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On computer science research and its temporal evolution

• Published: 30 July 2022
• Volume 127 , pages 4913–4938, ( 2022 )

Cite this article

• Camil Demetrescu   ORCID: orcid.org/0000-0002-4686-6745 1 ,
• Irene Finocchi   ORCID: orcid.org/0000-0002-6394-6798 2 ,
• Andrea Ribichini   ORCID: orcid.org/0000-0002-0281-4257 1 &
• Marco Schaerf   ORCID: orcid.org/0000-0002-2016-1966 1  

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In this article, we study the evolution of the computer science research community over the past 30 years. Analyzing data from the full Scopus database, we investigate how aspects such as the community size, gender composition, and academic seniority of its members changed over time. We also shed light on the varying popularity of specific research areas, as derived from the ACM’s Special Interest Groups and IEEE classifications. Our analysis spans 19 nations (all members of the G20 group, excluding the EU) and involves a total of 728,374 authors and 8,412,543 publications. This work shows that the overall size of the computer science community has increased by a factor of ten in the time period 1991–2020, with China and India enjoying the highest growth. At the same time, this increase has not been uniform across research areas. Female participation has also increased, but more slowly than expected and not uniformly across countries and areas.

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Prof. Demetrescu, Prof. Finocchi and Dr. Ribichini were partially supported for this work by MIUR, the Italian Ministry of Education, University and Research, under PRIN Project n. 20174LF3T8 AHeAD (Efficient Algorithms for HArnessing Networked Data).

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Demetrescu, C., Finocchi, I., Ribichini, A. et al. On computer science research and its temporal evolution. Scientometrics 127 , 4913–4938 (2022). https://doi.org/10.1007/s11192-022-04445-z

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Parts of a Technical Paper

The basic parts of a scientific or technical paper are:

Title and Author Information Abstract Introduction Literature Review Methods Results Discussion Conclusions References and Appendices

Detailed Explanation for Each Part

Title and Author Information:

The title of your paper and any needed information about yourself (usually your name and institution).

A short (usually around 250-400 words) description of the paper. Should include what the purpose of the paper is (including the basic research question/problem), the basic design of your project, and the major findings.

Introduction:

A general introduction to your topic and what you expect to learn from your project or experiment. Your research question should be found here.

Literature Review:

An analysis of what has already been published about your chosen topic. Should be able to show how your research question fits into the context of your field.

A description of everything you did in your experiment or project, step-by-step. Needs to be detailed enough so that any reader would be able to repeat each step exactly on their own.

What actually happened during your project or what you found at the end of your experiment. This is usually the best part to include the majority of your graphs, photos, tables, and other visual aids, as long as they help explain the results of your work.

Discussion:

An analysis of the results that integrates what you found into the wider body of research in your field. Can also include future hypotheses to be tested or future projects to build from your own.

Conclusion:

Can be included in the discussion if necessary. A final summary of the paper, including whether or not you were able to answer your original research question.

References and Appendices:

The reference page(s) is a list of all the sources you used to research and create your project/experiment, including everything cited in the literature review and methods sections. Remember to use the same citation style throughout the paper. An appendix would include any additional information about your work that you were not able to include within the body of your paper (like large datasets and figures) that would help readers better understand your results.

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Computer Science

Overview - library research for computer science, quick start, need more help, tutorial: understanding journals, literature searches, what is libkey nomad, library article databases, browse journals using browzine, find computer science books, computer science books you can check out or read online, e-book collections, e-reference sources, find web sources, associations, organizations, & societies, interesting websites, find journals, finding a specific journal, browsing journals using browzine, interlibrary loan, library research for computer science.

This guide will help you get started searching the computer science "literature" for research papers on your topic and finding other resources such as books, journals, government websites, and published theses and dissertations.

Be sure to visit the different pages of this guide using the tabs on the left.

Library Search connects you to books, articles, and a variety of other library sources.

Library Search at Rowan University Libraries

Schedule an appointment with me:

• Request a Research Consultation
• Understanding Journals tutorial A short online tutorial for students on how to understand and use library journals.

Computer Science Articles

We recommend that you use the specialized subscription databases on the library website to search for engineering "literature" (papers) because you will see only content that the library subscribes to. 

