Bryan Catanzaro, VP of Applied Deep Learning Research at NVIDIA, explains why semiconductor scaling no longer provides automatic economic benefits for chip manufacturing.
“The original statement of Moore's law was economic, right? It was about, we can afford to put twice as many transistors on the same chip in every, whatever, 24 months, whatever the time period is. And these days that is, Absolutely not the case. It hasn't been for probably five or 10 years, right?”
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More from Bryan Catanzaro
Opinion
Catanzaro: Chinese AI achievements are not driven by a copycat mentality
“I think it's absolutely false to say that you know the achievements of some other country are all being created by sort of, you know, copycat mentality. It's just not true.”
Catanzaro: China has been leading in open community-oriented AI development
“I think there's a chance for the rest of the world to catch up to China in the sense that you know, we can understand the benefits of working together as a community to build technologies for AI in a way that I think China has frankly been leading.”
Catanzaro: The technological singularity is a wrongheaded idea
“The singularity is, although it's an attractive idea, I think that it's a really a wrongheaded idea because it doesn't really take into account these other factors.”
Catanzaro: At AI compute limits, intelligence gains require higher efficiency
“If you accept as the truth that we're going to be running at the limit, then what that means is that the way to get more intelligence is to be more efficient. We can't get more intelligence by applying more force if we're already at the limit. We have to be mo…”
Catanzaro: Multi-token prediction lowers inference costs as model accuracy improves
“With multi-token prediction, the speed that you get is a function of the accuracy of your model. The more accurate your model is, the faster the inference is, the cheaper the inference is, the more accurate it is. That's not usually how it works, but in this c…”
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