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Get around this paywall in a flash: DOI: 10.1126/science.196.4287.293 URL: http://science.sciencemag.org/content/196/4287/293/tab-pdf PMC (Pubmed Central) ID: PMC4167664 Pubmed ID: 17756097 Title: Ribulose bisphosphate carboxylase: a two-layered, square-shaped molecule of symmetry 422 Citation: Baker, T. S., Eisenberg, D., & Eiserling, F. (1977). Ribulose Bisphosphate Carboxylase: A Two-Layered, Square-Shaped Molecule of Symmetry 422. Science, 196(4287), 293-295. doi:10.1126/science.196.4287.293 or try your favourite citation format (Harvard, Bibtex, etc).

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Analyze research papers at superhuman speed

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

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Tons of features to speed up your research

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

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

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

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

What is Elicit not a good fit for?

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

What is Elicit Plus?

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

What are credits?

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

How can you get in contact with the team?

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

What happens to papers uploaded to Elicit?

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

How accurate is Elicit?

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

The Semantic Reader Open Research Platform

Semantic Reader Project is a collaborative effort of NLP + HCI researchers from non-profit, industry, and academic institutions to create interactive, intelligent reading interfaces for scholarly papers. Our research led to the creation of Semantic Reader, an application used by tens of thousands of scholars each week.

The Semantic Reader Open Research Platform provides resources that enable the broader research community to explore exciting challenges around novel research support tools: PaperMage , a library for processing and analyzing scholarly PDFs, and PaperCraft , a React UI component for building augmented and interactive reading interfaces. Join us in designing the future of scholarly reading interfaces with our open source libraries!

Open Source Libraries

We provide PaperMage + PaperCraft for building intelligent and interactive paper readers. Below we showcase how to extract text from a PDF to prompt a LLM for term definitions and then visually augment the paper with highlights and popups.

Process and Analyze Scholarly PDF Documents

Create Visually Augmented Interactive Readers

Research Prototype Showcase

Here we present several interactive demos to showcase systems you can build with PaperMage and PaperCraft.

Augmenting Research Papers with Author Talk Videos

Demo Paper Presentation

Synergi & Threddy

Clipping Research Threads from Papers for Synthesis and Exploration

Paper Presentation

Paper Plain

Making Medical Research Papers Approachable to Healthcare Consumers

Demo Code Tutorial Paper

LLM Paper Q&A

A GPT-powered PDF QA system with attribution support.

Demo Code Tutorial

Augmenting Citations in Papers with Persistent and Personalized Context

In-Production Paper Presentation

Localizing Incoming Citations from Follow on Papers in the Margins

Automatic highlights for skimming support of scientific papers

In-Production Paper

Augmenting Papers with Just-in-Time Definitions of Terms and Symbols

Founding Project Demo Paper

Publications

Semantic reader project overview.

The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces Kyle Lo, Joseph Chee Chang, Andrew Head, Jonathan Bragg, Amy X. Zhang, Cassidy Trier, Chloe Anastasiades, Tal August, Russell Authur, Danielle Bragg, Erin Bransom, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Yen-Sung Chen, Evie (Yu-Yen) Cheng, Yvonne Chou, Doug Downey, Rob Evans, Raymond Fok, F.Q. Hu, Regan Huff, Dongyeop Kang, Tae Soo Kim, Rodney Michael Kinney, A. Kittur, Hyeonsu B Kang, Egor Klevak, Bailey Kuehl, Michael Langan, Matt Latzke, Jaron Lochner, Kelsey MacMillan, Eric Stuart Marsh, Tyler Murray, Aakanksha Naik, Ngoc-Uyen Nguyen, Srishti Palani, Soya Park, Caroline Paulic, Napol Rachatasumrit, Smita R Rao, P. Sayre, Zejiang Shen, Pao Siangliulue, Luca Soldaini, Huy Tran, Madeleine van Zuylen, Lucy Lu Wang, Christopher Wilhelm, Caroline M Wu, Jiangjiang Yang, Angele Zamarron, Marti A. Hearst, Daniel S. Weld . ArXiv. 2023 .

Interactive and Intelligent Reading Interfaces

Qlarify: Bridging Scholarly Abstracts and Papers with Recursively Expandable Summaries Raymond Fok, Joseph Chee Chang, Tal August, Amy X. Zhang, Daniel S. Weld . ArXiv. 2023 .

Papeos: Augmenting Research Papers with Talk Videos Tae Soo Kim, Matt Latzke, Jonathan Bragg, Amy X. Zhang, Joseph Chee Chang . The ACM Symposium on User Interface Software and Technology. 2023 .

Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking Hyeonsu B Kang, Sherry Wu, Joseph Chee Chang, A. Kittur . The ACM Symposium on User Interface Software and Technology. 2023 .

🏆 Best Paper Award CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context Joseph Chee Chang, Amy X. Zhang, Jonathan Bragg, Andrew Head, Kyle Lo, Doug Downey, Daniel S. Weld . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2023 .

Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections Srishti Palani, Aakanksha Naik, Doug Downey, Amy X. Zhang, Jonathan Bragg, Joseph Chee Chang . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2023 .

CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading Napol Rachatasumrit, Jonathan Bragg, Amy X. Zhang, Daniel S. Weld . 27th International Conference on Intelligent User Interfaces. 2022 .

🏆 Best Paper Award Math Augmentation: How Authors Enhance the Readability of Formulas using Novel Visual Design Practices Andrew Head, Amber Xie, Marti A. Hearst . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2022 .

Scim: Intelligent Skimming Support for Scientific Papers Raymond Fok, Hita Kambhamettu, Luca Soldaini, Jonathan Bragg, Kyle Lo, Andrew Head, Marti A. Hearst, Daniel S. Weld . Proceedings of the 28th International Conference on Intelligent User Interfaces. 2022 .

Exploring Team-Sourced Hyperlinks to Address Navigation Challenges for Low-Vision Readers of Scientific Papers Soya Park, Jonathan Bragg, Michael Chang, K. Larson, Danielle Bragg . Proceedings of the ACM on Human-Computer Interaction. 2022 .

Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, Kyle Lo . ACM Transactions on Computer-Human Interaction. 2022 . Presentation at CHI 2024.

Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature Hyeonsu B Kang, Joseph Chee Chang, Yongsung Kim, A. Kittur . Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 2022 .