However if you prefer to use Google Scholar, you can create a Google Scholar profile with your Rowan University credentials , which will give you access to the library's subscription content.

Then when you search in Google Scholar, you will see links to the right of some results labeled Full Text @Rowan University . Selecting one of these links will take you to the subscribed content.

LibKey Nomad is a browser extension for the Chrome, Firefox, and Edge web browsers.

Once you install it, you can use it to download full text articles to which we subscribe, from any publisher website where you find them. (Otherwise you would need to go to the library website to access the full text if you are off campus.)

When the full text PDF is available to you, you will see a LibKey Download PDF icon in the bottom left corner of the page.

If it is not, you will see a button labeled Access Options which will direct you to our Interlibrary Loan system.

Watch the short video shown below to learn how to download the extension for the browser you use.  

• LibKey Nomad Installation Video showing you how to install LibKey Nomad

The following subscription databases available through the library website contain scholarly articles in Computer Science.

• ACM Digital Library This link opens in a new window Academic journal articles in computer science. more... less... Full text of every article ever published by ACM (the professional organization for computer scientists) and bibliographic citations from major publishers in computing. (WALDO consortial access.)
• IEEE Xplore This link opens in a new window Journal and conference papers in electrical engineering. more... less... Full text access to journals and major conference proceedings published by the Institute of Electrical and Electronic Engineers (IEEE).
• MathSciNet This link opens in a new window The American Mathematical Society’s index to mathematical literature. more... less... Contains references, abstracts, and reviews from mathematical journals, conference proceedings, books, and dissertations.
• Web of Science This link opens in a new window Full text database of scientific journal articles with data on who has cited them. more... less... Web of Science® provides immediate data on who has cited research papers, covering over 12,000 of the highest impact journals worldwide, including Open Access journals and over 150,000 conference proceedings. You'll find current and retrospective coverage in the sciences, social sciences, arts, and humanities, with coverage to 1900.
• Scopus This link opens in a new window SciVerse Scopus is the world’s largest abstract and citation database of peer-reviewed literature and quality web sources.
• SIAM Journals Online Collection of journals published by the Society for Industrial and Applied Mathematics. more... less... Rowan account required.

Computer Science Books

This page explains ways to find computer science books through the library.

Computer science books are located in the QA76 section on the 4th floor of Campbell Library. Most programming books are in QA76 but some are in the TK (computer engineering) section.

• LibrarySearch Use Library Search to look up books by topic or title. Use the Books facet.
• Ebook Central ebook collection This link opens in a new window Collection of scholarly e-books in many academic disciplines. more... less... Multidisciplinary collection of scholarly ebooks, offering a strong collection of academic titles from leading scholarly publishers. Includes subscribed and purchased content. It is not a permanent acquisition of e-books, and is subject to change.

These subscription sources are available through the library website.

• Credo Reference - Science An online database which includes many science dictionaries and encyclopedias.

Computer Science Websites

This page highlights good websites for computer science research. 

• American Association for Artificial Intelligence more... less... The AAAI, founded in 1979, is a scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. Their site contains information on artificial intelligence, AAAI publications (book, journals, conference proceedings, and technical papers); conference, workshop, and symposia information; and membership benefits.
• ANSI standards search
• Association for Computing Machinery more... less... Billing itself as "the first society in computing," the ACM is the world's first educational and scientific computing society. Founded in 1947, its membership currently totals over 80,000 computing professionals and students world wide. The site includes information about ACM activities, services, conferences, publications, and policies. The ACM Digital Library contains full text of articles and papers from all of the their journals, magazines, and proceedings.
• IEEE Computer Society more... less... Founded in 1947, the IEEE Computer Society is the world's oldest and largest (98,000 members) professional association of people in computing. The site contains a full range of information about conferences, standards, publications, activities, education, certification, and employment. The Digital Library provides access to the Computer Society's magazines, transactions, and a growing body of conference proceedings.
• Society for Industrial and Applied Mathematics more... less... A group of professionals formed SIAM in 1952 to advance the application of mathematics to science and industry. SIAM members are computer scientists, mathematicians, engineers, statisticians, and engineers. This site includes information about their publications, conferences, meetings.
• Special Interest Group on Human-Computer Interaction
• ArXiv Pre-print repository of articles in Physics, Mathematics, and Computer Science maintained by Cornell University.
• Software Engineering Institute SEI is a federally-funded research and development center headquartered on the campus of Carnegie Mellon University.
• HCI Bibliography A site offering a bibliography of human-computer interaction resources.
• How to Get a Computer Science Job Commercial website with advice on getting your first professional job in computer science.

Computer Science Journals

This page offers several options for finding scientific journals containing research on computer science.

To discover whether the library subscribes to a specific journal, and see content in that journal, go to the Search Tools section of the Campbell Library home page. Click on the Journal Finder option.

Enter the complete name of the journal (no abbreviations) - for example, "Environmental & Engineering Geoscience."

If the journal is listed in the results, click on Available Online . Then scroll down to View Online and choose a database link. In this example there should be a link for the database GeoScienceWorld .

If you are looking for a specific issue of the journal, there is usually a way to browse volumes and issues to drill down to the desired issue.

Or, search within the journal using the Search box in the top right corner to find articles on a topic.

Journal articles to which we do not have full text access may be requested through our online Interlibrary Loan request system, Illiad. Articles are usually sent to your email address within a few days.

• Campbell Library Interlibrary Loan logon
• Last Updated: May 16, 2024 1:45 PM
• URL: https://libguides.rowan.edu/ComputerScience

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University library, search uic library collections.

Find items in UIC Library collections, including books, articles, databases and more.

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Search UIC Library Website

Find items on the UIC Library website, including research guides, help articles, events and website pages.

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• Computer Science

How to Read a Scientific Paper

Computer science: how to read a scientific paper.

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Structure of a Technical Paper

• How to Read a Paper A short work on how to read academic papers, organized as an academic paper. Some of the advice on doing a literature survey works better in the author's field (CS) but most the material works for everyone.
• How to Read a Research Paper Part of an assignment on how to read academic papers for a CS class, it describes some strategies and lays out some expectations in terms of time and effort that should be useful.
• How to Read Scientific Papers Without Reading Every Word A blog post that gives a similar but differently worded take on the same issue.
• How to read Mathematics An article discussing how to go about reading a math article or chapter.

  Health Promotion Practice. (2020). How to Read a Scholarly Article [Infographic].

 http://healthpromotionpracticenotes.com/2020/07/new-  tool-how-to-read-a-scholarly-article-infographic/  

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Journal of Computer Science

Aims and scope.

The Journal of Computer Science (JCS) is dedicated to advancing computer science by publishing high-quality research and review articles that span both theoretical foundations and practical applications in information, computation, and computer systems. With a commitment to excellence, JCS offers a platform for researchers, scholars, and industry professionals to share their insights and contribute to the ongoing evolution of computer science. Published on a monthly basis, JCS provides up-to-date insights into this ever-evolving discipline.

Science Publications is pleased to announce the launch of a new open access journal, Journal of Adaptive Structures. JAS brings together emerging technologies for adaptive smart structures, including advanced materials, smart actuation, sensing and control, to pursue the progressive adoption of the major scientific achievements in this multidisciplinary field on-board of commercial aircraft.

It is with great pleasure that we announce the SGAMR Annual Awards 2020. This award is given annually to Researchers and Reviewers of International Journal of Structural Glass and Advanced Materials Research (SGAMR) who have shown innovative contributions and promising research as well as others who have excelled in their Editorial duties.

This special issue "Neuroinflammation and COVID-19" aims to provide a space for debate in the face of the growing evidence on the affectation of the nervous system by COVID-19, supported by original studies and case series.

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SCIgen - An Automatic CS Paper Generator

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• We initially based SCIgen on Chris Coyne's grammar for high school papers; Chris is now making neat pictures with context-free grammars.

May 16, 2024

How New Science Fiction Could Help Us Improve AI

We need to tell a new story about AI, and fiction has that power, humanities scholars say

By Nick Hilden

Andrey Suslov/Getty Images

For the past decade, a group called the Future of Life Institute has been campaigning for human welfare in public conversations around nuclear weapons, climate change, artificial intelligence and other evolving threats. The nonprofit organization aims to steer technological development away from the dystopian visions that so frequently haunt media. But when it comes to discussions about artificial intelligence, its team has had to face one especially persistent foe: the Terminator .

