20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton
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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
51 min
3 speakers
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we're not going to have too many more cycles, possibly zero more cycles of a model that's almost an order of magnitude bigger in terms of the number of parameters than what came before and thereby more powerful. And I think a reason for that is data becoming a bottleneck. These models are already trained on essentially all of the data that companies can get their hands on. So while data is becoming a bottleneck, I think more compute still helps, but maybe not as much as it used to.
This is 20VC with me, Harry Stebbings, and we're sitting down today with one of my favorite writers in AI. He's been a big proponent in the belief that despite what many people think, increasing the amount of compute from this point will be unlikely to increase model performance significantly moving forward. I'm thrilled to welcome Arvind Narayanan, Professor of Computer Science at Princeton and the Director of the Center for Information Technology Policy. This is an incredible discussion that goes very deep on the bottlenecks in AI today, and you can watch it on YouTube by searching for 20VC.
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