Nyla Worker: commitment

Transcript context

…I think the problem here comes from like, I think we understand how to do this in a normal ML context, but when you're trying to build AGI, the real world is everything. There's nothing to optimize for because it's, it's everything. So how do you optimize for everything? I think the places where we're going to get AGI is where the AI can get complete feedback, but this is just my intuition behind it. So for example, in a coding environment that AI will have the ability to like rerun things and reevaluate if it's performing things well, and that will work, I still, I'm not sure how it would work with like something where you don't have. Feedback. So like in robotics, we first need to get like that really good, like grasping sensors or like really good vision sensors such that it can get some kind of feedback loop eventually started. But yeah, that goes more on like that reinforcement learning side where we've already seen superhuman performance, but it's still with LLMs. I think we're still approximating what we have available. It's a super interesting topic, but It really depends on like how you define it, and we will have to have a discussion on the definition and then how you measure it. Beyond the definition, what I'm trying to get across is the normal ML mindset is, oh, understand the problem, and then design the data set, design the architecture to fit the problem. Right? But with the foundation model paradigm, there is no problem to optimize for because you're really trying to just have a general purpose, everything model.…

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