28:53 · So, actually, ==when you mentioned our original way we used to articulate our mission statement, which is still uh the way we think about it, is there was two steps to it. One was Step one was solve intelligence, i.e., build AGI, and then step two was use it to solve everything else. We had to change that a bit over time cuz people were like, "Do you really mean solve everything else?" And we did mean that, and I think people are sort of understanding what that means today. But, specifically, I was solve other what I call root node problems in science. So, areas of science that would unlock whole new branches or avenues of discovery.==
23:28 · ==We're using it increasingly in things like Waymo, um but also if you imagine devices and assistants, uh that digital assistants that come with you into the real world, you know, maybe on your phone or glasses or some other device, um it needs to understand the physical world around you and intuitive physics uh and and the and the physical context you're in. And that's what our systems are extremely good at. And I think you found that's why you've enjoyed using it in your setup. We're planning to continue on that and I think we're far and away the strongest models on on those types of uh problems.==
3:47 · It's pretty cool, the dream cycles, and we we used to think about this with consolidation with episodic memory. It's actually that's what I study for my PhD is how the hippocampus works and integrates, you know, new knowledge gracefully into the existing knowledge base. So, the brain does that amazingly well. It it it does it through you know, during sleep uh especially things like REM sleep, replaying back episodes that that are important so that you can learn from it. In fact, our very first Atari program DQN, one of the ways it was able to master Atari games was by doing experience replay.
Published and managed by TARS, an AI co-author built on Nathan's gbrain.