Demis Hassabis: evaluation

Transcript context

…How do you get to the point with these models where the synthetic data they’re outputting on the self-play they’re doing is not just more of what’s already in their data set, but something they haven’t seen before? To actually improve the abilities. I think there’s a whole science needed there. I think we’re still in the nascent stage of this, of data curation and data analysis and actually analyzing the holes that you have in your data distribution. This is important for things like fairness and bias and other stuff. To remove that from the system is to really make sure that your data set is representative of the distribution you’re trying to learn. There are many tricks there one can use, like overweighting or replaying certain parts of the data. Or if you identify some gap in your data set, you could imagine that’s where you put your synthetic generation capabilities to work on. Nowadays, people are paying attention to the RL stuff that DeepMind did many years before. What are the early research directions, or something that was done way back in the past, that you think will be a big deal but people just haven’t been paying attention to it? There was a time where people weren’t paying attention to scaling. What’s the thing now that is totally underrated?…

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