Jeremy Howard, every mention
8 scenes · ← back to Jeremy Howard
tap a year for its mentions
every year anyone Shawn Wang 3Alessio Fanelli 2Kyle Corbitt 1
Verbatim, from the transcripts: the passages where Jeremy Howard comes up
Why RL Won — Kyle Corbitt, OpenPipe (acq. CoreWeave)
- ▶ 20:32 Kyle Corbitt I know, like, Jeremy Howard's been beating this drum for, for, for years, and I honestly agree with him.
Information Theory for Language Models: Jack Morris
- ▶ 10:36 Shawn Wang If you have some kind of thing, Jeremy Howard will basically help you out and they, they have some distributed training.
Why Every Agent needs Open Source Cloud Sandboxes
- ▶ 50:22 Alessio Fanelli Obviously we have Jeremy Howard, who's been working on llm.txt to kind of have a separate interface for that.
Agent Engineering with Pydantic + Graphs — with Samuel Colvin, CEO of Pydantic Logfire
- ▶ 57:15 Shawn Wang I think similarly to what Jeremy Howard does with his stuff, it seems a little bit too magical still.
2024 Year in Review: The Big Scaling Debate, the Four Wars of AI, Top Themes and the Rise of Agents
- ▶ 0:44 Alessio Fanelli You know, we had like Tridao, we had Jeremy Howard, we had more folks like that.
The new Claude 3.5 Sonnet, Computer Use, and Building SOTA Agents — with Erik Schluntz, Anthropic
- ▶ 40:58 Shawn Wang I think that there's a lot of like, there's helper library structure, you know, I don't know if there, if there's anyone else that is specializing for Anthropic, maybe Jeremy Howard's and Simon Willis and stuff.
Breaking down the OG GPT Paper by Alec Radford
- ▶ 8:59 unnamed speaker The second approach is like to use a specific recipe or schema to do transfer learning, and a very popular example of this is ULM Fit by Jeremy Howard and Sebastian Ruder, and we're going to cover this in the next two slides, I think.
- ▶ 19:29 unnamed speaker And the authors mentioned the paper that the closest line of work to their work is actually what we discovered, uh, what we covered so far, the Olimfed by Howard and Ruder, and also another, uh, work by Dai et al.