🏆 Best Paper Award SciA11y: Converting Scientific Papers to Accessible HTML Lucy Lu Wang, Isabel Cachola, Jonathan Bragg, Evie (Yu-Yen) Cheng, Chelsea Hess Haupt, Matt Latzke, Bailey Kuehl, Madeleine van Zuylen, Linda M. Wagner, Daniel S. Weld . Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility. 2021 .

Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols Andrew Head, Kyle Lo, Dongyeop Kang, Raymond Fok, Sam Skjonsberg, Daniel S. Weld, Marti A. Hearst . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2020 .

Open Research Resources: Libraries, Models, Datasets

🏆 Best Paper Award PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents Kyle Lo, Zejiang Shen, Benjamin Newman, Joseph Chee Chang, Russell Authur, Erin Bransom, Stefan Candra, Yoganand Chandrasekhar, Regan Huff, Bailey Kuehl, Amanpreet Singh, Chris Wilhelm, Angele Zamarron, Marti A. Hearst, Daniel S. Weld, Doug Downey, Luca Soldaini. Conference on Empirical Methods in Natural Language Processing: Demos. 2023.

A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents Benjamin Newman, Luca Soldaini, Raymond Fok, Arman Cohan, Kyle Lo . undefined. 2023 .

🏆 Best Paper Award LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization Kalpesh Krishna, Erin Bransom, Bailey Kuehl, Mohit Iyyer, Pradeep Dasigi, Arman Cohan, Kyle Lo . ArXiv. 2023 .

Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents Catherine Chen, Zejiang Shen, D. Klein, G. Stanovsky, Doug Downey, Kyle Lo . ArXiv. 2023 .

The Semantic Scholar Open Data Platform Rodney Michael Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, Jason Dunkelberger, Oren Etzioni, Rob Evans, Sergey Feldman, Joseph Gorney, D. Graham, F.Q. Hu, Regan Huff, Daniel King, Sebastian Kohlmeier, Bailey Kuehl, Michael Langan, Daniel Lin, Haokun Liu, Kyle Lo, Jaron Lochner, Kelsey MacMillan, Tyler Murray, Christopher Newell, Smita R Rao, Shaurya Rohatgi, P. Sayre, Zejiang Shen, Amanpreet Singh, Luca Soldaini, Shivashankar Subramanian, A. Tanaka, Alex D Wade, Linda M. Wagner, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Madeleine van Zuylen, Daniel S. Weld . ArXiv. 2023 .

VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups Zejiang Shen, Kyle Lo, Lucy Lu Wang, Bailey Kuehl, Daniel S. Weld, Doug Downey . Transactions of the Association for Computational Linguistics. 2021 .

Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions Dongyeop Kang, Andrew Head, Risham Sidhu, Kyle Lo, Daniel S. Weld, Marti A. Hearst . Proceedings of the First Workshop on Scholarly Document Processing @ ACL. 2020 .

See the  Project Overview Paper  to see a full list of contributors. † For questions and inquiries, please contact Joseph Chee Chang (PaperCraft & Intelligent reading interfaces), or Kyle Lo and Luca Soldaini (PaperMage & Scientific document processing).

Research Advisory Board

Intelligent reading interfaces research, scientific document processing research, research libraries and tooling.

You Are All Set!

You now have access to Semantic Reader Beta features including highlighting and note taking.

Introducing Semantic Reader

An AI-Powered Augmented Scientific Reading Application

What is Semantic Reader?

Semantic Reader is an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual.

Studies have uncovered many points of friction that break the flow of comprehension when reading technical papers:

• Frequently paging back and forth looking for the details of cited papers
• Challenges recognizing the same work across multiple papers
• Losing track of reading history and notes
• Contending with a PDF format that is not well suited to mobile reading or assistive technologies such as screen readers

To create a better reading experience, Semantic Reader uses artificial intelligence to understand a document’s structure and merge it with the Semantic Scholar’s academic corpus, providing detailed information in context via tooltips and other overlays. If you’re logged-in, Semantic Reader integrates with your library and, over time, will incorporate personalized contextual augmentations as well.

A Revolutionary Reading Experience

Semantic Reader is now available for most arXiv papers on Semantic Scholar with a growing set of features.

• Citations Cards that show details of a cited paper in-line where you’re reading, including TLDR summaries
• Table of Contents to quickly navigate between sections (availability varies)
• Save to Library to conveniently track your reading list

We are incrementally improving, testing, and rolling out new features in Semantic Reader and expanding coverage to more paper sources. Subscribe to our Product Spotlight emails for updates!

Personalized In-line Citations

With the volume and variety of citations during literature review, it can be challenging to prioritize which ones to explore. In Semantic Reader, citations within a paper are visually augmented based on their connections to your research activities, such as saved in your library or cited by a paper in your library .

If you have at least one paper in your library , this feature is available on desktop devices for you! For more details, visit our FAQ . ‍ We plan to introduce visual augmentation for additional types of connections in the coming months. Stay tuned!

Skim Papers Faster

Find key points of a paper using AI-generated highlighted overlays with 3 category labels: Goal, Method, and Result. Customize the number of highlights and the opacity of highlights from the side panel to create your own experience. Now available on most English-language arXiv papers in computer science fields.

Skimming Examples

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• ALBERT: A Lite BERT For Self-supervised Learning of Language Representations
• LLaMA: Open and Efficient Foundation Language Models
• Scim: Intelligent Skimming Support for Scientific Papers

Open Resources for the Community

Create innovative research tools with resources and demos on the Semantic Reader Open Research Platform.

Access open-source libraries for PaperMage and PaperCraft to build intelligent and interactive paper readers. Discover interactive prototype demos developed with these tools.

Definitions On-Demand

Learn definitions for words and acronyms without losing your place in the paper.

Simply click any term with a dotted underline and get an AI-generated definition based on its context in the paper. For more instructions on using this feature in Semantic Reader, visit our FAQ .

Annotate and Highlight

With Hypothesis integration, Semantic Reader allows you to highlight and take notes while reading papers.

To access the Annotations panel, highlight some text in Semantic Reader and select Annotate or Highlight. From the panel, sign up for a Hypothesis account and log in. From there, you can post annotations, review your annotations anytime, and share them with others. For more instructions on using Hypothesis in Semantic Reader, visit our FAQ or Hypothesis help articles .

