Jeremy Howard: evaluation

Transcript context

…I know. And there's also this notion that there are so many different ways to abstract and represent something. Know, the world is a very complex place. And maybe the way we've been abstracting and representing software is mostly a reflection of our own cognitive limitations, right? And even in the sciences and in physics, you tend to have a lot of quite reductive methods of modeling the world. And then you've got complexity science, is just embracing the constructive, dissipative, gnarly nature of things. And I think a lot of software today, we don't understand. Right? So for example, there are many globally distributed software applications that use the actor pattern. And this is just this ins it's basically like a complex system. Right? And the only way we can understand it is by doing simulations and tests because no 1 actually knows how all of these things fit together. So you could argue, I guess, as a bull case that maybe we already are doing this at the top of software engineering, and that is what we want to do eventually anyway. Yeah. I'd say probably not. You see companies like Instagram and WhatsApp dominate their sectors whilst having 10 staff, and beating companies like Google and Microsoft in the process. I would argue this way of building software in very large companies is actually failing. And I think we're seeing a lot of these very large companies becoming, you know, increasingly desperate. And, you know, for example, the quality of Microsoft Windows and Mac OS has very obviously deteriorated greatly in the last 5 to 10 years. You know, back when Dave Cutler was looking at every line of the NT kernel and making sure it was beautiful, it was a elegant and marvelous piece of software, you know. And this I don't think there's anybody in the world who's gonna say that Windows 11 is an elegant and marvelous piece of software. So I actually think we do need to find these smaller components that we do fully understand, and that we need to build them up. And here's the problem. AI is no good at that. So and and so I say that empirically. They're really bad at software engineering. And then I think that's possibly always gonna be true, because, you know, we're we're asking them to often move outside of their training data, you know, if we're trying to build something that literally hasn't been built before and do it in a better way than has been done before, we're saying, like, don't just copy what was in the training data. So and again, this is a confusing point for a lot of people, because they see AI being very good at coding. And then you think like, that's software engineering. You know, it's like, it must be good at software engineering. But it's they're different tasks. There's not a huge amount of overlap between them. And there's no current empirical data to suggest that LLMs are gaining any competency at software engineering. Every time you look at a piece of software engineering they've done, like the browser, for example, which Cursor created, or the C compiler, which Anthropic compared created. Like I've read the source code of those things quite a bit. Chris Latner is much more familiar with the compiler example than me. But they're they're very very obvious copies of things that already exist. So that's the challenge, you know, is if you want to build something that's not just a copy, then you can't outsource that to an LLM. There's no theoretical reason to believe that you'll ever be able to, And there's no empirical data to suggest that you'll ever be able to. Yes. I think the punch line of this conversation is, and I'm sure you would agree of this, that we need to have the combination of AI and humans working together. Right? Because Right. The humans provide the understanding and all of the stuff we were saying about knowledge. But we can still use AIs as a tool. But we to design operating models or ways of working that make that we say we don't want to diminish our competence and understanding. Right. So it's very it's a very fine…

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