neato links

http://www.t3x.org/bits/prolog-6-slides.pdf – prolog flow-control howto.

http://www.t3x.org/bits/prolog6.html – lots of neato links. includes sources.

Where was this twenty years ago?

http://www.t3x.org/bits/index.html – lots of old-school scheme code.

a hecking book of old-school ai papers

https://www.amazon.ca/s?k=handbook+of+artificial+intelligence+volume+5&ref=nb_sb_noss – i have up to volume 4 on my shelf. lots of great intro material from the 70s.

spoiler warning: they made a big deal about these in computer class in the aughties
spoiler warning: P != NP (probably)

there’s a lot of good stuff up there, if you’re into lambda calculus.

now for the hobbyist esoterica:

http://www.hutter1.net/ai/uaibook.htm – universal artificial intelligence.

http://www.hutter1.net/publ/aixictwxcode.zip – an approximate implementation that kinda works, a little?

https://arxiv.org/abs/0909.0801 – associated paper.

https://wiki.lesswrong.com/wiki/AIXI – everyone panic!

https://www.lesswrong.com/posts/t47TeAbBYxYgqDGQT/let-s-reimplement-eurisko – they used to link the sources in here… or perhaps one of the aggregator sites that linked to it…

everyone panic! the series

http://alignment-newsletter.libsyn.com/ – ai safety podcast

behavioural cloning howto

https://arxiv.org/abs/1906.08237 – XLNet: Generalized Autoregressive Pretraining for Language Understanding. how to language.

https://arxiv.org/abs/1901.02860 – Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. library used.

https://arxiv.org/abs/1708.02182 – Regularizing and Optimizing LSTM Language Models. how to lstm from scratch, more ore less.

https://arxiv.org/abs/1611.01576 – Quasi-Recurrent Neural Networks. lstm hacking guide.

where can you find ai task environments?

https://en.wikibooks.org/wiki/Artificial_Intelligence/AI_Agents_and_their_Environments – apparently there’s a book on it.

http://aima.cs.berkeley.edu/ – i hear good things about this book.

https://webdocs.cs.ualberta.ca/~rgreiner/C-366/SLIDES/syllabus.html – intro material data dump.

https://webdocs.cs.ualberta.ca/~rgreiner/C-466/ – intro material data dump.

https://web.cs.dal.ca/~eem/4150/AI.html – and another

http://users.cs.dal.ca/~eem/2140/ – this was a really fun course number. pretty decent textbook, back in the day.

https://web.cs.dal.ca/~vlado/csci6509/ – more intro material

https://standardebooks.org/ – nicely formatted public-domain ebooks.