Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning | Lex Fridman Podcast #258

the following is a conversation with john le his second time in the podcast he is the chief ai scientist at meta formerly facebook professor at nyu touring award winner one of the seminal figures in the history of machine learning and artificial intelligence and someone who is brilliant and opinionated in the best kind of way and

so is always fun to talk to this is a lex friedman podcast to support it please check out our sponsors in the description and now here's my conversation with yon lacoon you co-wrote the article self-supervised learning the dark matter of intelligence great title by the way with ishan mizrah so let me ask what is self-supervised

learning and why is it the dark matter of intelligence i'll start by the dark matter part uh there is obviously a kind of learning that humans and animals are are doing that we currently are not reproducing properly with machines with ai right so the most popular approaches to machine learning today are or paradigms i should say are supervised

running and reinforcement learning and they are extremely inefficient supervised learning requires many samples for learning anything and reinforcement learning requires a ridiculously large number of trials and errors to for you know a system to run anything and that's why we don't have self-driving cars that's a big leap from one to the other

okay so that to solve difficult problems you have to have a lot of uh human annotation for supervised learning to work and to solve those difficult problems with reinforcement learning you have to have some way to maybe simulate that problem such that you can do that large scale kind of learning that reinforcement