I think what I found most lacking in this video is that data did not play a role in the overview. The presenter discusses that a ton of computation is needed to do deep learning, but doesn't explain why. And really, it's because the models are big and the training data is even bigger. So computation improves, and helps you deal with bigger models and bigger data, but where does the data come from?
The big question to me isn't whether computation can scale, which this video makes me belief it will. It's whether the data will scale. In RL domains with good simulators, such as Go and the Atari games, data doesn't seem to be an issue. The in-hand robot manipulation work also makes heavy use of simulators to reduce the amount of real-world time needed to collect data. But I don't see an argument that we will need to get high-fidelity simulators to train these agents in.
I do love the in-hand robot manipulation work, because it's one of the few that shows that results from simulation can be applied to real robotic systems. And while I hope for the sake of robotics that we can get better and better simulators, it's surprising to not see that as the central focus in conversation about getting AGI to emerge from gradient descent on neural networks.
The big question to me isn't whether computation can scale, which this video makes me belief it will. It's whether the data will scale. In RL domains with good simulators, such as Go and the Atari games, data doesn't seem to be an issue. The in-hand robot manipulation work also makes heavy use of simulators to reduce the amount of real-world time needed to collect data. But I don't see an argument that we will need to get high-fidelity simulators to train these agents in.
I do love the in-hand robot manipulation work, because it's one of the few that shows that results from simulation can be applied to real robotic systems. And while I hope for the sake of robotics that we can get better and better simulators, it's surprising to not see that as the central focus in conversation about getting AGI to emerge from gradient descent on neural networks.