Shawn Wang: evaluation

Transcript context

…I mean, I've written some libraries before, but a lot of my projects tend to be like, the way that I approach it is less about building something that other people are going to use en masse. And make yourself happy. Yeah, more about like, here's the thing that I built, if you want to, and often I learn something in the process of building that thing. So like with Monocle, I wrote a custom sort of full text search index. And I thought a lot of the parts of what I built was interesting. And so I just wanted other people to be able to look at it and see how it works and understand it. But the goal isn't necessarily for you to be able to replicate it and run it on your own. Well, we can kind of dive into your other AIUX thoughts. As you've been diving in, you tend to share a lot on Twitter. And I just kind of took out some of your greatest hits. This is relevant to the demo that you picked out, Alessio. And what we're talking about, which is, most knowledge work is not a text generation task. That's funny, because a lot of what Notion AI is, is text generation right now. Maybe you want to elaborate a little bit. Yeah. I think the first time you look at something like GPT, the shape of the thing you see is like, oh, it's a thing that takes some input text and generates some output text. And so the easiest thing to build on top of that is a content generation tool. But I think there's a couple of other categories of things that you could build that are sort of progressively more useful and more interesting. And so besides content generation, which requires the minimum amount of wrapping around ChatGPT, the second tier up from that is things around knowledge, I think. So if you have, I mean, this is the hot thing with all these vector databases things going around. But if you have a lot of existing context around some knowledge about your company or about a field or all of the internet, you can use a language model as a way to search and understand things in it and combine and synthesize them. And that synthesis, I think, is useful. And at that point, I think the value that that unlocks, I think, is much greater than the value of content generation. Because most knowledge work, the artifact that you produce isn't actually about writing more words. Most knowledge work, the goal is to understand something, synthesize new things, or propose actions or other kinds of knowledge-to-knowledge tasks. And then the third category, I think, is automation. Which I think is sort of the thing that people are looking at most actively today, at least from my vantage point in the ecosystem. Things like the React prompting technique, and just in general, letting models propose actions or write code to accomplish tasks. That's also moving far beyond generating text to doing something more interesting. So much of the value of what humans sit down and do at work isn't actually in the words that they write. It's all the thinking that goes on before you write those words. So how can you get language models to contribute to those parts of work?…

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