"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.

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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.

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"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.

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"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

"Intelligent Infrastructure, Ubiquitous Mobility, and Smart Libraries—Innovate for the Future"

Yi Shen has published "Intelligent Infrastructure, Ubiquitous Mobility, and Smart Libraries—Innovate for the Future" in Data Science Journal.

Here's an excerpt:

This paper presents an empirical research on the strategic development of a large-scale transdisciplinary area, named Intelligent Infrastructure for Human-Centered Communities or IIHCC, in the institutional context of Virginia Tech. . . . . Within such developments, this study discusses the developing scenarios of "smart" libraries as innovative testbeds for data exploration, community knowledge base, and intelligent information interface. It further projects an intelligent, learning, and adaptive library system, featuring exemplary data science platform and dynamic data management mechanism, smart design and innovation space, as well as collective intelligence and creative partnership. During this extraordinary time of horizon change, this timely work informs academic library transformation and its architectural innovation in the age of "smartness."

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

Artificial Intelligence, Machine Learning and EU Copyright Law: Who Owns AI?

The UK Copyright and Creative Economy Centre, has released Artificial Intelligence, Machine Learning and EU Copyright Law: Who Owns AI?.

Here's an excerpt:

The question thus becomes the following: is the act of training a model for ML [Machine Learning] purposes a copyright relevant activity? . . . . In more precise terms, the research question of this short contribution will focus on the act of training a model for ML/NLP [Machine Learning/Natural Language Processing] purposes and attempts to answer the question of whether this act infringes copyright and in particular the right of reproduction. In addition to this, the contribution also intends to explore whether there are other rights that may be infringed, in particular the right of adaptation, and thus determine whether a ML trained model can be considered a creative adaptation of the original corpora.

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Artificial Intelligence and the Future of Humans

The Pew Research Center has released Artificial Intelligence and the Future of Humans.

Here's an excerpt:

The experts predicted networked artificial intelligence will amplify human effectiveness but also threaten human autonomy, agency and capabilities. They spoke of the wide-ranging possibilities; that computers might match or even exceed human intelligence and capabilities on tasks such as complex decision-making, reasoning and learning, sophisticated analytics and pattern recognition, visual acuity, speech recognition and language translation.

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