Demis Hassabis: evaluation

Transcript context

…DeepMind has published all kinds of interesting stuff in speeding up science in different areas. If you think AGI is going to happen in the next 10 to 20 years, why not just wait for the AGI to do it for you? Why build these domain-specific solutions? I think we don’t know how long AGI is going to be. We always used to say, back even when we started DeepMind, that we don’t have to wait for AGI in order to bring incredible benefits to the world. My personal passion especially has been AI for science and health. You can see that with things like AlphaFold and all of our various Nature papers on different domains and material science work and so on. I think there’s lots of exciting directions and also impact in the world through products too. I think it’s very exciting and a huge unique opportunity we have as part of Google. They’ve got dozens of billion-user products that we can immediately ship our advances into and then billions of people can improve, enrich, and enhance their daily lives. I think it’s a fantastic opportunity for impact on all those fronts. I think the other reason from the point of view of AGI specifically is that it battle tests your ideas. You don’t want to be in a research bunker where you theoretically are pushing things forward, but then actually your internal metrics start deviating from real-world things that people would care about, or real-world impact. So you get a lot of direct feedback from these real-world applications that then tells you whether your systems really are scaling or if we need to be more data efficient or sample efficient. Because most real-world challenges require that. So it kind of keeps you honest and pushes you to keep nudging and steering your research directions to make sure they’re on the right path. So I think it’s fantastic. Of course, the world benefits from that. Society benefits from that on the way, maybe many years before AGI arrives. The development of Gemini is super interesting because it comes right at the heels of merging these different organizations, Brain and DeepMind. I’m curious, what have been the challenges there? What have been the synergies? It’s been successful in the sense that you have the best model in the world now. What’s that been like?…

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