Try Out Semantic Reader

Here are examples of Semantic Reader operating over popular Computer Science papers across various subfields. The current design is best experienced on a full-size screen.

Natural Language Processing

• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
• Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

Computer Vision

• Long-term Recurrent Convolutional Networks for Visual Recognition and Description
• Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
• Rethinking the Inception Architecture for Computer Vision

Machine Learning

• Conditional Generative Adversarial Nets
• Learning Important Features Through Propagating Activation Differences
• WaveNet: A Generative Model for Raw Audio
• Pixel Recurrent Neural Networks

Send us your Semantic Reader feedback .

Powered by State-of-the-Art Research

Semantic Reader is based on research from the Semantic Scholar team at AI2, UC Berkeley and the University of Washington, and supported in part by the Alfred P. Sloan Foundation.

• September 2020

TLDR This work introduces ScholarPhi, an augmented reading interface with four novel features: tooltips that surface position-sensitive definitions from elsewhere in a paper, a filter over the paper that “declutters” it to reveal how the term or symbol is used across the paper, automatic equation diagrams that expose multiple definitions in parallel, and an automatically generated glossary of important terms and symbols.

TLDR A novel paper reading experience that integrates relevant information about follow-on work directly into a paper, allowing readers to learn about newer papers and see how a paper is discussed by its citing papers in the context of the reference paper.

TLDR Scim is presented, an AI-augmented reading interface designed to help researchers skim papers by automatically identifying, classifying, and highlighting salient sentences, organized into rhetorical facets rooted in common information needs.

Experience a smarter way to search and discover scholarly research.

Latest news & updates.

Case Study: Iterative Design for Skimming Support

How might we help researchers quickly assess the relevance of scientific literature? Take a closer look at Skimming, Semantic Reader’s latest AI feature, and the collaborative design process behind it.

Behind the Scenes of Semantic Scholar’s New Author Influence Design

We released a new version of Author Influence interface to help scholars better discover other scholars in their fields. Here's how we identified user insights and made those design choices.

Artificial-intelligence search engines wrangle academic literature

Nature had a chat with Dan Weld, Chief Scientist at Semantic Scholar, to discuss how search engines are helping scientists explore and innovate by making it easier to draw connections from a massive collection of scientific literature.

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Who can use and benefit from r discovery.

R Discovery is an app for students and researchers to simplify literature search and research reading, but it can be used by anyone interested in scholarly pursuits. Say goodbye to information overload and hello to seamless research reading with R Discovery, which instantly creates personalized reading feeds based on your interests, so you’re always updated on the latest, most relevant academic research for you!

What content will I find on R Discovery?

R Discovery hosts content from top aggregators like PubMed, PubMed Central, CrossRef, Unpaywall, Open Alex and top academic research publications, including Wiley, Elsevier, Springer Nature, IOP, SAGE, Taylor & Francis, NEJM, Emerald Publishing, BMJ, Karger, Underline.io and more. Elevate your research reading experience with access to 250M+ research articles, including 40M+ open access articles, across 32,000 journals as well as 2M+ preprints and 7.5M+ patents.

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• Published: 07 August 2017

Half of papers searched for online are free to read

• Dalmeet Singh Chawla  

Nature ( 2017 ) Cite this article

2269 Accesses

3 Citations

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This article has been updated

Large study of open research analysed reader data from Unpaywall tool, which finds freely available versions of articles.

Almost half of the scholarly papers that people attempt to access online are now freely and legally available, according to a huge study that tracked 100,000 online requests for journal papers in June.

The work, published on 2 August in PeerJ Preprints 1 , examined reader data from a web-browser extension called Unpaywall , which trawls the Internet to find free-to-read versions of paywalled papers.

The tool, which launched in April, was developed by two authors of the study, Jason Priem and Heather Piwowar, who co-founded the non-profit company Impactstory in Vancouver, Canada. It has been installed by more than 80,000 people worldwide and is used around 50,000 times a day, says Priem.

When Unpaywall users land on a journal paper, the tool queries a database called oaDOI — also developed by the pair — that contains records of all 67 million journal articles with digital object identifiers (DOIs), an identifier code widely used for academic publications. The widget then signals to the user whether a free-to-read version of the article is available.

The study authors analysed server logs of 100,000 papers that Unpaywall users tried to access during one week in June, and found that 47% of accessed studies were legally available to read for free somewhere on the web. Around half the content being accessed was published in the past two years, says Priem.

The study, which hasn’t yet been peer-reviewed, is “careful and extensive”, says Ludo Waltman, deputy director of the Centre for Science and Technology Studies at Leiden University in the Netherlands who edits the Journal of Informetrics .

The study authors say theirs is the first broad analysis of the state of open research since a 2014 report produced for the European Commission . But the two analyses employed different methods: the earlier one used automated software to search online for papers drawn at random from the Scopus database. It also scoured social scholarly networks such as Academia.edu and ResearchGate — which the Unpaywall study does not examine — and estimated that, at the time, more than half of peer-reviewed research articles published from 2007–12 were free to read online. Given the methodological differences, that’s roughly comparable to the finding in the new work, Piwowar says. 

The latest work also delves into how papers become free to read. More than 20% of scholarly articles searched for through Unpaywall were available directly from journals, with clear licences describing whether the papers were free not just to read, but also to download or redistribute. Another 9% of the studies were still published behind a paywall, but authors later uploaded their paper — or some version of it, such as a peer-reviewed manuscript — to an online repository (see ‘The state of open research’).

The most intriguing category of papers were the 15% that were posted on a publisher’s site as free to read, but without any explicit open licence. The authors say this type of open-access — which they call ‘bronze’, in contrast to the widely used ‘gold’ and ‘green’ definitions — has been scarcely discussed.

Citation complications

To measure the prevalence of free-to-read papers in the scholarly literature as a whole, the authors used oaDOI to identify the publication statuses of 100,000 articles chosen randomly from the 67 million journal articles available on the DOI registry Crossref. In this sample, 28% of articles were free-to-read, predicting a total of 19 million such articles in the literature. Of papers published in 2015 — the most recent year examined — 45% were freely available, which suggests that newer articles are more likely to be open.

The study also investigated the claim that open-access articles are more cited than paywalled studies. It analysed another random set of 100,000 papers from the 8 million indexed in the Web of Science database between 2009 and 2015, found that, for a given subject area and publication year, free-to-read articles are cited 18% more than the average.

The trend is supported by several previous studies 2 , but some have questioned whether the effect exists. Waltman says that it’s difficult to know for sure whether these studies are being cited more frequently specifically because they are open. To be certain, he says, one would need to check whether researchers citing the studies have access to paywalled content.

Priem says that one limitation of the study is that its samples included only articles with DOIs, which aren’t always used by publishers in the arts and humanities disciplines and in the developing world.

Still, “the percentage of literature that is OA continues to grow quite steadily”, he says. And that could have implications for academic libraries. As tensions over the costs of institutional subscription packages grow between universities and publishers, the finding that roughly half of recently published research may be available to read for free could “tip the scales toward cancellation for some institutions”, the study says.

To Priem, the future looks open. “In the next few decades, we’re going to be seeing nearly all the literature available freely.”

Change history

09 august 2017.

An earlier version of this story stated that free-to-read articles are cited 18% more than paywalled articles; in fact, the comparison is with the average for all articles for a given subject area and publication year.

Piwowar, H. et al. Preprint at http://dx.doi.org/10.7287/peerj.preprints.3119v1 (2017).

Gargouri, Y. et al. PLoS ONE 5 , e1363 (2010).

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Unpaywall finds free versions of paywalled papers 2017-Apr-04

Open-access website gets tough 2014-Aug-06

Half of 2011 papers now free to read 2013-Aug-20

Science publishing: Open access must enable open use 2012-Dec-19

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Singh Chawla, D. Half of papers searched for online are free to read. Nature (2017). https://doi.org/10.1038/nature.2017.22418

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Table of contents, listen to research papers aloud: we show you how, types of research papers, how text to speech works, technical language, length and density, time constraints, accessibility issues, proofreading, benefits of listening while reading research papers, text highlighting, speed controls, lifelike voices, ocr scanning, how you can listen to research papers aloud with the speechify website, how you can listen to research papers with the speechify chrome extension, how you can listen to research papers aloud with the speechify app, scan and listen to printed research papers with the speechify app, try speechify and read any text aloud, frequently asked questions.

Listen to research papers aloud and boost productivity and comprehension with our TTS .

In the realm of academia, research papers are a cornerstone for disseminating knowledge and contributing to the growth of various fields. However, the dense and technical nature of these papers can pose a challenge for many readers. Fortunately, text to speech (TTS) technology has emerged as a powerful tool to aid in the consumption of all academic papers. This article will explore different types of research papers, delve into the challenges of reading them, and highlight the benefits of using TTS, with a special focus on Speechify as a premier TTS app for academic purposes.

Research papers are a cornerstone of academic exploration, acting as vehicles for the dissemination of knowledge and the advancement of various fields. Within the realm of scholarly writing, a diverse array of research papers exists, each tailored to specific objectives and methodologies, including:

• Analytical research papers: These delve into breaking down and examining a subject, often presenting an in-depth analysis of complex ideas or concepts.
• Argumentative or persuasive research papers: These papers aim to convince the reader of a particular viewpoint, often involving the presentation of evidence and logical reasoning.
• Cause and effect research papers: Focused on exploring the relationships between events, these papers aim to identify the causes and consequences of a particular phenomenon.
• Compare and contrast research papers: These papers highlight similarities and differences between two or more subjects, encouraging critical thinking and analysis.
• Definition research papers: These aim to provide a comprehensive understanding of a specific concept or term, often clarifying its various facets.
• Experimental research papers: Centered around scientific experiments, these papers detail the methodology, results, and conclusions of research studies.
• Interpretative research papers: These involve the interpretation of data, literature, or artistic works, requiring a nuanced understanding of the subject matter.
• Survey research papers: Based on survey data, these papers analyze and present findings from questionnaires or interviews.

Text to speech (TTS) is a technology that converts written text into spoken language. This innovative system enables computers, devices, or applications to audibly articulate the content of written material, ranging from articles and documents to emails and web pages.

TTS works by processing the input text through algorithms that analyze linguistic elements, such as syntax and semantics, to generate a corresponding audio output. The synthesized speech can be delivered in a variety of voices and accents, often aiming for a natural and human-like sound.

TTS serves a crucial role in enhancing accessibility, aiding individuals with visual impairments or learning disabilities, and providing a versatile solution for consuming written content in situations where reading may be impractical or inconvenient.

Challenges of reading research papers and how text to speech can help

Studying often involves grappling with the challenges presented by research papers. As we navigate through these dense repositories of knowledge crucial for intellectual growth, one powerful ally emerges to mitigate these challenges: text to speech (TTS) technology. Let’s unravel the challenges posed by academic texts and delve into how TTS emerges as a transformative tool, enhancing accessibility, efficiency, and overall engagement:

One of the primary challenges of reading research papers is the abundance of technical language and specialized terminology. For individuals not well-versed in the specific field, deciphering these terms can be a daunting task. Text to speech (TTS) technology addresses this challenge by providing an auditory component to the reading process. Hearing the content aloud can aid in pronunciation, contextual understanding, and overall comprehension of intricate terms. By engaging multiple senses, TTS assists readers in navigating the intricate linguistic landscape of academic papers.

Research papers are often lengthy and densely packed with information, requiring dedicated time and mental focus to absorb the content fully. TTS can alleviate this challenge by allowing users to listen to papers while performing other tasks or listen at a faster rate than physical reading allows. By breaking down the information into manageable auditory segments, TTS enables users to absorb complex concepts without the need for prolonged, uninterrupted reading sessions.

Busy schedules, whether due to academic, professional, or personal commitments, can limit the time available for in-depth reading and analysis of research papers. TTS provides a solution by offering a more time-efficient means of consuming academic content. Users can listen to research papers during activities such as commuting, exercising, or doing household chores, maximizing the utility of their time and seamlessly integrating learning into their daily routines.

Traditional reading methods can pose accessibility challenges for individuals with conditions such as dyslexia, vision issues, or attention disorders. TTS technology serves as an inclusive solution, offering an alternative mode of content consumption. By listening to research papers, individuals with learning differences can overcome barriers related to text-based challenges, making academic content more accessible and fostering a more equitable learning environment. TTS also addresses eye strain issues associated with prolonged reading, promoting a more comfortable reading experience.

