For me the convenience of “hey remember this fact” is outweighed by a desire not to get stuck in a search or context bubble.

It might be nice to have better UI to control which bits of history get added to the context of a chat, but then just use a coding harness instead of web UI

Maybe. But I don't just use it for coding. For instance one thing I use it for is helping me track and evolve my workouts over time. It helps that it can remember the weight and number of reps I did the previous time. I could make an app for this, but it works well to do it purely within chat, with memory, and that's convenient. This is just an example, I use it this way for a number of things. I'm not going to use a coding harness for this kind of trivial use case!

It should resonate here. The thesis is that good design principles are transferable between humans and agents. So focus on solving important problems and build well designed tools and documentation to get there, you don’t need special design considerations just because agents

I don’t know about the startup space, but in AI for science people are spending time building MCP wrappers around poorly designed APIs instead of redesigning the API or building an abstraction layer that humans can also use. That seems like a mis-allocation of effort

There’s no reason it has to be like that. We could change course one more time back to a stable and non-partisan science funding landscape and then stick with it if we choose to. Even some of the alarm in this article over the proposed funding cuts isn’t set in stone, the presidential budget request from last year aimed to cut NSF by a similar amount and Congress didn’t go for it

Only to be reversed by the next MAGA politician when the administration from the “other team” fails to immediately address the economic impact of decisions made in 2025.

We vote on vibes and popularity now, not qualifications.

On the contrary, I think the chess comparison is on point. We’re discussing observations that even the strongest models devolve into making invalid moves without scaffolding. For me that raises the question of whether these models are learning the rules and generalizing from them, or of they’re just pattern matching and flailing on this task. Maybe the reality is somewhere in between, but the benchmarks don’t seem to directly measure conceptual generalization, they measure task completion. They can disrupt a lot of people and industries by pattern matching and flailing without being AGI.

I’m sure these models know the rules and can explain them when prompted, but that doesn’t seem to be the way they actually complete this task. Will they get there? Maybe

the discussion isn’t really about whether language models can become strong chess players though, the point is they seem to struggle to consistently make valid moves. Most humans don’t need to read two books to pick that up, just a couple lines of basic instructions

That has not been my experience with new players, they regularly make invalid or incorrect moves even after detailed instructions especially in novel situations.

Maybe it depends on the person? My six year old isn’t great at strategy but they can pretty consistently make valid moves. Sometimes they ask for confirmation on a move which is also not a trait I see in language models (at least unprompted)

Your child never messed up en passant (or had trouble understanding it in a real game), castled through or into a check, didn't see a discovered check after moving a piece, never got confused by how stalemate works?

For numerical code I like einops.reduce more than numpy/pytorch sum reductions because you can reduce over named dimensions. It’s much more readable than having to reason through axis indexing again every time you come back to the code

That’s the prevailing narrative, but I think this controversy calls it into question to some extent. If the OpenAI result wouldn’t have been possible without experts seeding the training data with feedback on promising solution routes, there’s less reason to believe this, IMO. More information and transparency is needed

That's the pickle isn't it? AI solved it with human help. But it's standing on their shoulders. Neither AI nor the humans got it alone.

Humanity is better having solved this issue. But which humans were credited and benefited is the issue.

I agree (and so does Buckmaster based on his written statement) that we are better having solved this.

But I disagree that which humans were credited is the heart of the issue in this particular controversy. The question is what do you need to bring to the table for a result like this. A pre-release frontier model trained on the open literature and $15 million of inference? Or all that plus a year of the experts finding the path to the solution for the model to run with?

I think it makes a huge difference in terms of what we think the future of mathematical research will be like, and whether we should still encourage students to go into this field, which was the original topic of this thread

I think you’re missing an important distinction. “Major damage” to the talent pipeline because models become capable of original end-to-end mathematics is what the community has been discussing. But if the models rely on sniping nearly complete work then this damage is antisocial without a lot of upside, it would be destroying a talent pipeline that would still necessary for continued progress.

Which is it? I don’t think OpenAI is being transparent enough for us to really understand whether these results would have been possible without relying on unpublished information from the solution strategies of the experts

The session data could be cryptographically signed. Probably easier in an open harness?

Without seeing the full correspondence it’s hard to evaluate for sure, but parent linked to a tweet from the OpenAI employee at the center of the controversy, that’s a primary source you can read and evaluate yourself

Personally I don’t find the tweet a satisfactory explanation of their behavior, it seems like a lot of deflection without directly responding to the specific claims of front-running, and the screen cap of the correspondence doesn’t include all the relevant context. If there was really no bad behavior, why not post the whole thing?

> If there was really no bad behavior, why not post the whole thing?

what is whole thing?..

The screen cap of their full correspondence in the Twitter thread, which was the subject of the preceding sentence. The Twitter post has an obviously incomplete fragment of the conversation that doesn’t resolve what the author presents it as resolving, which is the dispute over how the discussion of authorship of Alpöge actually went down

I read Tristan's allegation from https://cims.nyu.edu/~tristanb/statement.pdf, and he didn't make it clear if threats to him were made over email, texts, calls or during personal meetings, so it is not clear if the whole thing was in writing.

He mentioned that they had meetings:

I asked to speak the following week. On Friday, September 4th, I was asked whether I could meet that day; I again said the following week. At 12:45 on Sunday, September 6th, I was asked whether I could meet “at any point today.” Sebastien Bubeck joined. The three of us spoke twice that afternoon. Levent was not on the calls.