Why we should view LLMs as powerful Cognitive Orthotics rather than alternatives for human intelligence #SundayHarangue LLMs are amazing giant external non-veridical memories that can serve as powerful cognitive orthotics for us, if rightly used (c.f. x.com/rao2z/status/170346612…). The trick, IMHO, is to exploit them without deluding ourselves in the process. The delusion comes chiefly from our incessant need to confuse them for human intelligence, merrily applying anthropomorphic concepts such as thinking , thoughts, reasoning and self-critiquing to LLMs. (c.f. x.com/rao2z/status/170832978…; x.com/rao2z/status/169710160…; x.com/rao2z/status/164890979…) This anthropomorphization is quite futile--and, as shown in the case of some of the current Ersatz Natural Science AI literature--even counterproductive and misleading. Sure we didn't quite foresee how impressive the approximate omniscience of these n-gram models on steroids would be, but that doesn't have to make us assume they do everything humans do. Unless human-level #AI is your singular goal, you don't necessarily need to think auto-regressive LLMs suck (as @ylecun puts it colorfully). LLMs can be very effective complementary cognitive orthotics without subsuming human intelligence. LLMs do some things way way way better than humans do  (the litmus test--from my perspective--being converting anything to iambic pentameter in seconds 😋x.com/rao2z/status/171863933…), and do other things (planning, reasoning, self critiquing, mental modeling) much worse (c.f. ; x.com/rao2z/status/170161286…; x.com/rao2z/status/171580081…; x.com/rao2z/status/164346320…) If we can manage to tone down the "LLMs are Zero-shot <XXX>" studies rife with confirmation biases that conflate approximate retrieval for other capabilities associated with human intelligence (c.f. x.com/rao2z/status/155308270…; x.com/rao2z/status/170785801…), we can: 1. Focus on the right way of leveraging the strengths of LLMs. This can certainly be done in LLM-modulo architectures, with either humans or other specialized sound reasoners in the loop). 2. To the extent human-level intelligence is still your holy grail, keep open research avenues that don't just involve scaling up autoregressive architectures. (It is in this sense that I sympathize with @ylecun's AR-LLMs suck comment--they do, currently, suck the oxygen out of the research room 😅) tldr; you can get much farther with LLMs & #AI if you think of them as cognitive orthotics and can stop anthropomorphizing them.. (c.f. x.com/rao2z/status/167866072…; x.com/deliprao/status/168681…)

Do you (really) know PyTorch 🔥? Try out my Tensor Puzzles 🧩. 16 mini-puzzles for those ready to take off the stackoverflow training wheels. github.com/srush/Tensor-Puzz…

48 male US Vice Presidents til now. Today....Vice President Kamala Harris.

Probably one of the best free resources I've found online! An introduction to linear algebra for machine learning with Python 🐍. Bookmark this link in your browser and use it as a reference whenever you need it. pabloinsente.github.io/intro… via @CodeBug88

"The time is ALWAYS right to do what is right." Dr #MartinLutherKingJr

A machine learning algorithm walks into a bar and the bartender asks, "what'll you have?" The machine learning algorithm says, "I don't know, what is everyone else having?"

How to become a SUCCESSFUL academic: a guide 1/n

Happy 15th Birthday jQuery! On January 14, 2006, the software engineer John Resig announced the creation of the jQuery JavaScript library at BarCamp NYC. webdesignmuseum.org/web-desi… #jQuery #InternetHistory

Do SotA natural language understanding models care about word order? Nope 🙃, 75% to 90% of the time, for BERT-based models, on many GLUE tasks (where they outperformed humans). "marijuana cause cancer" == "cancer cause marijuana" Ouch... arxiv.org/abs/2012.15180 1/4