Howard: Training AI models from random weights is almost never justified
“If you're training for random weights, you better have a really good reason, you know, because it seems so unlikely to me that nobody has ever trained on data that has any similarity whatsoever to the general class of data you're working with, and that's the o…”
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…”
Howard: AI research went backwards for years pursuing zero-shot learning
“And so I actually feel like we kind of went backwards for years and not to be honest, I mean, I'm a bit sad about this now, but I kind of got so disappointed and dissuaded by like, It felt like these bigger lab, much bigger labs, you know, like fast.ai had onl…”
Howard: Tesla and OpenAI Scholars used Fast.ai courses for deep learning training
“Andre Capathy grabbed me when I saw him at NeurIpes a few years ago, and he's like, I have to tell you, thanks to the fast AI courses, when people come to Tesla, and they need to know more about deep learning, we always send them to your course. And the OpenAI…”
Howard: Fast.ai was never more than two people and is now solo
“That's just one of, like, three things we do, is the course, you know, and it's only ever been at most two people, either me and Rachel, or me and Sylvain. Nowadays it's just me.”
Jeremy Howard: AI research artifacts should be software and courses, not papers
“To me the main artifact shouldn't be papers, because papers are things read by a small exclusive group of people, you know, to me the main artifacts should be, like, something teaching you people, here's how to use this insight, and here's software you can use…”
Jeremy Howard: Fast.ai won Stanford's DawnBench in 10 days using progressive resizing
“We only found out about this 10 days before the competition finished but, you know, we basically got together an emergency bunch of our students, and Rachel and I, and sat for the next 10 days, and just tried to crunch through, and Try to use all of our best i…”
Howard: Rapid LLM race created massive technical debt and optimization opportunities
“There's a whole lot of technical debt everywhere, you know, nobody's really figured this stuff out because everybody's been so busy building what we know works as quickly as possible. So, yeah, I think there's a huge amount of opportunity to, you know, I think…”