AI: A Modern Approach?

In a fit of frustration, I wrote a troll post about AI: A Modern Approach.

https://twitter.com/srush_nlp/status/1446469104894873613

It led to a very interesting set of reactions. Here is a brief bibliography of the discussions that ensued:

Camp 0 -> AI is exactly what is in R&N

• Lots of people feel this way!

Camp 1 -> This confusion is really about the sociology of the field circa 1992

• Scruffy vs Neat : https://en.wikipedia.org/wiki/Neats_and_scruffies, https://monoskop.org/images/1/1e/McCorduck_Pamela_Machines_Who_Think_2nd_ed.pdf
• Symbolic vs Connectionist (Minsky): https://web.media.mit.edu/~minsky/papers/SymbolicVs.Connectionist.html

Camp 2 -> AI is inherently about Agency

• Rationality of Herbert Simon: https://www.scielo.br/j/rep/a/CWfwPPVWKvLrndfxR9vYFHL/?lang=en

Camp 3 -> AI is inherently about sequential decision making (Read a formal RL text)

• Algorithms for Decision Making: https://algorithmsbook.com/files/dm.pdf (this book looks amazing)
• Algorithms for Reinforcement Learning: https://sites.ualberta.ca/~szepesva/rlbook.html

Camp 4 -> High-level is good, but R&N is not

• ML from Tom Mitchell's text: http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html

Camp 5 -> ML is good, but prob ML texts are too informal

• Foundations of ML: https://cs.nyu.edu/~mohri/mlbook/
• Elements of Statistical Learning: https://web.stanford.edu/~hastie/ElemStatLearn/
• Understanding Machine Learning: https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/

Camp 6 -> PRML is on the right track, but doesn't have enough new stuff

• Prob ML - https://probml.github.io/pml-book/book1.html

Camp 7 -> Philosophy is critical for AI, but R&N is too scattered

• Cambridge Handbook of AI: http://www.cambridge.org/9780521871426

Camp X -> Arguments about Rigor are really important and we should have more of them

• Ali Rahimi's Test of Time Talk: https://www.youtube.com/watch?v=Qi1Yry33TQE