the Ramanujan machine

Nature of 4 Feb. 2021 offers a rather long (Nature-like) paper on creating Ramanujan-like expressions using an automated process. Associated with a cover in the first pages. The purpose of the AI is to generate conjectures of Ramanujan-like formulas linking famous constants like π or e and algebraic formulas like the novel polynomial continued fraction of 8/π²:

which currently remains unproven. The authors of the “machine” provide Python code that one can run to try uncover new conjectures, possibly named after the discoverer! The article is spending a large proportion of its contents to justify the appeal of generating such conjectures, with several unsuspected formulas later proven for real, but I remain unconvinced of the deeper appeal of the machine (as well as unhappy about the association of Ramanujan and machine, since S. Ramanujan had a mystical and unexplained relation to numbers, defeating Hardy’s logic,  “a mathematician of the highest quality, a man of altogether exceptional originality and power”). The difficulty is in separating worthwhile from anecdotal (true) conjectures, not to mention wrng conjectures. This is certainly of much deeper interest than separating chihuahua faces from blueberry muffins, but does it really “help to create mathematical knowledge”?

love thy command line [Bourne again]

“Prebuilt into macOS and Unix systems (…) the command line (also called the shell) is a powerful text-based interface in which users issue terse instructions to create, find, sort and manipulate files, all without using the mouse. There are actually several distinct (…) shell systems, among the most popular of which [sic?] is Bash, an acronym for the ‘Bourne again shell’ (a reference to the Bourne shell, which it replaced in 1989).”

An hilarious rediscovery of the joys of shell (line) commands in Nature! Which I use by default for most operations on my computer, albeit far from expertly (despite the use of a cheat tee, from time to time!). One of the arguments in the article, “The mouse doesn’t scale,” is definitely mine as well. Among other marketing lines, wrangling files with no software interference (check), handling huge files (very rarely), manipulating spreadsheets (I don’t), parallelising work on remote servers (check), automate via cron (not anymore)…. Unsurprisingly, most of our students are never using terminals of command lines.

missing bit?

Nature of 7 December 2020 has a Nature Index (a supplement made of a series of articles, more journalistic than scientific, with corporate backup, which “have no influence over the content”) on Artificial Intelligence, including the above graph representing “the top 200 collaborations among 146 institutions based between 2015 and 2019, sized according to each institution’s share in artificial intelligence”, with only the UK, Germany, Switzerland and Italy identified for Europe… Missing e.g. the output from France and from its major computer science institute, INRIA. Maybe because “the articles picked up by [their] database search concern specific applications of AI in the life sciences, physical sciences, chemistry, and Earth and environmental sciences”.  Or maybe because of the identification of INRIA as such.

“Access to massive data sets on which to train machine-learning systems is one advantage that both the US and China have. Europe, on the other hand, has stringent data laws, which protect people’s privacy, but limit its resources for training AI algorithms. So, it seems unlikely that Europe will produce very sophisticated AI as a consequence”

This comment is sort of contradictory for the attached articles calling for a more ethical AI. Like making AI more transparent and robust. While having unrestricted access to personal is helping with social engineering and control favoured by dictatures and corporate behemoths, a culture of data privacy may (and should) lead to develop new methodology to work with protected data (as in an Alan Turing Institute project) and to infuse more trust from the public. Working with less data does not mean less sophistication in handling it but on the opposite! Another clash of events appears in one of the six trailblazers portrayed in the special supplement being Timnit Gebru, “former co-lead of the Ethical AI Team at Google”, who parted way with Google at the time the issue was published. (See Andrew’s blog for Jessica Hullman discussion of her firing. And the MIT Technology Review for an analysis of the paper potentially at the source of it.)

For a resilient and recovery year

cosmos behind bars [Technológia börtönében]