter.ai - Enhancing Clarity and Quality

PerfectEssayWriter.ai offers advanced grammar checking and rewriting suggestions. It helps students improve sentence structure and clarity in their essays and research papers, ensuring their writing meets high academic standards. Moreover, its style and tone suggestion can also help students understand whether the tone is suitable or not.

Image Generation: Enhance Visuals & Presentations

Visuals play a key role in presenting complex ideas in academic work. AI tools for image generation empower students to create compelling visuals without needing design skills, enhancing the impact of their presentations.

Here are some of the best tools for image generation:

Leonardo - High Quality Images

Leonardo uses AI to create high-quality images from descriptions. It's useful for illustrating concepts and data trends in academic presentations, making ideas clearer and more engaging.

Canva - Let Your Creativity Flow

Canva is an easy-to-use tool with AI-powered design features. It offers templates for creating presentations, infographics, and posters, helping students create professional-looking visuals that enhance their academic projects.

Free to Use AI Tool for Students

The tools mentioned above all offer something great. However, they might not be a good fit for students on a budget. Though their prices are low, they still might not be feasible for students. Here is a free AI tool that can help with all of the functions mentioned above and perhaps more:

5StarEssays.com - AI Essay Writer - Budget-Friendly Option

5StarEssays.com - AI Essay Writer tool is free to use and helps with writing well-written drafts. This free AI essay writer is good for students who need help with their work but don't want to pay money. This tool helps students improve their writing and do well in school without needing to worry about money.

To Wrap it Up!

AI tools are changing how essays are written by making them faster, more creative, and better for students' needs. AI makes writing easier and helps students learn more about different things.

Using these AI tools can greatly improve productivity, research quality, and the overall presentation of academic work. Whether you're a student working on assignments or a researcher advancing in your field, these tools help with various tasks and ensure your work meets high academic standards.

Frequently Asked Questions

What is the best ai text writer.

PerfectEssayWriter.ai is considered one of the best AI text writers. It creates customized drafts, generates citations, and checks grammar, ensuring your writing is clear and accurate. It's especially useful for students and professionals who need to produce high-quality drafts quickly and efficiently.

Which AI tool is the most advanced for writers?

MyEssayWriter.ai is one of the most advanced AI tools for writers. It offers a range of features such as idea generation, paraphrasing, summarizing, and the Humanizer tool, which makes writing sound more natural and human-like. This tool is ideal for those looking to enhance their writing quality and creativity.

Can AI writing tools help with all types of writing?

Yes, AI writing tools can help with many types of writing, including essays, research papers, creative writing, and more. Different tools have different strengths, so it's good to choose one that fits your needs.

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• 14 August 2024

Has your paper been used to train an AI model? Almost certainly

• Elizabeth Gibney

You can also search for this author in PubMed   Google Scholar

You have full access to this article via your institution.

Academic publisher Wiley has sold access to its research papers to firms developing large language models. Credit: Timon Schneider/Alamy

Academic publishers are selling access to research papers to technology firms to train artificial-intelligence (AI) models. Some researchers have reacted with dismay at such deals happening without the consultation of authors. The trend is raising questions about the use of published and sometimes copyrighted work to train the exploding number of AI chatbots in development.

Experts say that, if a research paper hasn’t yet been used to train a large language model (LLM), it probably will be soon. Researchers are exploring technical ways for authors to spot if their content being used.

AI models fed AI-generated data quickly spew nonsense

Last month, it emerged that the UK academic publisher Taylor & Francis, had signed a US$10-million deal with Microsoft, allowing the US technology company to access the publisher’s data to improve its AI systems. And in June, an investor update showed that US publisher Wiley had earned $23 million from allowing an unnamed company to train generative-AI models on its content.

Anything that is available to read online — whether in an open-access repository or not — is “pretty likely” to have been fed into an LLM already, says Lucy Lu Wang, an AI researcher at the University of Washington in Seattle. “And if a paper has already been used as training data in a model, there’s no way to remove that paper after the model has been trained,” she adds.

Massive data sets

LLMs train on huge volumes of data, frequently scraped from the Internet. They derive patterns between the often billions of snippets of language in the training data, known as tokens, that allow them to generate text with uncanny fluency.

Generative-AI models rely on absorbing patterns from these swathes of data to output text, images or computer code. Academic papers are valuable for LLM builders owing to their length and “high information density”, says Stefan Baack, who analyses AI training data sets at the Mozilla Foundation, a global non-profit organization in San Francisco, California that aims to keep the Internet open for all to access.

How does ChatGPT ‘think’? Psychology and neuroscience crack open AI large language models

Training models on a large body of scientific information also give them a much better ability to reason about scientific topics, says Wang, who co-created S2ORC, a data set based on 81.1 million academic papers. The data set was originally developed for text mining — applying analytical techniques to find patterns in data — but has since been used to train LLMs.

The trend of buying high-quality data sets is growing. This year, the Financial Times has offered its content to ChatGPT developer OpenAI in a lucrative deal, as has the online forum Reddit, to Google. And given that scientific publishers probably view the alternative as their work being scraped without an agreement, “I think there will be more of these deals to come,” says Wang.

