Lex Fridman
3 HN recommenders · recommended in 8 months, 2022–2025
3:28:48 · 4M views · Oct 29, 2022
Lex Fridman's long conversation with Andrej Karpathy on Tesla's self-driving approach, Optimus, neural networks, transformers, AGI and more. It also touches on his OpenAI work and working with Elon Musk.
Cited for Karpathy's first-hand explanations of camera-only self-driving, transformers and ML trends.
11 comments from Hacker News
Anyone seriously interested in this question should listen to the Lex Friedman interview of Andrej Karpathy: this video
My understanding is the transformer layer in the LLM is basically doing something akin to message passing, it’s like a mini computer. In predicting the next word, it has to understand a lot about a lot of different kinds of topics My understanding is kinda fuzzy because I haven’t coded it up myself, but this was the takeaway I got from this explanation (starts at 36:21) this video
Andrej Karpathy was recently on Lex Fridman's podcast and covered this to some extent. He has the same perspective on this topic and expanded on it quite a bit. Great listen overall IMHO - this video I like his idea of finding 0-days in physics. :)
As pointed out in [1], it seems machine learning takes the same path as physics already did. In the mid-20th century there was a "break" in physics, before individuals were making ground breaking discoveries in their private/personal labs (think Newton, Maxwell, Curie, Roentgen, Planck, Einstein, and many others) later huge collaborations (LHC/CERN, Icecube, EHT, et al.) are required, since the machinery, simulations, models are so complex, that groups of people are needed to create, comprehend and use them. 1. this video Lex Fridman podcast with Andrej Karpathy P.S. To counteract that (unintentionally actually, likely because of a simple optimization of instruments' duty cycle) in astronomy people come up with a concept of "observatory" (Like Hubble, JWST) instead of "experiment" (like LHC, HESS telescopes) where outside people can submit their proposals, and if selected get observational time. Along with raw data authors of the proposals get required expertise from the collaboration to process and analyze that data.
The part that I always found difficult to square was not that Elon Musk towed this “no need for LiDAR” line so hard, but that Andrej Karpathy, who I generally consider a very reliable voice in this space, was also in strong agreement that cameras were all that was needed. Does anyone know if he still believes cameras is all you need? Edit: Here is a link to Karpathy discussing the trade-offs: this video