Evidence receipt / evaluation
Published · transcript-backedShawn Wang: evaluation
23 Jul 2024 Latent Space Llama 2, 3 & 4: Synthetic Data, RLHF, Agents on the path to Open Source AGI
“We're basically saying that, I think that one of the lessons from AlphaGo is that people thought that human interest in Go would be diminished because computers are better than humans.”
Source trail
Everything needed to verify it.
- Speaker
- Shawn Wang
- Attribution
- Verified speaker
- Claim type
- evaluation
- Recorded
- 23 Jul 2024
- Publisher
- Latent Space
Transcript context
…No. What we know is it's not plateauing yet. It's not plateauing yet, yeah. So just infinite amounts more, well, you know, scale AI and all the annotation providers are very happy to hear that. So we mentioned at the start of the conversation about the AlphaGo moment, and I feel like this is very interesting to reflect on, right? We're basically saying that, I think that one of the lessons from AlphaGo is that people thought that human interest in Go would be diminished because computers are better than humans. But then we have this sort of centaur model where humans and computers are actually doing better than either humans and computers would be alone. And I think we're seeing that with this, what are you talking about, this RLHF improvement, right? That we're kind of building human preference into the model and the blending of the human preference and the model capability is actually doing better than we could on our own. I just think it's pretty fascinating. It is fascinating.…
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