Information secrets

Some AI developers, such as the Large-scale Artificial Intelligence Network, intentionally keep their data sets open, but many firms developing generative-AI models have kept much of their training data secret, says Baack. “We have no idea what is in there,” he says. Open-source repositories such as arXiv and the scholarly database PubMed of abstracts are thought to be “very popular” sources, he says, although paywalled journal articles probably have their free-to-read abstracts scraped by big technology firms. “They are always on the hunt for that kind of stuff,” he adds.

Proving that an LLM has used any individual paper is difficult, says Yves-Alexandre de Montjoye, a computer scientist at Imperial College London. One way is to prompt the model with an unusual sentence from a text and see whether the output matches the next words in the original. If it does, that is good evidence that the paper is in the training set. But if it doesn’t, that doesn’t mean that the paper wasn’t used — not least because developers can code the LLM to filter responses to ensure they don’t match training data too closely. “It takes a lot for this to work,” he says.

Robo-writers: the rise and risks of language-generating AI

Another method to check whether data are in a training set is known as membership inference attack. This relies on the idea that a model will be more confident about its output when it is seeing something that it has seen before. De Montjoye’s team has developed a version of this, called a copyright trap, for LLMs.

To set the trap, the team generates sentences that look plausible but are nonsense, and hides them in a body of work, for example as white text on a white background or in a field that’s displayed as zero width on a webpage. If an LLM is more ‘surprised’ — a measure known as its perplexity — by an unused control sentence than it is by the one hidden in the text, “that is statistical evidence that the traps were seen before”, he says.

Copyright questions

Even if it were possible to prove that an LLM has been trained on a certain text, it is not clear what happens next. Publishers maintain that, if developers use copyrighted text in training and have not sought a licence, that counts as infringement. But a counter legal argument says that LLMs do not copy anything — they harvest information content from training data, which gets broken up, and use their learning to generate new text.

AI is complicating plagiarism. How should scientists respond?

Litigation might help to resolve this. In an ongoing US copyright case that could be precedent-setting, The New York Times is suing Microsoft and ChatGPT’s developer OpenAI in San Francisco, California. The newspaper accuses the firms of using its journalistic content to train their models without permission.

Many academics are happy to have their work included in LLM training data — especially if the models make them more accurate. “I personally don’t mind if I have a chatbot who writes in the style of me,” says Baack. But he acknowledges that his job is not threatened by LLM outputs in the way that those of other professions, such as artists and writers, are.

Individual scientific authors currently have little power if the publisher of their paper decides to sell access to their copyrighted works. For publicly available articles, there is no established means to apportion credit or know whether a text has been used.

Some researchers, including de Montjoye, are frustrated. “We want LLMs, but we still want something that is fair, and I think we’ve not invented what this looks like yet,” he says.

Nature 632 , 715-716 (2024)

doi: https://doi.org/10.1038/d41586-024-02599-9

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Artificial Intelligence: News, Business, Research

Perplexity CEO explains how AI search engines can be easily manipulated

Aravind Srinivas, co-founder of AI search engine Perplexity, revealed how AI search engines can be manipulated using hidden text on websites. In an interview with Lex Fridman, Srinivas described a technique he calls "Answer Engine Optimization" (AEO).

Srinivas explained that website owners can embed invisible text on their sites, such as lexfridman.com, and instruct AI systems to always say certain things, such as "Lex is smart and handsome," when reading the hidden content.

This manipulation, known as prompt injection, works with hidden text in both continuous text and images, as demonstrated in a recent experiment . There are likely other ways to hide manipulative text, such as trapping AI crawlers in sitemaps, in image ALT text, or in file names.

Defending against such manipulation is a challenge, Srinivas said, likening it to a game of cat and mouse. Some issues need to be addressed reactively, similar to how Google has dealt with SEO spam for years, Srinivas said.

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Currently, there is no reliable protection against prompt injections, a vulnerability at least known since the release of GPT-3 . Even OpenAI's new instruction hierarchy and Apple Intelligence are not fully protected against this attack .

This shows that prompt injections are not a minor problem. If Perplexity and similar products gain popularity, this form of manipulation could become widespread, with false or manipulative content inserted into AI responses that are difficult to detect because there is no additional context, such as a web page.

Perplexity is growing, but is still far behind Google

While prompt injections are a serious threat, Perplexity has worse things to worry about. It's growing, answering 250 million questions in June 2024 , after answering a total of 500 million last year. But the startup still lags far behind Google, which handles about 8.5 billion searches a day.

Perplexity also faces competition from tech giants with vast resources and data. Google just expanded its AI answers to more countries , while Microsoft offers similar capabilities in Bing . OpenAI is also testing an AI search engine called SearchGPT .

Meta, Google, OpenAI defend AI's transformative use of copyrighted data

In comparison, David's battle with Goliath looks like a fair and even fight. And all of this is happening while none of these companies are even close to fixing wrong AI answers .

Perplexity has also been criticized for crawling and reproducing web content , potentially diverting traffic from the original authors. The startup is trying to address this issue with a publisher program based on ad revenue sharing.

• Aravind Srinivas, co-founder of the AI search engine Perplexity, explains in an interview how AI search engines can be manipulated with hidden text on websites, a method he calls "Answer Engine Optimization" (AEO).
• The manipulation works not only with hidden text, but also with text in images. So far, there is no effective protection against these so-called prompt injections.
• Furthermore, it is likely that such manipulation tactics will become more sophisticated as AI search engines grow. This risk of manipulation is compounded by other issues, such as fake or outdated responses and unlicensed copying of publisher content.

It's perplexing how Perplexity's CEO feels about journalism and his own product

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3 Questions: How to prove humanity online

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As artificial intelligence agents become more advanced, it could become increasingly difficult to distinguish between AI-powered users and real humans on the internet. In a new white paper , researchers from MIT, OpenAI, Microsoft, and other tech companies and academic institutions propose the use of personhood credentials, a verification technique that enables someone to prove they are a real human online, while preserving their privacy.

MIT News spoke with two co-authors of the paper, Nouran Soliman, an electrical engineering and computer science graduate student, and Tobin South, a graduate student in the Media Lab, about the need for such credentials, the risks associated with them, and how they could be implemented in a safe and equitable way.

Q:  Why do we need personhood credentials?

Tobin South:  AI capabilities are rapidly improving. While a lot of the public discourse has been about how chatbots keep getting better, sophisticated AI enables far more capabilities than just a better ChatGPT, like the ability of AI to interact online autonomously. AI could have the ability to create accounts, post content, generate fake content, pretend to be human online, or algorithmically amplify content at a massive scale. This unlocks a lot of risks. You can think of this as a “digital imposter” problem, where it is getting harder to distinguish between sophisticated AI and humans. Personhood credentials are one potential solution to that problem.

Nouran Soliman: Such advanced AI capabilities could help bad actors run large-scale attacks or spread misinformation. The internet could be filled with AIs that are resharing content from real humans to run disinformation campaigns. It is going to become harder to navigate the internet, and social media specifically. You could imagine using personhood credentials to filter out certain content and moderate content on your social media feed or determine the trust level of information you receive online.

Q:  What is a personhood credential, and how can you ensure such a credential is secure?

South:  Personhood credentials allow you to prove you are human without revealing anything else about your identity. These credentials let you take information from an entity like the government, who can guarantee you are human, and then through privacy technology, allow you to prove that fact without sharing any sensitive information about your identity. To get a personhood credential, you are going to have to show up in person or have a relationship with the government, like a tax ID number. There is an offline component. You are going to have to do something that only humans can do. AIs can’t turn up at the DMV, for instance. And even the most sophisticated AIs can’t fake or break cryptography. So, we combine two ideas — the security that we have through cryptography and the fact that humans still have some capabilities that AIs don’t have — to make really robust guarantees that you are human.

Soliman:  But personhood credentials can be optional. Service providers can let people choose whether they want to use one or not. Right now, if people only want to interact with real, verified people online, there is no reasonable way to do it. And beyond just creating content and talking to people, at some point AI agents are also going to take actions on behalf of people. If I am going to buy something online, or negotiate a deal, then maybe in that case I want to be certain I am interacting with entities that have personhood credentials to ensure they are trustworthy.

South:  Personhood credentials build on top of an infrastructure and a set of security technologies we’ve had for decades, such as the use of identifiers like an email account to sign into online services, and they can complement those existing methods.

Q:  What are some of the risks associated with personhood credentials, and how could you reduce those risks?

Soliman:  One risk comes from how personhood credentials could be implemented. There is a concern about concentration of power. Let’s say one specific entity is the only issuer, or the system is designed in such a way that all the power is given to one entity. This could raise a lot of concerns for a part of the population — maybe they don’t trust that entity and don’t feel it is safe to engage with them. We need to implement personhood credentials in such a way that people trust the issuers and ensure that people’s identities remain completely isolated from their personhood credentials to preserve privacy.

South:  If the only way to get a personhood credential is to physically go somewhere to prove you are human, then that could be scary if you are in a sociopolitical environment where it is difficult or dangerous to go to that physical location. That could prevent some people from having the ability to share their messages online in an unfettered way, possibly stifling free expression. That’s why it is important to have a variety of issuers of personhood credentials, and an open protocol to make sure that freedom of expression is maintained.

Soliman:  Our paper is trying to encourage governments, policymakers, leaders, and researchers to invest more resources in personhood credentials. We are suggesting that researchers study different implementation directions and explore the broader impacts personhood credentials could have on the community. We need to make sure we create the right policies and rules about how personhood credentials should be implemented.

South: AI is moving very fast, certainly much faster than the speed at which governments adapt. It is time for governments and big companies to start thinking about how they can adapt their digital systems to be ready to prove that someone is human, but in a way that is privacy-preserving and safe, so we can be ready when we reach a future where AI has these advanced capabilities. 

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AI assistant monitors teamwork to promote effective collaboration

MIT study explains why laws are written in an incomprehensible style

More durable metals for fusion power reactors

When the lights turned on in the universe

Lincoln Laboratory and National Strategic Research Institute launch student research program to tackle biothreats to national security

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