"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

"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

"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

"Springer Nature Publishes Its First Machine-Generated Book"

Springer Nature has released "Springer Nature Publishes Its First Machine-Generated Book."

Here's an excerpt:

Springer Nature published its first machine-generated book in chemistry. The book prototype provides an overview of the latest research in the rapidly growing field of lithium-ion batteries. . . .

In close collaboration between Springer Nature and researchers from Goethe University Frankfurt/Main, a state-of-the-art algorithm, the so-called Beta Writer, was developed to select, consume and process relevant publications in this field from Springer Nature’s content platform SpringerLink. Based on this peer-reviewed and published content, the Beta Writer uses a similarity-based clustering routine to arrange the source documents into coherent chapters and sections. It then creates succinct summaries of the articles. The extracted quotes are referenced by hyperlinks which allow readers to further explore the original source documents. Automatically created introductions, table of contents and references facilitate the orientation within the book.

Research Data Curation Bibliography, Version 9 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap