http://www.digitalhumanities.org/dhq/vol/14/2/000454/000454.html
Category: Artificial Intelligence/Robots
"Mellon Foundation Grant Supports Development of a Plan for Using Artificial Intelligence to Plumb the National Archives"
"Peer Review of Scholarly Research Gets an AI Boost"
Special Issue of International Journal of Librarianship on AI, Machine Learning, and Data Science
Machine Learning + Libraries: A Report on the State of the Field
Library of Congress: Machine Learning + Libraries Summit Event Summary
"Clearview AI Says the First Amendment Lets It Scrape the Internet. Lawyers Disagree"
"Artificial Intelligence-Created Medicine to Be Used on Humans for First Time"
"WIPO Raises Questions about Artificial Intelligence and Copyright"
"A Sobering Message about the Future at AI’s Biggest Party"
DIY AI Tools: "Cloudy with a Chance of Neurons: The Tools That Make Neural Networks Work"
OCLC Research: Responsible Operations: Data Science, Machine Learning, and AI in Libraries
Boston Dynamics: "The World’s Most Freakishly Advanced Robot Dog Is Now For Sale"
Paywall Article: "Text Mining and Subject Analysis for Fiction; or, Using Machine Learning and Information Extraction to Assign Subject Headings to Dime Novels"
"Computational Intelligence to Aid Text File Format Identification"
Santhilata Kuppili Venkata and Alex Green have self-archived "Computational Intelligence to Aid Text File Format Identification."
Here's an excerpt:
One of the challenges faced in digital preservation is to identify the file types when the files can be opened with simple text editors and their extensions are unknown. The problem gets complicated when the file passes through the test of human readability, but would not make sense how to put to use! The Text File Format Identification (TFFI) project was initiated at The National Archives to identify file types from plain text file contents with the help of computing intelligence models. A methodology that takes help of AI and machine learning to automate the process was successfully tested and implemented on the test data. The prototype developed as a proof of concept has achieved up to 98.58% of accuracy in detecting five file formats.
Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap
Special Issue: Ethics of Artificial Intelligence
ARL has released a special issue of Research Library Issues on the "ethics of artificial intelligence."
Here's an excerpt from the announcement:
To frame this discussion, we invited three individuals to share their expertise and recommendations in this issue of RLI. In the first article, Sylvester Johnson, the founding director of the Center for Humanities and the assistant vice provost for the humanities at Virginia Tech, focuses on the role of ethics in innovation. AI, like other influential technologies, can be a force for innovation, and is known to have harmful as well as helpful implications, which Johnson examines.
Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap
"OpenAI Said Its Code Was Risky. Two Grads Re-Created It Anyway"
"AI Trained on Old Scientific Papers Makes Discoveries Humans Missed"
"Artificial Intelligence—The Revolution Hasn’t Happened Yet"
Michael I. Jordan has published "Artificial Intelligence—The Revolution Hasn't Happened Yet" in Harvard Data Science Review.
Here's an excerpt:
We now come to a critical issue: is working on classical human-imitative AI the best or only way to focus on these larger challenges? Some of the most heralded recent success stories of ML have in fact been in areas associated with human-imitative AI—areas such as computer vision, speech recognition, game-playing, and robotics. Perhaps we should simply await further progress in domains such as these. There are two points to make here. First, although one would not know it from reading the newspapers, success in human-imitative AI has in fact been limited; we are very far from realizing human-imitative AI aspirations. The thrill (and fear) of making even limited progress on human-imitative AI gives rise to levels of over-exuberance and media attention that is not present in other areas of engineering.
Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap
Machine-Learning Algorithm: "Microsoft Open Sources Algorithm That Gives Bing Some of Its Smarts"
"Drowning in Research Reading? AI Could Help"
Paywall Article: "What If Artificial Intelligence Wrote This: Artificial Intelligence and Copyright Law"
"Using AI to Solve Business Problems in Scholarly Publishing"
Michael Upshall has published "Using AI to Solve Business Problems in Scholarly Publishing" in Insights.
Here's an excerpt:
Artificial intelligence (AI) tools are widely used today in many areas, and are now being introduced into scholarly publishing. This article provides a brief overview of present-day AI and machine learning as used for text-based resources such as journal articles and book chapters, and provides an example of its application to identify suitable peer reviewers for manuscript submissions. It describes how one company, UNSILO, has created a tool for this purpose, and the underlying technology used to deliver it. The article also offers a glimpse into a future where AI will profoundly change the way that academic publishing will work.
Research Data Curation Bibliography, Version 9 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap