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

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being transparent enough for us to really understand whether these results would have been possible without relying on unpublished information from the solution strategies of the experts

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?

Those problems can’t be formally verified with an automated theorem prover. We have a lot of physics based simulation tools, but they tend to focus on small subsets of the full design problem and they make limiting approximations because otherwise they’d be too computationally expensive, or we just don’t have the right data to parameterize them beyond describing qualitative behavior. Agents are helping accelerate research in these fields but I think it’s mostly a different class of problem that’s a lot harder to specify and verify

If so that seems really nice, one of the things I don’t like with coding agents is having to defensively commit changes to roll back if the agent goes off the rails. I’m not sure if that’s a problem with my workflow, but your tool seems great for exploratory stuff

How does it change the way you personally use pi? How git aware is it, do you have to commit at the end of a branching session, and does it handle manual edits?

> White House journalists are outraged that a threat credible enough to force Donald Trump to escape from Air Force One using an airport catering truck wasn’t relayed to them.

As an added complication, some of the DOE labs do have civil servant scientists, for example National Energy Technology Lab and National Renewable Energy Lab are like 50/50 civil servants and contractors. And most of the funding arm of DOE are career civil servants. LANL, Sandia, Livermore, Argonne are all staffed by contractors

That doesn’t necessarily mean there’s no need to be concerned with potential impact of policy and priority changes from the administration, but it does temper the threat model because the government employees you’re considering trusting have given oaths of office to protect and defend the Constitution.

The reactive components are also really nice for interactive plotting and exploratory data analysis. You can do this in Jupyter but it feels less seamless somehow. Interactive marimo workflow feels like streamlit but in a notebook interface

One thing I miss from Pluto.jl workflow is `let` for lowering friction for exploratory or plot cells. In marimo you have to name a `_` prefixed function and then call it which is better than nothing but not as clean as `let`. This is a minor complaint that’s more down to language features though

What I’m pushing back on is what I think is a sort of one-dimensional view of Sutton’s bitter lesson. People seem to equate it with model scaling, but there are lots of general ways to leverage computation that don’t involve just scaling models and supervised training datasets up. For example Sutton’s first example is straight up search, no parameters at all.

The point of the force field example is that it seems you don’t need billions of parameters to represent the functions we’re interested in, but with small models it’s harder to find those functions by pushing harder on the standard training algorithms, and that maybe some different algorithm that leverages computation more effectively could do so.