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Artificial Intelligence (AI) has been applied to an increasing number of creative tasks from the composition of music, to painting and more recently the creation of academic texts. Reflecting on this development Harry Collins, considers how we might understand AI in the context of academic writing and warns that we should not confuse the work of algorithms with tacit complex socially constructed forms of knowledge.

Apparently there are now academic books generated by artificial intelligence algorithms.  An example just published by Springer Nature, and written by ‘Beta Writer’, is called Lithium-Ion Batteries: A Machine-Generated Summary of Current Research.  I don’t know anything much about Lithium-Ion batteries, nor about how these algorithms work, but I do know something about scientific knowledge and the way it is generated. I have also written three books (without the aid of an algorithm) on artificial intelligence that draw on this knowledge, most recently: Artifictional Intelligence, Against humanity’s surrender to computers.

Full paper here

Google offers a new search engine to find datasets on specific topics.  Here is the link:

https://toolbox.google.com/datasetsearch

Times Higher Education produced in the 3rd of April a new world ranking for 450 universities based on the United Nations Sustainable Development Goals like (gender equality, sustainable cities and communities, real-world problem solving research, etc.).

In 1942, sociologist Robert Merton articulated an ethos of science in “A Note on Science and Technology in a Democratic Order.” He argued that, although no formal scientific code exists, the values and norms of modern science can nevertheless be inferred from scientists’ common practices and widely held attitudes. Merton discussed four idealized norms: Universalism, Communality, Disinterestedness, and Organized Skepticism. In this video, we explore what these norms are and what they mean for the scientific community. « A Note on Science and Technology » can be found at: http://www.collier.sts.vt.edu/5424/pd…

Merton, Robert K. 1973. The Sociology of Science: Theoretical and Empirical Investigations. University of Chicago Press.

Source: Berkeley Initiative for Transparency in the Social Sciences (BITSS)

The future of automated scientific writing is upon us—and that’s a good thing.

In 2014, a researcher in France revealed a disturbing fact about the published scientific literature: At least 120 computer-generated manuscripts had made their way into academic conference proceedings, according to his analysis. Those robot-written papers, containing little more than strung-together buzzwords, had been created with a piece of software known as SCIgen, originally written on a lark by a trio of MIT graduate students in 2005. But in the years since, it seemed scientists had repurposed SCIgen to puff up their resumes and boost their professional status. This was understood to be a major scandal.

For Klemen Zupancic, though, the scandal was a source of inspiration. “It got us thinking,” the 32-year-old molecular biologist and tech entrepreneur told me this week from his office in Slovenia. Zupancic is head of sciNote, a tech startup that builds tools for helping scientists to switch from using pen-and-paper laboratory notebooks to more efficient online apps. (The company claims to have about 20,000 users, of which almost half are in the U.S.) When he read about the infiltration of academic journals by robo-generated text, he realized that the same approach might be used for honest ends. If software can publish scientific gobbledygook, then maybe it can write a valid scientific paper, too. So his company set out to create a program that would do just that.

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