Writing research articles can be difficult and re-reading them for typos can seem even more daunting. Text to speech platforms offer a distinct advantage in catching typos and grammatical errors that might be easily missed during traditional visual proofreading. By listening to your research paper, you engage a different cognitive process, allowing you to detect discrepancies in syntax, grammar, and word choice more effectively. This dual approach to proofreading, both visual and auditory, enhances the overall accuracy of your written work, ensuring that typos are promptly identified and rectified, contributing to the production of polished and error-free research papers.

Listening while reading research papers can significantly enhance the learning experience. Combining auditory input with the visual engagement of reading creates a multimodal learning approach that caters to different learning styles. The act of listening to text to speech read research papers aloud can help improve concentration and maintain focus during the often rigorous and dense process of digesting such content. This dual-input method not only reinforces comprehension but also aids in retaining information by tapping into multiple cognitive channels. Additionally, it can make the learning process more dynamic and enjoyable, potentially reducing the perceived difficulty of understanding complex topics.

Why Speechify is the best text to speech for reading research papers

In the ever-expanding landscape of text to speech (TTS) applications, Speechify emerges as a standout contender, particularly for the discerning academic reader. Navigating the intricate realm of research papers demands a tool that not only provides seamless functionality but also caters to the diverse needs of scholars and learners. Speechify, with its comprehensive set of features and user-friendly design, stands out as the premier TTS app for reading research papers. Here are just a few unique features that position Speechify as the go-to TTS app for the academic community, elevating the reading experience for research papers to unprecedented heights:

Speechify offers text highlighting synchronized with the audio, facilitating better retention and comprehension. This feature is especially beneficial for individuals with dyslexia, ADHD, and other learning differences, who benefit substantially from following along with the text as it is read aloud.

Users can adjust the reading speed to suit their preferences, enabling a customized and comfortable listening experience. Students can easily slow down the reading as they take notes or speed up the reading to meet deadlines or boost productivity.

Speechify boasts a diverse range of 200+ natural-sounding voices indistinguishable from human speech across 30+ various languages and accents, accommodating a global audience and providing an immersive reading experience.

The OCR scanning functionality allows users to convert printed or handwritten text into digital format, enabling students to listen to any digital or physical text aloud.

How to read research papers aloud with Speechify

Speechify, the leading text to speech app, provides an unparalleled solution for listening to research papers aloud, offering a seamless and enriching experience for academic readers. In fact, let’s explore how you can use the Speechify website, Chrome extension , or app to listen to research papers, including how to listen to scanned research papers.

You can listen to research papers straight from the Speechify website. Simply follow the steps below:

• Open your web browser and navigate to Speechify.com
• Sign in or create an account if you haven't already.
• Tap “New” in the left-hand toolbar.
• Click “Text Document.”
• Copy and paste the research paper copy into the text box.
• Press submit.
• Customize the voice, reading speed, and other preferences.
• Click the "Play" button to listen to your research paper with Speechify.
• Enjoy a seamless and accessible reading experience right in your web browser.

If your favorite browser is Google Chrome, you can also listen to research papers by using the Speechify Chrome extension. Here’s a breakdown of how to get started:

• Install the Speechify Chrome extension from the Chrome Web Store.
• Click on the Speechify icon in your browser toolbar.
• Sign in or create an account.
• Select the text you want to read and choose your desired settings.
• Click the "Play" button on the Speechify pop-up to start the text to speech conversion.
• Listen to the content being read out loud while you browse the web, and even adjust settings on the fly.

If you’d like to read research papers on the go, follow this easy tutorial showing how to use the Speechify app:

• Download the Speechify IOS or Android app from the App store or Google Play store.
• Open the app and sign in or create a new account.
• Tap “Add” on the bottom toolbar.
• Choose “From your computer.”
• Choose files and import your research paper or copy and paste text into the app.
• Customize voice preferences, reading speed, and other settings.
• Tap the “Play” button to begin listening to the converted content.
• Use the app’s additional features, such as highlighting text or changing the voice for a more interactive reading experience.

You can even read printed research papers with Speechify. Follow this guide to use the Speechify app to scan pictures of your physical documents:

• Download the Speechify IOS or Android app on your mobile device from the App store or Google Play store.
• Choose “Scan Pages.”
• Grant Speechify access to your camera.
• Use the OCR scanner to take photos of the research paper you wish to convert to audio files.
• Press “Next” in the bottom right hand corner.
• Click “Listen” in the top right hand corner.
• Press “Save.”
• Tap the "Play" button to begin listening to the new audio version of your research paper.
• Customize the settings to suit your preferences, such as reading speed and voice selection.
• Enjoy hands-free learning while you focus on comprehension or follow along as the text is highlighted.

Navigate through dense research papers, craft concise summaries or Google Doc annotations, review social science notes, explore journal articles, read ChatGPT responses, or immerse yourself in academic journals, check emails, and listen to research papers with the help of Speechify. Whether you're a student, researcher, or lifelong learner, Speechify makes it easy to transform any text into speech. Try Speechify for free today and transform your reading experience all while taking advantage of its user-friendly design and innovative features.

Can text to speech read HTML tags?

Yes, text to speech software, such as NaturalReader or Speechify can read HTML tags and citations aloud, making it easier to follow the structure of the paper and understand the sources cited.

How can I listen to my paper aloud?

Speechify allows you to easily listen to any physical or digital text aloud. Sign up for free and check it out today.

How can text to speech benefit language learners?

Text to speech can benefit language learners by improving their pronunciation and listening skills, increasing vocabulary and comprehension, and providing access to a variety of materials in the target language.

What are the best podcasts for academic research?

For academic research, some of the best podcasts include "The Research Report Show" and "Research in Action," which provide insights into the latest research across various fields.

What are the best audiobooks for academic research?

Some of the best audiobooks about researching include, How to Read a Book by Mortimer Adler and The Craft of Research by Wayne Booth, Gregory Colomb, and Joseph Williams. These audiobooks are highly recommended for academic researchers.

How can I listen to research papers on an iPhone?

You can listen to any text aloud, including research papers on an iPhone using the Speechify app.

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Cliff Weitzman

Cliff Weitzman is a dyslexia advocate and the CEO and founder of Speechify, the #1 text-to-speech app in the world, totaling over 100,000 5-star reviews and ranking first place in the App Store for the News & Magazines category. In 2017, Weitzman was named to the Forbes 30 under 30 list for his work making the internet more accessible to people with learning disabilities. Cliff Weitzman has been featured in EdSurge, Inc., PC Mag, Entrepreneur, Mashable, among other leading outlets.

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• Youth Violence: What We Know and What We Need to Know (PDF, 388KB) American Psychologist January 2016 by Brad J. Bushman, Katherine Newman, Sandra L. Calvert, Geraldine Downey, Mark Dredze, Michael Gottfredson, Nina G. Jablonski, Ann S. Masten, Calvin Morrill, Daniel B. Neill, Daniel Romer, and Daniel W. Webster
• Supervenience and Psychiatry: Are Mental Disorders Brain Disorders? (PDF, 113KB) Journal of Theoretical and Philosophical Psychology November 2015 by Charles M. Olbert and Gary J. Gala
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• Resilience in the Study of Minority Stress and Health of Sexual and Gender Minorities (PDF, 40KB) Psychology of Sexual Orientation and Gender Diversity September 2015 by Ilan H. Meyer
• Self-Reported Psychopathy and Its Association With Criminal Cognition and Antisocial Behavior in a Sample of University Undergraduates (PDF, 91KB) Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement July 2015 by Samantha J. Riopka, Richard B. A. Coupland, and Mark E. Olver

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7 reasons Nvidia is poised to soar 67% as its rally continues for the next 2 years, according to a Wall Street research firm

• Constellation Research said Nvidia stock will soar 65% to $200 per share over the next year.
• The research firm said it expects Nvidia stock to continue soaring for the next 18 to 24 months as it benefits from its AI dominance.
• There are seven moats around Nvidia's business that will enable continued growth. 

Nvidia stock will surge to $200 per share over the next 12 months, and its ongoing rally is set to last up to another two years, according to Constellation Research.

Constellation founder R "Ray" Wang told CNBC on Monday that he believes Nvidia has seven moats that will help it maintain its dominant position in the market for GPUs that are fueling the AI boom.

"Nvidia is the foundational stock in the Age of AI. CEO Jensen Huang intends to achieve vertically integrated domination from silicon to software through partnerships and direct routes to market. Unlike the PC age where Microsoft, Intel, and Cisco served as a triumvirate foundational players, this new era will have new players all tied back to Nvidia," Wang told Business Insider in an e-mail on Monday.

These are the seven reasons Wang expects Nvidia stock to soar 65% from current levels.

1. Visionary founder-led CEO

"It's a visionary-led CEO, and that's very very important as you've seen in the valley. Those are the ones that have led, like the Larry Ellisons of the world, the scott Mcnealys, the Mark Zuckerbergs," Wang said.

2. High barrier to entry

"There's few competitors that can come into this chip market, and it takes a long time to get a chip to market, and if you can do that and if you succeed and then if you can actually get the right chip, that's a very hard thing to do."

3. High switching costs

"Once you're in, you're locked in because of the CUDA software and all the access to the chips, the software, and the entire stack. You're going to be locked in for quite some time and they've got quite a lead in terms of doing that."

4. Dominant market share

"Nvidia has had dominant market share, and I think that makes a big difference because they've been in this market for quite some time and the competitors are behind by 24 months."

5. Strong product roadmap

"We're only seeing one-tenth, maybe one-one hundredth of the product roadmap that Nvidia has out there, and that's really exciting for those who actually have some insight into what they have next, because it's more than just chips, and it's more than just what's happening in software. That ability to go from silicon all the way to the end side, that's where we're going to see a lot of the innovation."

6. GPU is the default standard in AI

"The ecosystem has made the GPU a default standard. It's the standard everyone's looking to for AI from inference and testing."

7. The numbers don't lie

"We're seeing some amazing growth here that actually matches the P/E ratio, and that's what everyone is looking at, they're trying to figure out how this is going to continue, but gross margins are 78%, 262% growth compared to a year ago, this is going to continue for at least the next 18 to 24 months."

Wang said the current 14% decline in the stock since it peaked at about $140 per share last week represents yet another buying opportunity for investors.

"The pullback is coming at a macro level. People are worried about the consumer side, people worried about where the economy is going to head, and they're doing some profit-taking before the summer, so I think it's a good time to buy the dip," Wang said.

Wang isn't the only analyst on Wall Street with a $200 price target for Nvidia stock.

Last week, Rosenblatt raised its Nvidia price target to $200 per share on the prospect of the company better monetizing its CUDA software platform.

• Main content

How Do Our Memories Last a Lifetime? New Study Offers a Biological Explanation

Whether it’s a first-time visit to a zoo or when we learned to ride a bicycle, we have memories from our childhoods kept well into adult years. But what explains how these memories last nearly an entire lifetime? 

A new study in the journal Science Advances , conducted by a team of international researchers, has uncovered a biological explanation for long-term memories. It centers on the discovery of the role of a molecule, KIBRA, that serves as a “glue” to other molecules, thereby solidifying memory formation.

“Previous efforts to understand how molecules store long-term memory focused on the individual actions of single molecules,” explains André Fenton, a professor of neural science at New York University and one of the study’s principal investigators. “Our study shows how they work together to ensure perpetual memory storage.”

“A firmer understanding of how we keep our memories will help guide efforts to illuminate and address memory-related afflictions in the future,” adds Todd Sacktor, a professor at SUNY Downstate Health Sciences University and one of the study’s principal investigators.

Discovery of a "glue" molecule's purpose affirms a concept introduced Nobel Laureate Francis Crick to explain the brain’s role in memory storage.

It’s been long-established that neurons store information in memory as the pattern of strong synapses and weak synapses, which determines the connectivity and function of neural networks. However, the molecules in synapses are unstable, continually moving around in the neurons, and wearing out and being replaced in hours to days, thereby raising the question: How, then, can memories be stable for years to decades?  

In a study using laboratory mice, the scientists focused on the role of KIBRA, or kidney and brain expressed protein, the human genetic variants of which are associated with both good and poor memory. They focused on KIBRA’s interactions with other molecules crucial to memory formation—in this case, protein kinase Mzeta (PKMzeta). This enzyme is the most crucial molecule for strengthening normal mammalian synapses that is known, but it degrades after a few days.

