Jeffrey Ladish
speaker
1,006 appearances
1 recordings
1 series
first heard Apr 2025
last heard Apr 2025
Jeffrey Ladish’s voice in public audio — every appearance, attributed to the second.
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And I'm like, well, what happens when we scale up this approach?
And I I think this is where it makes me think that we might be pretty close to AI systems that are not.
not just chatbots, but can actually go out in the world and do do real things and then learn from doing that.
Yeah, so that's a that's a great question.
I think this is cur this is currently something that's not totally solved.
But I imagine what the companies will do is a combination of having sort of humans sort of break down tasks into sub steps and then sort of sort of grade them.
But I also imagine that they'll be getting the AI systems themselves to do this, to say, you know, look at all this data, break down these these these tasks into sub steps and then
sort of assign assign credit, assign reward on the basis of, okay, did you complete this task?
Did you complete this task?
Did you sort of do well combining these tasks?
And it might be a little, it might be difficult, but it's the kind of thing where I'm like, it doesn't seem like a fundamental difficulty.
It seems like something that you have to throw more more data, more compute, more engineering at.
And it seems like you'll be able to solve.
Yeah, that's a great question.
And so I I do think it it relates to the thing that you just said, which is
Currently, AI systems are extremely good, especially after this 01-03 paradigm, at these short time horizon tasks.
They've sort of learned from by trial and error on these tasks, but they're tasks that you know don't involve that many steps.
Um and it is harder to sort of train them to do longer time horizon tasks.
A great example, sort of some great data on this is to look at meters sort of AI RD report, where they are basically
Be like, we want to test how good AI systems are at sort of these long time horizon tasks.
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