“When we first started talking about AI risk, every article that came out about our work had a Terminator in it,” says Emilia Javorsky, director of the institute’s Futures program. The Terminator film franchise’s specter of a powerful and antagonistic robot that is driven only by ruthless logic is hard to dispel. Ask people to imagine a powerful artificial intelligence, and they tend to think of the fictional archetype of a machine with a “Machiavellian soul,” Javorsky adds—even though actual AI systems inherently “have no malevolence, no human intent to them whatsoever.”

Recognizing the influence that popular narratives have on our collective perceptions, a growing number of AI and computer science experts now want to harness fiction to help imagine futures in which algorithms don’t destroy the planet. The arts and humanities, they argue, must play a role to ensure AI serves human goals. To that end, Nina Beguš, an AI researcher at the University of California, Berkeley, advocates for a new discipline that she calls the “artificial humanities.” In her upcoming book Artificial Humanities: A Fictional Perspective on Language in AI, she contends that the “responsibility of making these technologies is too big for the technologists to bear it alone.” The artificial humanities, she explains, would fuse science and the arts to leverage fiction and philosophy in the exploration of AI’s benevolent potential.

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“The humanities simply have to be part of the conversation, or this new world advances without our input,” says cultural historian Catherine Clarke of the University of London, who has studied the intersection of literature and AI.

Entertainment strongly shapes people’s perceptions of AI, as a recent public opinion study by researchers at the University of Texas at Austin shows. These depictions, however, frequently ignore positive technological potential in favor of portraying our worst fears. “We need fictional works that consider machines for what they are and articulate what their intelligence and creativity could be,” Beguš says. And because fiction is “not obliged to mirror actual technological developments,” it can be a “public space for experimentation and reflection.”

Importantly, it also turns out that our entertainment-fueled negative impressions of AI can, in turn, influence how the technology performs in the real world; the stories we tell ourselves about AI prime us to use it in certain ways. Preconceptions that an AI chatbot will answer like a manipulative machine initiate a hostile feedback loop so that the bot acts as expected, according to a recent study by researchers at the Massachusetts Institute of Technology Media Lab. A user’s internalized fears can be self-fulfilling, seasoning an algorithm with adversarial ingredients. So it may be that if fiction trains us to expect the worst from AI, that’s exactly what we’ll get.

But if we treat AI models with some finesse, they will respond in kind. Clarke, along with Murray Shanahan of Imperial College London and Google DeepMind, recently sought to determine whether a text-generating AI could be coached to deliver human-quality prose. They provided the beginning of a story to a chatbot and used prompts of varying detail and complexity to ask it to complete the narrative. As their preprint results found, stories composed by an AI that was given crude prompts fell flat, but more elegant and creatively refined prompts led to more literary prose. This suggests that what we give to a generative AI is returned to us.

“Why do we always imagine science fiction to be a dystopia? Why can’t we imagine science fiction that gives us hope?” — Pat Pataranutaporn, M.I.T. Media Lab

If these patterns hold true for more intelligent forms of AI, we need to instill them with scruples before we flip their “on” switches. The University of Oxford’s AI doomsayer Nick Bostrom has called this need “philosophy with a deadline.”

To pull more artists and thinkers into that discussion, the Future of Life Institute has sponsored multiple initiatives linking fiction writers and other creatives with technologists. “You can't mitigate risks that you can’t imagine,” Javorsky says. “You also can’t build positive futures with technology and steer toward those if you’re not imagining them.” The institute’s Worldbuilding Competition , for example, brings together multidisciplinary teams to conceptualize various friendly-AI futures. Those imagined tomorrows include a world in which a centralized AI manages the equitable distribution of goods. A second scenario suggests a system of digital nations that are free of geographic bounds. In yet another, artificial governance programs advocate for peace. In a fourth, AI helps us achieve a more inclusive society .

Merely imagining such worlds, where growth and innovation no longer depend on conventional human labor, allows fiction writers and other thinkers to ask provocative questions, Javorsky says: “What does meaning look like? What does aspiration look like? How do we rethink human purpose and agency in a world of shared abundance?”

