Evidence receipt / belief
Published · transcript-backedMark Zuckerberg: belief
18 Apr 2024 Dwarkesh Podcast Mark Zuckerberg — Llama 3, $10B models, Caesar Augustus, & 1 GW datacenters
“I think that at some point AI is probably going to surpass people at most of those things, depending on how powerful the models are.”
Source trail
Everything needed to verify it.
- Speaker
- Mark Zuckerberg
- Attribution
- Verified speaker
- Claim type
- belief
- Recorded
- 18 Apr 2024
- Publisher
- Dwarkesh Podcast
Transcript context
…Is the programmer in this building 10x more productive after Llama-10? I would hope more. I don't believe that there's a single threshold of intelligence for humanity because people have different skills. I think that at some point AI is probably going to surpass people at most of those things, depending on how powerful the models are. But I think it's progressive and I don't think AGI is one thing. You're basically adding different capabilities. Multimodality is a key one that we're focused on now, initially with photos and images and text but eventually with videos. Because we're so focused on the metaverse, 3D type stuff is important too. One modality that I'm pretty focused on, that I haven't seen as many other people in the industry focus on, is emotional understanding. So much of the human brain is just dedicated to understanding people and understanding expressions and emotions. I think that's its own whole modality, right? You could say that maybe it's just video or image, but it's clearly a very specialized version of those two. So there are all these different capabilities that you want to train the models to focus on, in addition to getting a lot better at reasoning and memory, which is its own whole thing. I don't think in the future we're going to be primarily shoving things into a query context window to ask more complicated questions. There will be different stores of memory or different custom models that are more personalized to people. These are all just different capabilities. Obviously then there’s making them big and small. We care about both. If you're running something like Meta AI, that's pretty server-based. We also want it running on smart glasses and there's not a lot of space in smart glasses. So you want to have something that's very efficient for that. If you're doing $10Bs worth of inference or even eventually $100Bs, if you're using intelligence in an industrial scale what is the use case? Is it simulations? Is it the AIs that will be in the metaverse? What will we be using the data centers for?…
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