Recently I've been "tidying up" and finding new homes for code - trying to keep older software from falling to 'bitrot'.
And today I came across this "Ten Years Reproducibility Challenge" by the ReScience journal:
I have some code in #lisp from ~2000-10, and lisp is pretty stable - but there might be some minor issues with the version of Lisp. It would be nice to have those case studies working and available.
There's a project in #java, but too young.
Some interesting articles on the subject of practice and its relation to becoming an expert, in this month's Journal of Expertise: authors look at olympic athletes, racing-car drivers, and musicians.
Tried the Plots library with pyplots, but 'using' Plots takes 40 seconds, and a first histogram with 10^7 points takes 50 seconds to appear.
Tried Gadfly. After precompilation, 'using' Gadfly takes 20 seconds and plot(y=[1,2,3]) needs 90 seconds to appear.
Both libraries do better on the second plot, but there are alternatives which don't make you wait so long for a single graph.
Is this just the state of Julia now, or is there a quicker 'plot' package?
The European Conference on AI in 2020 will be in Spain, June 8-20.
Abstract submission: 15th November
Paper submission: 19th November
Can (will?) computers take over creative activities? Humans tend to get stuck with biased attitudes, can be incapable of looking beyond their typical solutions to problems, and generally are limited in the amount of information they can process. Computers can potentially break away from these problems and produce new solutions. Quite often, in quantifiable situations, these are better than those of human experts.
A readable summary (by a colleague):
The 2020 European Lisp Symposium will be held in Zurich, April 27th-28th.
Topics for papers include language features, education and applications/experience reports.
(Starting with a 'brag'!)
Our edited book "Scientific Discovery in the Social Sciences" is now available.
"This volume offers selected papers exploring issues arising from scientific discovery in the social sciences. It features a range of disciplines including behavioural sciences, computer science, finance, and statistics with an emphasis on philosophy."
Hello. I'm a UK-based lecturer in computer science. I mostly teach programming with some data mining and AI thrown in. Since my post-doc days, I've also worked in cognitive science, particularly on creating computational models of human learning.
I am here to experiment with an official 'work' microblog. I look forward to following the first two "encouraged uses of scholar.social", i.e. bragging about publications and sharing references! And also some of the third, networking.
Computer science academic interested in programming, learning algorithms and cognitive models.
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