The Future of Life Institute has also joined forces with an organization called Hollywood, Health & Society and other organizations to form the Blue Sky Scriptwriting Contest , which awards writers for creating television scripts that depict fair and equitable applications for artificial intelligence.

“We’ve all seen lots of dystopian and postapocalyptic futures in popular entertainment,” says Hollywood, Health & Society’s program director Kate Langrall Folb. There are “very few depictions of a greener, safer, more just future.” The inaugural contest was held in 2022, with prizes awarded last year. In that competition, the winning entry was set in a town where AI equally serves the needs of all residents, who are shaken when a once-in-a-generation murder complicates their potential techno-utopia. In another, AI-powered advisers equipped with Indigenous wisdom support a more sustainable society. Another tells of an Earth where AI has moved all manufacturing and heavy infrastructure off-planet, regenerating the terrestrial ecosystems below.

To further inspire these lines of thinking, the Future of Life Institute is in the process of producing a free, publicly available “Worldbuilding” course to train participants in hope rather than doom when it comes to AI. And once a person has managed to escape the doom loop, Javorsky says, it can be difficult to know where to direct efforts at developing positive AI. To address this, the institute is developing detailed scenario maps that suggest where different trajectories and decision points could lead this technology over the long run. The intention is to bring these scenarios to creative, artistic people who will then flesh out these stories, pursuing the crossover between technology and creativity—and providing AI developers with ideas about where different courses of action may take us.

This moment desperately needs “the power of storytelling and the humanities,” Javorsky says, to steer people away from the Terminator and toward a future where they’d be excited to live alongside AI—in peace and felicity.

“We need to come up with a new story,” says Pat Pataranutaporn, a researcher at the M.I.T. Media Lab and a co-author of the study on AI user preconceptions. “Why do we always imagine science fiction to be a dystopia? Why can’t we imagine science fiction that gives us hope?”

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• Published: 02 August 2023

Scientific discovery in the age of artificial intelligence

• Hanchen Wang   ORCID: orcid.org/0000-0002-1691-024X 1 , 2   na1   nAff37   nAff38 ,
• Tianfan Fu 3   na1 ,
• Yuanqi Du 4   na1 ,
• Wenhao Gao 5 ,
• Kexin Huang 6 ,
• Ziming Liu 7 ,
• Payal Chandak   ORCID: orcid.org/0000-0003-1097-803X 8 ,
• Shengchao Liu   ORCID: orcid.org/0000-0003-2030-2367 9 , 10 ,
• Peter Van Katwyk   ORCID: orcid.org/0000-0002-3512-0665 11 , 12 ,
• Andreea Deac 9 , 10 ,
• Anima Anandkumar 2 , 13 ,
• Karianne Bergen 11 , 12 ,
• Carla P. Gomes   ORCID: orcid.org/0000-0002-4441-7225 4 ,
• Shirley Ho 14 , 15 , 16 , 17 ,
• Pushmeet Kohli   ORCID: orcid.org/0000-0002-7466-7997 18 ,
• Joan Lasenby 1 ,
• Jure Leskovec   ORCID: orcid.org/0000-0002-5411-923X 6 ,
• Tie-Yan Liu 19 ,
• Arjun Manrai 20 ,
• Debora Marks   ORCID: orcid.org/0000-0001-9388-2281 21 , 22 ,
• Bharath Ramsundar 23 ,
• Le Song 24 , 25 ,
• Jimeng Sun 26 ,
• Jian Tang 9 , 27 , 28 ,
• Petar Veličković 18 , 29 ,
• Max Welling 30 , 31 ,
• Linfeng Zhang 32 , 33 ,
• Connor W. Coley   ORCID: orcid.org/0000-0002-8271-8723 5 , 34 ,
• Yoshua Bengio   ORCID: orcid.org/0000-0002-9322-3515 9 , 10 &
• Marinka Zitnik   ORCID: orcid.org/0000-0001-8530-7228 20 , 22 , 35 , 36  

Nature volume  620 ,  pages 47–60 ( 2023 ) Cite this article

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A Publisher Correction to this article was published on 30 August 2023

