Nyla Worker: evaluation

Transcript context

…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. Yet what we're doing with LLMs is like choosing the next word. My thoughts here is that I see text as completely labeled data because it's what a human has put out. Like we, we've seen papers like textbooks is all you need, right? And that is because the textbooks are starting informationally dense and it's years of a human carefully crafting like word after word after word of what they are saying. And then the LLMs are learning from that. And yes, it's multitask learning because it's learning to do a lot of things because of that careful selection, but it's all labeled. I think it's a good approximation to human intelligence, but I'm not sure if it is going to be. And the best kind of human intelligence, right? Like whoever can write a quantum mechanics book and like the fact that AI can now predict what is the next word in a quantum mechanic textbook is like the best of human intelligence. But I am not a hundred percent sure. Like my definition of AGI is along the lines of it's self improving and it's much better than anything that humans could ever produce. And I'm not, I'm not sure. I'm particularly convinced on like that this is feasible today with what we have, but maybe I'm wrong. That's where I stand. We can leave that topic for coffee chats and go ahead to Convai or Convai. I always keep saying Convai. Um.…

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