Category: Artificial Intelligence/Robots

"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.

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"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."

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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.

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Search Results Ranking Using Machine-Learning Algorithms: "Best Match: New Relevance Search for PubMed"

Nicolas Fiorini et al. have published "Best Match: New Relevance Search for PubMed" in PLOS Biology.

Here's an excerpt:

PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature—about two articles are added every minute on average—finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively.

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