Their experiments reveal that KIBRA is the “missing link” in long-term memories, serving as a “persistent synaptic tag,” or glue, that sticks to strong synapses and to PKMzeta while also avoiding weak synapses.

“During memory formation the synapses involved in the formation are activated—and KIBRA is selectively positioned in these synapses,” explains Sacktor, a professor of physiology, pharmacology, anesthesiology, and neurology at SUNY Downstate. “PKMzeta then attaches to the KIBRA-synaptic-tag and keeps those synapses strong. This allows the synapses to stick to newly made KIBRA, attracting more newly made PKMzeta.”

More specifically, their experiments in the Science Advances paper show that breaking the KIBRA-PKMzeta bond erases old memory. Previous work had shown that randomly increasing PKMzeta in the brain enhances weak or faded memories, which was mysterious because it should have done the opposite by acting at random locations, but the persistent synaptic tagging by KIBRA explains why the additional PKMzeta was memory enhancing, by only acting at the KIBRA tagged sites. 

Memories are stored by the interaction of two proteins: a structural protein, KIBRA (green), that acts as a persistent synaptic tag, and a synapse-strengthening enzyme, protein kinase Mzeta (red). Drugs that disrupt the memory-perpetuating interaction (other colors) erase pre-established long-term and remote memories. Credit: Changchi Hsieh, Ph.D.

“The persistent synaptic tagging mechanism for the first time explains these results that are clinically relevant to neurological and psychiatric disorders of memory,” observes Fenton, who is also on the faculty at NYU Langone Medical Center’s Neuroscience Institute. 

The paper’s authors note that the research affirms a concept introduced in 1984 by Francis Crick. Sacktor and Fenton point out that his proposed hypothesis to explain the brain’s role in memory storage despite constant cellular and molecular changes is a Theseus’s Ship mechanism—borrowed from a philosophical argument stemming from Greek mythology in which new planks replace old ones to maintain Theseus’s Ship for years.

“The persistent synaptic tagging mechanism we found is analogous to how new planks replace old planks to maintain Theseus’s Ship for generations, and allows memories to last for years even as the proteins maintaining the memory are replaced,” says Sacktor. “Francis Crick intuited this Theseus’s Ship mechanism, even predicting the role for a protein kinase. But it took 40 years to discover that the components are KIBRA and PKMzeta and to work out the mechanism of their interaction.”

The study also included researchers from Canada’s McGill University, Germany’s University Hospital of Münster, and University of Texas Medical School at Houston.

This work was supported by grants from the National Institutes of Health (R37 MH057068, R01 MH115304, R01 NS105472, R01 MH132204, R01 NS108190), the Natural Sciences and Engineering Research Council of Canada Discovery (203523), and the Garry and Sarah S. Sklar Fund.

About New York University Founded in 1831, NYU is one of the world’s foremost research universities (with more than $1 billion per year in research expenditures, it is ranked seventh among private research universities) and is a member of the selective Association of American Universities. NYU has degree-granting university campuses in New York, Abu Dhabi, and Shanghai; has 13 other global academic sites, including London, Paris, Florence, Tel Aviv, Buenos Aires, and Accra, and US sites in Washington, DC, Los Angeles, CA, and Tulsa, OK; and both sends more students to study abroad and educates more international students than any other U.S. college or university. Through its numerous schools and colleges, NYU is a leader in conducting research and providing education in the arts and sciences, law, medicine, business, dentistry, engineering, education, nursing, the cinematic and performing arts, music and studio arts, public service, social work, public health, and professional studies, among other areas.

About SUNY Downstate Health Sciences University Downstate Health Sciences University in Brooklyn is one of four academic health centers (AMCs) in the 64-campus State University of New York (SUNY) system and the only SUNY AMC in New York City dedicated to health education, research, and patient care for the borough’s 2.7 million residents. Its flagship hospital, University Hospital at Downstate (UHD), is a teaching hospital that benefits from the expertise of Downstate’s exceptional medical school and world-class research facilities. Beyond its clinical excellence, Downstate houses a range of esteemed educational institutions, including the College of Medicine, College of Nursing, School of Health Professions, School of Graduate Studies, and School of Public Health. Downstate fosters innovation through its multifaceted biotechnology initiatives, including the Biotechnology Incubator and BioBAT, which support both early-stage and more mature biotech companies. Downstate’s research enterprise drives innovation and discovery across a wide array of disciplines. Our investigators are making discoveries that are changing the world and pushing the boundaries of what is possible in biomedicine and healthcare.

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• Americans’ Views of Government’s Role: Persistent Divisions and Areas of Agreement

Wide majorities of Biden and Trump supporters oppose cuts to Social Security

Table of contents.

• Views on the efficiency of government
• Views on the government’s regulation of business
• Confidence in the nation’s ability to solve problems
• Views on the effect of government aid to the poor
• Views on government’s role in health care
• Views on the future of Social Security
• Trust in government
• Feelings toward the federal government
• Acknowledgments
• The American Trends Panel survey methodology

Pew Research Center conducted this study to understand Americans’ attitudes about U.S. government, such as its size and role.

This report is based primarily on a survey of 8,709 adults, including 7,166 registered voters, from April 8 to 14, 2024. Some of the analysis in this report is based on a survey of 8,638 adults from May 13 to 19, 2024.

Everyone who took part in these surveys is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for the report and its methodology .

While the economy, immigration and abortion have emerged as major issues in the 2024 election, Joe Biden and Donald Trump also have dramatically different ideas about the size and role of government.

These differences reflect decades-old divisions between Democrats and Republicans over the scope of government.

Among registered voters, large majorities of Biden supporters – roughly three-quarters or more – favor a bigger, more activist government.

• 74% say they would rather have a bigger government providing more services.
• 76% say government should do more to solve problems.
• 80% say government aid to the poor “does more good than harm.”

Trump supporters, by comparable margins, take the opposing view on all three questions.

The Pew Research Center survey of 8,709 adults – including 7,166 registered voters – conducted April 8-14, 2024, examines Americans’ views of the role and scope of government , the social safety net and long-term trends in trust in the federal government .