This article has been updated

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI tools need a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

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A Correction to this paper has been published: https://doi.org/10.1038/s41586-023-06559-7

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Acknowledgements

M.Z. gratefully acknowledges the support of the National Institutes of Health under R01HD108794, U.S. Air Force under FA8702-15-D-0001, awards from Harvard Data Science Initiative, Amazon Faculty Research, Google Research Scholar Program, Bayer Early Excellence in Science, AstraZeneca Research, Roche Alliance with Distinguished Scientists, and Kempner Institute for the Study of Natural and Artificial Intelligence. C.P.G. and Y.D. acknowledge the support from the U.S. Air Force Office of Scientific Research under Multidisciplinary University Research Initiatives Program (MURI) FA9550-18-1-0136, Defense University Research Instrumentation Program (DURIP) FA9550-21-1-0316, and awards from Scientific Autonomous Reasoning Agent (SARA), and AI for Discovery Assistant (AIDA). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. We thank D. Hassabis, A. Davies, S. Mohamed, Z. Li, K. Ma, Z. Qiao, E. Weinstein, A. V. Weller, Y. Zhong and A. M. Brandt for discussions on the paper.

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Hanchen Wang

Present address: Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA

Present address: Department of Computer Science, Stanford University, Stanford, CA, USA

These authors contributed equally: Hanchen Wang, Tianfan Fu, Yuanqi Du

Authors and Affiliations

Department of Engineering, University of Cambridge, Cambridge, UK

Hanchen Wang & Joan Lasenby

Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

Hanchen Wang & Anima Anandkumar

Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Department of Computer Science, Cornell University, Ithaca, NY, USA

Yuanqi Du & Carla P. Gomes

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Wenhao Gao & Connor W. Coley

Department of Computer Science, Stanford University, Stanford, CA, USA

Kexin Huang & Jure Leskovec

Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA

Payal Chandak

Mila – Quebec AI Institute, Montreal, Quebec, Canada

Shengchao Liu, Andreea Deac, Jian Tang & Yoshua Bengio

Université de Montréal, Montreal, Quebec, Canada

Shengchao Liu, Andreea Deac & Yoshua Bengio

Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA

Peter Van Katwyk & Karianne Bergen

Data Science Institute, Brown University, Providence, RI, USA

NVIDIA, Santa Clara, CA, USA

Anima Anandkumar

Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA

Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA

Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA

Department of Physics and Center for Data Science, New York University, New York, NY, USA

Google DeepMind, London, UK

Pushmeet Kohli & Petar Veličković

Microsoft Research, Beijing, China

Tie-Yan Liu

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

Arjun Manrai & Marinka Zitnik

Department of Systems Biology, Harvard Medical School, Boston, MA, USA

Debora Marks

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Debora Marks & Marinka Zitnik

Deep Forest Sciences, Palo Alto, CA, USA

Bharath Ramsundar

BioMap, Beijing, China

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

University of Illinois at Urbana-Champaign, Champaign, IL, USA

HEC Montréal, Montreal, Quebec, Canada

CIFAR AI Chair, Toronto, Ontario, Canada

Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

Petar Veličković

University of Amsterdam, Amsterdam, Netherlands

Max Welling

Microsoft Research Amsterdam, Amsterdam, Netherlands

DP Technology, Beijing, China

Linfeng Zhang

AI for Science Institute, Beijing, China

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Connor W. Coley

Harvard Data Science Initiative, Cambridge, MA, USA

Marinka Zitnik

Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA

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All authors contributed to the design and writing of the paper, helped shape the research, provided critical feedback, and commented on the paper and its revisions. H.W., T.F., Y.D. and M.Z conceived the study and were responsible for overall direction and planning. W.G., K.H. and Z.L. contributed equally to this work (equal second authorship) and are listed alphabetically.

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Wang, H., Fu, T., Du, Y. et al. Scientific discovery in the age of artificial intelligence. Nature 620 , 47–60 (2023). https://doi.org/10.1038/s41586-023-06221-2

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Title: dalk: dynamic co-augmentation of llms and kg to answer alzheimer's disease questions with scientific literature.

