Transfer Learning
topic on 5 shows · 7 statements across 6 episodes
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7 statements about Transfer Learning, every show
Masad: AI lacks cross-domain transfer learning for generalized reasoning
“Because there doesn't seem to be transfer learning across these domains that are, you know, significant, right? So if we get a lot better at code, We're not immediately getting better at, like, generalized reasoning. We need to go also, you know, get training …”
Andreessen: Humans themselves rarely achieve cross-domain transfer learning
“Transfer learning is the ability of the machine to, right, to be an expert in one domain and then generalize that into another domain. My answer to that is, like, have you met people? And how many people do you know are able to do transfer learning?”
Foody: AI reasoning from code and math will generalize via transfer learning
“Yeah, I generally believe in it but to a certain extent, like, you still need a reasonable amount of data for the new domain and to kickstart it but there's gonna be a lot of transfer learning.”
Karpathy: General robot platforms uniquely enable cross-task transfer learning in AI
“And then I would say also one last dimension of it is you benefit a ton from like the transfer learning between the different tasks. And in AI, you really want a single neural nut that is multitasking, doing lots of things that's very getting all the intellige…”
Howard: Transfer learning reduces deep learning compute and data needs
“There's this thing which nobody knows about, nobody talks about, called transfer learning, where you take somebody else's model where they already figured out, like, how to Detect edges, and gradients, and corners, and text, and whatever else, and then you can…”
Delangue: Transfer learning was the missing piece enabling deep learning in NLP
“The last kind of, like, missing piece to this puzzle for us is transfer learning with the reference paper on the subject that came out a bit more than two years ago called Attention is all you need. And as a kind of, like, high-level, simplistic way that's wha…”
Victoroff: Indico builds custom classification models from 10 to 20 examples
“The really cool thing about transfer learning is that we can actually, you know, if a user gives us 10 or 20 Any examples, then we've got a totally performing classification algorithm that's personalized to them. That doesn't, you know, kill the servers becaus…”