Democratic support for bigger government is little changed in the last five years but remains higher than it was a decade ago. Republicans’ views have shifted less over the last 10 years.

Among all adults, about three-quarters of Democrats and Democratic-leaning independents favor a bigger government, up from about six-in-ten in 2014 and 2015. The share of Republicans and Republican leaners who prefer a bigger government has increased only modestly over the same period.

Democratic support for bigger government, while slightly lower than in 2021 (78%), remains at nearly its highest level in five decades. During Bill Clinton’s presidency in the 1990s, fewer than half of Democrats said they preferred a bigger government with more services.

Voters continue to express very different views about government’s role in specific areas than about the government generally.

A large majority of voters (80%) – including 82% of Biden supporters and 78% of Trump supporters – say that in thinking about the long-term future of Social Security, benefits should not be reduced in any way.

However, Biden supporters are more likely than Trump supporters to say Social Security should cover more people with greater benefits.

• 46% of Biden supporters favor expanding Social Security coverage and benefits, compared with 28% of Trump supporters.

Most Americans (65%) continue to say the federal government has a responsibility to make sure all Americans have health care coverage.

Democrats overwhelmingly (88%) say the federal government has this responsibility, compared with 40% of Republicans.

The share of Republicans who say the government has a responsibility to provide health coverage has increased 8 percentage points since 2021, from 32% to 40%.

There are wide income differences among Republicans in opinions about the government’s role in health care:

• 56% of Republicans with lower family incomes say the government has a responsibility to provide health coverage for all, compared with 36% of those with middle incomes and 29% of higher-income Republicans.

When asked how the government should provide health coverage, 36% of Americans say it should be provided through a single national program, while 28% say it should be through a mix of government and private programs. These views have changed little in recent years.

Democrats continue to be more likely than Republicans to favor a “single payer” government health insurance program (53% vs. 18%).

Other key findings in this report

• Americans’ trust in the federal government remains low but has modestly increased since last year. Today, 22% of American adults say they trust the government to do what is right always or most of the time, which is up from 16% in June 2023.
• While the public overall is divided over the nation’s ability to solve important problems, young adults are notably pessimistic about the country’s ability to solve problems . About half of Americans (52%) say the U.S. can’t solve many of its important problems, while 47% say it can find a way to solve problems and get what it wants. Roughly six-in-ten adults under age 30 (62%) say the nation can’t solve major problems, the highest share in any age group and 16 points higher than two years ago.

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6 facts about presidential and vice presidential debates, biden, trump are least-liked pair of major party presidential candidates in at least 3 decades, cultural issues and the 2024 election, more than half of americans are following election news closely, and many are already worn out, most popular, report materials.

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Research: Using AI at Work Makes Us Lonelier and Less Healthy

• David De Cremer
• Joel Koopman

Employees who use AI as a core part of their jobs report feeling more isolated, drinking more, and sleeping less than employees who don’t.

The promise of AI is alluring — optimized productivity, lightning-fast data analysis, and freedom from mundane tasks — and both companies and workers alike are fascinated (and more than a little dumbfounded) by how these tools allow them to do more and better work faster than ever before. Yet in fervor to keep pace with competitors and reap the efficiency gains associated with deploying AI, many organizations have lost sight of their most important asset: the humans whose jobs are being fragmented into tasks that are increasingly becoming automated. Across four studies, employees who use it as a core part of their jobs reported feeling lonelier, drinking more, and suffering from insomnia more than employees who don’t.

Imagine this: Jia, a marketing analyst, arrives at work, logs into her computer, and is greeted by an AI assistant that has already sorted through her emails, prioritized her tasks for the day, and generated first drafts of reports that used to take hours to write. Jia (like everyone who has spent time working with these tools) marvels at how much time she can save by using AI. Inspired by the efficiency-enhancing effects of AI, Jia feels that she can be so much more productive than before. As a result, she gets focused on completing as many tasks as possible in conjunction with her AI assistant.

• David De Cremer is a professor of management and technology at Northeastern University and the Dunton Family Dean of its D’Amore-McKim School of Business. His website is daviddecremer.com .
• JK Joel Koopman is the TJ Barlow Professor of Business Administration at the Mays Business School of Texas A&M University. His research interests include prosocial behavior, organizational justice, motivational processes, and research methodology. He has won multiple awards from Academy of Management’s HR Division (Early Career Achievement Award and David P. Lepak Service Award) along with the 2022 SIOP Distinguished Early Career Contributions award, and currently serves on the Leadership Committee for the HR Division of the Academy of Management .

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Guest Essay

What Happened to Stanford Spells Trouble for the Election

By Renée DiResta

Ms. DiResta is the former research director of the Stanford Internet Observatory, a unit of Stanford University that studies abuse of online platforms.

In 2020 the Stanford Internet Observatory, where I was until recently the research director, helped lead a project that studied election rumors and disinformation. As part of that work, we frequently encountered conspiratorial thinking from Americans who had been told the 2020 presidential election was going to be stolen.

The way theories of “the steal” went viral was eerily routine . First, an image or video, such as a photo of a suitcase near a polling place, was posted as evidence of wrongdoing. The poster would tweet the purported evidence, tagging partisan influencers or media accounts with large followings. Those accounts would promote the rumor, often claiming, “Big if true!” Others would join, and the algorithms would push it out to potentially millions more. Partisan media would follow.

If the rumor was found to be false — and it usually was — corrections were rarely made and even then, little noticed. The belief that “the steal” was real led directly to the events of Jan. 6, 2021.

Within a couple of years, the same online rumor mill turned its attention to us — the very researchers who documented it. This spells trouble for the 2024 election.

For us, it started with claims that our work was a plot to censor the right. The first came from a blog related to the Foundation for Freedom Online, the project of a man who said he “ran cyber” at the State Department. This person, an alt-right YouTube personality who’d gone by the handle Frame Game, had been employed by the State Department for just a couple of months .

Using his brief affiliation as a marker of authority, he wrote blog posts styled as research reports contending that our project, the Election Integrity Partnership, had pushed social media networks to censor 22 million tweets. He had no firsthand evidence of any censorship, however: his number was based on a simple tally of viral election rumors that we’d counted and published in a report after the election was over. Right-wing media outlets and influencers nonetheless called it evidence of a plot to steal the election, and their followers followed suit.

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