Abstract: Recent advancements in large language models (LLMs) have achieved promising performances across various applications. Nonetheless, the ongoing challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. In this work, we introduce DALK, a.k.a. Dynamic Co-Augmentation of LLMs and KG, to address this limitation and demonstrate its ability on studying Alzheimer's Disease (AD), a specialized sub-field in biomedicine and a global health priority. With a synergized framework of LLM and KG mutually enhancing each other, we first leverage LLM to construct an evolving AD-specific knowledge graph (KG) sourced from AD-related scientific literature, and then we utilize a coarse-to-fine sampling method with a novel self-aware knowledge retrieval approach to select appropriate knowledge from the KG to augment LLM inference capabilities. The experimental results, conducted on our constructed AD question answering (ADQA) benchmark, underscore the efficacy of DALK. Additionally, we perform a series of detailed analyses that can offer valuable insights and guidelines for the emerging topic of mutually enhancing KG and LLM. We will release the code and data at this https URL .

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The tentacles of retracted science reach deep into social media: A simple button could change that

by University of Sydney

In 1998, a paper linking childhood vaccines with autism was published in the journal, The Lancet , only to be retracted in 2010 when the science was debunked.

Fourteen years since its retraction, the paper's original claim continues to flourish on social media, fueling misinformation and disinformation around vaccine safety and efficacy.

A University of Sydney team is hoping to help social media users identify posts featuring misinformation and disinformation arising from now-debunked science. They have developed and tested a new interface that helps users discover further information about potentially fraught claims on social media.

The study is published in the journal Proceedings of the ACM on Human-Computer Interaction .

They created and tested the efficacy of adding a "more information" button to social media posts . The button links to a drop down which allows users to see more details about claims or information in news posts, including information on whether that news is based on retracted science. The researchers say social media platforms could use an algorithm to link posts to details of retracted science.

Testing of the interface among a group of participants showed that when people understand the idea of retraction and can easily find when health news is based on a claim from retracted research, it can help reduce the impact and spread of misinformation as they are less likely to share it.

"Knowledge is power," said Professor Judy Kay from the School of Computer Science who led the research. "During the height of the COVID-19 pandemic, myths around the efficacy and safety of vaccines abounded. We want to help people to better understand when science has been debunked or challenged so they can make informed decisions about their health," she said.

"The ability to read and properly interpret often complex scientific papers is a very niche skill—not everybody has that literacy or is up to date on the latest science. Many people would have seen posts about now-debunked vaccine research and thought: 'It was published in a medical journal, so it must be true.' Sadly, that isn't the case for retracted publications."

"Social media platforms could do much better than they do now," said co-author and Ph.D. student Waheeb Yaqub. "During the height of the COVID-19 pandemic, myths around the efficacy and safety of vaccines spread like wildfire."

"Our approach shows that when people understand the idea of retraction and can find when health news is based on a retracted science article, it can reduce the impact and spread of misinformation," he said.

Tool boosts literacy of processes behind scientific research

The research was conducted with 44 participants who started with little or no understanding of scientific retraction. After completing a five-minute tutorial, they rated how various reasons for retraction make a paper's findings invalid.

The researchers then studied how participants used the "More Information" button. They found the new information altered the participants' beliefs on three health claims based on retracted papers shared on social media.

These claims were: whether masks are effective in limiting the spread of coronavirus; that the Mediterranean diet is effective in reducing heart disease ; and snacking while watching an action movie leads to overeating.

The first claim was based on two papers, one which had been retracted and one which hadn't. The other two claims were based on retracted papers. The researchers specifically chose papers of which participants would have differing knowledge.

"Participants confidently considered masks were effective. Most didn't know about the Mediterranean diet and so were unsure about whether it was true. Many people whose personal experience of snacking during films made them believe it was true."

The button influenced participants when they knew little about a topic to begin with. When the participants discovered the post was based on a retracted paper, they were less likely to like or share it.

On social media, both misinformation (the inadvertent spread of false information) and disinformation (false information deliberately spread with malicious intent), are rising.

Papers can be retracted when problems with methodology, results or experiments are found.

The researchers say it would be feasible for social media platforms to develop back-end software that links databases of retracted papers.

"If social media platforms want to maintain their quality and integrity, they should look to implement simple methods like ours," Professor Kay said.

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