The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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
Arvind Narayanan
So for instance, if in a particular language, there's too little data, you can try to augment that, or you can try to have a model, you know, solve a bunch of mathematical equations, throw that into the training data. And so for the next training run, that's going to be part of the pre training. And so the model will get better at doing that.
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