That is, there are a few "really important" (highly curved) dimensions in parameter space (the top eigenvectors) which control the model's performance (the loss function). Conversely, there are very many "unimportant"/low curvature dimensions in the model. There was a recent interesting paper that showed that "deleting" these low-curvature dimensions appeared to correspond to removing "memorized" information in LLMs, such that their reasoning performance was left unchanged while their ability to answer questions which require some memorized knowledge was reduced [2].

It appears that sometimes models undergo dramatic transitions from memorization to perfect generalization, which corresponds to the models becoming much more compressible [3].

I'm hopeful that we'll find a way to distill the models down to the most useful core cognitive/reasoning capabilities, and that that core will be far simpler than the current scale of LLMs. But they might need to look stuff up like we do without all that memorized world knowledge!

[1]: https://openreview.net/pdf?id=o62ZzfCEwZ

[2]: https://www.goodfire.ai/research/understanding-memorization-...

[3]: https://arxiv.org/abs/2412.09810

Still, comms that framed it like: "This software purchase upgrades your car with state-of-the-art autonomy capabilities from our AI team, as we approach full self-driving" would have been more honest, still exciting to consumers, and avoided over-promising.

The undergraduate teaching here is phenomenal. It's incredibly labor intensive for the staff, but the depth and breadth students are exposed to in their subject is astonishing. It's difficult to imagine how it can be improved.

My favorite study of university rankings comes from faculty hiring markets, which compute implicit rankings by measuring which institutions tend to hire (PhD->faculty) from others. [1] It's not perfect, but at the very least it's a parameter free way to get a sense of how different universities view each other. The parameters in most university rankings are rather arbitrary and game-able.

Some have pointed to things like contextual admissions [2], and more broadly some identity politics capture of the administration for declining standards. While this might be true, in my view Oxford is still far more meritocratic than US institutions on the whole. There are no legacy admissions, and many subjects have difficult tests which better distinguish between applicants who have all done extremely well on national standardised tests (British A Levels are far more difficult than the SAT/ACT/AP exams.)

Lastly, admissions at Oxford are devolved to the individual colleges, of which there are ~40. The faculty at each college directly interview and select the applicants which they will take as students. This devolved system and the friction it creates is surprisingly robust and makes complete ideological capture more difficult.

The most pressing issue for Oxford's long-term viability as a leading institution, in my view, is the funding situation. For one the British economy is in a long, slow decline. Secondly, even though Oxford has money, there are lots of regulations/soft power influence from the British govt to standardise pay across the country, which makes top institutions like Oxford less competitive on the international market for PhD students, postdocs, and faculty in terms of pay.

[1]: https://www.science.org/doi/10.1126/sciadv.1400005

[2]: https://www.ox.ac.uk/admissions/undergraduate/applying-to-ox...

[1] https://www.reddit.com/r/oxforduni/comments/q0giir/my_all_so...

* Oops, they link to my post at the bottom. Sorry for the redundancy.

I'm most excited for when these methods will make a meaningful difference in robotics. RL is still not quite there for long-horizon, sparse reward tasks in non-zero-sum environments, even with a perfect simulator; e.g. an assistant which books travel for you. Pay attention to when virtual agents start to really work well as a leading signal for this. Virtual agents are strictly easier than physical ones.

Compounding on that, mismatches between the simulated dynamics and real dynamics make the problem harder (sim2real problem). Although with domain randomization and online corrections (control loop, search) this is less of an issue these days.

Multi-scale effects are also tricky: the characteristic temporal length scale for many actions in robotics can be quite different from the temporal scale of the task (e.g. manipulating ingredients to cook a meal). Locomotion was solved first because it's periodic imo.

Check out PufferAI if you're scale-pilled for RL: just do RL bigger, better, get the basics right. Check out Physical Intelligence for the same in robotics, with a more imitation/offline RL feel.

Comments URL: https://news.ycombinator.com/item?id=44485374

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Comments URL: https://news.ycombinator.com/item?id=44248180

Points: 1

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Honestly I've asked different hardware researchers this question and they all seem to give different answers.

Are there a few big things, many small things...? I'm curious what fruit are left hanging for fast SIMD matrix multiplication.

The entropy of some data is well-defined with respect to a model, but the model choice is free. I.e. different models will assign different entropy to the same data.

And how do we choose a model...? Well, formally by minimizing the information needed to describe both the model and data (the sum of model complexity and data entropy under the model) [1]

You might argue that's all too information-theoretic and in physics there simply is an objective count of the state-space, a maximum entropy, and so on. Alas, there is not even general consensus on whether there is a locally finite number of degrees of freedom.

[1]: https://en.wikipedia.org/wiki/Minimum_description_length

Very few true eccentrics left.

Comments URL: https://news.ycombinator.com/item?id=42434095

Points: 1

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lim n->\infty K(X|n)/n

Possible solutions that come tom mind:

1) UTMs are actually too powerful, and we should use a finitary abstraction to have a more sensible measure of complexity for finite strings.

2) We might need to define a kind of "relativity of complexity". This is my preferred approach and something I've thought about to some degree. That is, that we want a way of describing the complexity of something relative to our computational resources.

Presumably there's a market for this in other niches, e.g. weather monitoring, defense/border monitoring, etc... The question is whether the juice is worth the squeeze. Where's the really valuable data?