01 / uncertainty
I don't know exactly why, but because it's a state school, the focus is a lot on STEM topics.
“I don't know exactly why, but because it's a state school, the focus is a lot on STEM topics.”
- Speaker
- Linus Lee
- Publisher
- Latent Space
Public evidence record
Published podcast speaker
Claim ledger
18 transcript-backed records
01 / uncertainty
“I don't know exactly why, but because it's a state school, the focus is a lot on STEM topics.”
02 / belief
“Speaking of adding constraints to general systems, adding constraints, adding program verification, all of these things I think are super fascinating.”
03 / belief
“I think we can, I'm sure, talk about this more later, but there is a consistent kind of focus on taste that I think flows down from Ivan and the founders into the product.”
04 / belief
“I think anytime you have sort of an unbounded action space for, for models like agents, it's especially important to be able to answer those questions easily and to have some sense of security that in the same way that you want to know whether your like coworker or collaborator has access to a document or has modified a document, you want to know whether an AI has permissions to access something.”
05 / belief
“Anthropic recently announced their 100,000 token context model recently. And so I think in the longer term that's going to be taken care of anyway by the models becoming more accommodating of longer contexts.”
06 / uncertainty
“You can put all of those in a single Notion database. And the benefit of Notion is that all of them live in a single space where you can link to your wiki pages from your, I don't know, like onboarding docs.”
07 / belief
“I think in practice there are a couple of trends that make that an issue. So for things like generating summaries, a summary is only going to be so many tokens long.”
08 / belief
“In all of these things, speaking of adding constraints, we have a lot of suggested prompts that we've worked on and we've curated and we think work pretty well for things like summarization and writing drafts to blog posts and things.”
09 / prediction
“We do do quite a bit of using language models to evaluate language models. So our unit test descriptions are kind of funny because the test is literally just an input document and a query, and then we expect the model to say something.”
10 / evaluation
“You can take a page inside a database and pull it out and it'll just become a link to that page. And so this core abstraction of a block that can also be a page, that can also be a row in a database, like an Excel sheet, that fluidity and this like shared abstraction across all these different areas inside Notion, I think is what really makes Notion powerful.”
11 / prediction
“Another sort of more, more recent revelation that I had while working on this and autofill thing inside notion is the importance of zones of influence for AI agents, especially in collaborative settings. So having worked on lots of interfaces for independent work on my year off, one of the surprising lessons that I learned early on when I joined notion was that if you build a collaboration permeates everything, which is great for notion because collaborating with an AI, you reuse a lot of the same metaphors for collaborating with humans.”
12 / commitment
“One of the first things that I wrote in this exploration year was this piece called Notational Intelligence, where I talk about this idea that so much of, as a total sidebar, there's a whole other fascinating conversation that I would love to have at some point, maybe today, maybe later, about how to evolve a budding scene of research into a fully-fledged field.”
13 / commitment
“The motivation was that I think I was at a sort of a privileged and fortunate enough place where I felt like I had some money saved up that I had saved up explicitly to be able to take some time off and investigate my own kind of questions because I was already working on lots of side projects and I wanted to spend more time on it.”
14 / evaluation
“I think especially when people approach project building with a goal of learning, I think a common mistake is to be over-ambitious and sort of not scope things very tightly.”
15 / preference
“For me, that's Go, and my language, and a couple other libraries that I've written that I know all the way down to the bottom of the stack. And then I barely have to look anything up, because I've just debugged every possible issue that could come up.”
16 / prediction
“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.”
17 / commitment
“I think one of the early transitions that we made was that the initial prototype for Notion AI was built on instruction following, the sort of classic instruction following models, TextWG003, and so on.”
18 / prediction
“You can ask a question, then it'll look things up and give you an answer. But if something looks like a chat, and this is a lesson that's been learned over and over for anyone building chat interfaces since, like, 2014, 15, if you have anything that looks like a chat interface or a messaging app, people are going to put some, like, weird stuff in there that just don't look like the thing that you want the model to take in, because the expectation is, hey, I can use this like a messaging app, and people will send in, like, hi, hello, you know, weird questions, weird comments.”