High Signal Podcasts Evidence ledger
Method
Browse
← Back to evidence

Evidence receipt / uncertainty

Published · transcript-backed

Alessio Fanelli: uncertainty

1 Jun 2023 Latent Space Building the AI × UX Scenius — with Linus Lee of Notion AI

“I think when you first tweeted about this, I don't know if you already accepted the job, but you tweeted about this, and then the next one was like, this is a NotionAI subtweet.”

— Alessio Fanelli

Source trail

Everything needed to verify it.

Speaker
Alessio Fanelli
Attribution
Verified speaker
Claim type
uncertainty
Recorded
1 Jun 2023
Publisher
Latent Space

Transcript context

…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? I think when you first tweeted about this, I don't know if you already accepted the job, but you tweeted about this, and then the next one was like, this is a NotionAI subtweet. So I didn't realize that.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.

Search evidence