Itamar Friedman: prediction

Transcript context

…Yeah. And when you say benchmark, you know, we did a one hour long episode, some benchmarks, there's like many of them. Are you building some unique evals to like your own problems? Like how are you doing that? And that's also work for your future model building, obviously, having good benchmarks. Yeah. . Yeah. That's very interesting. So first of all, with all the respect, I think like we're dealing with ML benchmark for hundreds of years now. I'm, I'm kidding. But like for tens of years, right? Benchmarking statistical creatures is something that, that we're doing for a long time. I think what's new here is the generative part. It's an open challenge to some extent. And therefore, like maybe we need to re rethink some of the way we benchmark. And one of the notions that I really believe in, I don't have a proof for that, is like create a benchmark in levels. Let's say you create a benchmark from level one to 10, and it's a property based benchmark. Let's say I have a WebGPT ask something from the internet and then it should fetch it for me. So challenge level one could be, I'm asking it and it brings me something. Level number two could be I'm asking it and it has a certain structure. Let's say for example, I want to test AutoGPT. Okay. And I'm asking it to summarize what's the best cocktail I could have for this season in San Francisco. So I would expect, like, for example, for that model to go. This is my I what I think to search the internet and do a certain thing. So level number three could be that I want to check that as part of this request. It uses a certain tools level five, you can add to that. I expect that it'll bring me back something like relevance and level nine it actually prints the cocktail for me I taste it and it's good. So, so I think like how I see it is like we need to have data sets similar to before and make sure that we not fine tuning the model the same way we test it. So we have one challenges that we fine tune over, right? And few challenges that we don't. And the new concept may is having those level which are property based, which is something that we know from software testing and less for ML. And this is where I think that these two concepts merge. Maybe Codium can do ML testing in the future as well.…

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