Ben Goldhaber

• Ben Goldhaber 5 Jan 2026 15:25 UTC

  2 points

  0

  on: bgold’s Shortform

  In Jack Clark’s Import AI 439, he references a new paper Universally Converging Representations of Matter Across Scientific Foundation Models
  
  > Do AI systems end up finding similar ways to represent the world to themselves? Yes, as they get smarter and more capable, they arrive at a common set of ways of representing the world.
  > The latest evidence for this is research from MIT which shows that this is true for scientific models and the modalities they’re trained on: “representations learned by nearly sixty scientific models, spanning string-, graph-, 3D atomistic, and protein-based modalities, are highly aligned across a wide range of chemical systems,” they write. “Models trained on different datasets have highly similar representations of small molecules, and machine learning interatomic potentials converge in representation space as they improve in performance, suggesting that foundation models learn a common underlying representation of physical reality.”

  > As with other studies of representation, they found that as you scale the data and compute models are trained on, “their representations converge further”.
  
  This seems like useful empirical evidence for the Natural Abstraction Hypothesis (I haven’t been following progress on that research agenda, so I don’t know how significant of an update this is)

A Full Epistemic Stack: Knowl­edge Com­mons for the 21st Century

• Ben Goldhaber 2 Dec 2025 14:44 UTC

  3 points

  0

  in reply to: Saul Munn’s comment on: Ex­plo­sive Skill Acquisition

  Thank you for the comment Saul—I agree with a lot of your points, in particular that “explosive” periods are costly and inefficient (relative perhaps to some ideal), and that they are not in and of themselves a solution for long-term retention.

  I expect if we have a crux it’s whether someone who intends to follow an incremental path vs someone who does an intense acquisition period is more likely to, ~ a year later, actually have the skill. And my guess is, for a number of reasons, it’s the later; I’d expect a lot of incrementalists to ‘just not actually do the thing’.

  * My ideal strategy would be “explore lightly a number of things, to determine what you want → explode towards that for an intense period of time → establish incremental practices to maintain and improve”

  * Your comment also highlighted for me, something that I had cut from the initial draft, my belief that explosive periods help overcome emotional blockers, which I think might be a big part of why people shy away from skills they say they want.

• Ben Goldhaber 30 Nov 2025 19:25 UTC

  2 points

  0

  in reply to: cousin_it’s comment on: Ex­plo­sive Skill Acquisition

  No worries, I appreciate the perspective. I agree that for many skills there is a consolidation and rest period that is needed. An obvious example is that you can’t cram all of the effort needed to build muscle into one week and expect the same kinds of returns that you would get over many months. Though, I do expect you could master the biomechanical skills of weightlifting much faster with that attitude!

  If you have examples of the multidimensional learning schedule, I’d love to hear them. I’m imagining something like {30 minutes of spanish language shows}?

• Ben Goldhaber 30 Nov 2025 18:56 UTC

  2 points

  0

  in reply to: philip_b’s comment on: Ex­plo­sive Skill Acquisition

  thanks for sharing a data point on that claim

Ex­plo­sive Skill Acquisition

The First Thanksgiving

• Ben Goldhaber 26 Nov 2025 4:06 UTC

  LW: 6 AF: 2

  4

  AF

  on: Align­ment will hap­pen by de­fault. What’s next?

  Thank you for the post! I have also been (very pleasantly!) surprised by how aligned current models seem to be.

  I’m curious if you expect ‘alignment by default’ to continue to hold in a regime where continuous learning is solved and models are constantly updating themselves/​being updated by what they encounter in the world?
  
  Chain of Thought not producing evidence of scheming or instrumental convergence does seem like evidence against, but it seems quite weak to me as a proxy for what to expect from ‘true agents’. CoT doesn’t run long enough or have the type of flexibility I’d expect to see in an agent that was actually learning over long time horizons, which would give it the affordance to contemplate the many ways it could accomplish its goals.

  And, while just speculation, I imagine that the kind of training procedures we’re doing now to instill alignment will not be possible with Continuous Learning, or we’ll have to pay a heavy alignment tax to do that for these agents. Note: Jan’s recent tweet on his impression that it is quite hard to align large models and it doesn’t fall out for free from size.

[Question] Are there ex­am­ples of com­mu­ni­ties where AI is mak­ing epistemics bet­ter now?

• Ben Goldhaber 10 Nov 2025 4:48 UTC

  5 points

  0

  in reply to: Mo Putera’s comment on: Un­ex­pected Things that are People

  Thank you! I had not read this Kevin Simler essay but I quite like it, and it does match my perspective.

Un­ex­pected Things that are People

FLF Fel­low­ship on AI for Hu­man Rea­son­ing: $25-50k, 12 weeks

• Ben Goldhaber 28 Apr 2025 18:17 UTC

  3 points

  0

  in reply to: Garrett Baker’s comment on: bgold’s Shortform

  I did! and I in fact have read—well some of :) - the whitepaper. But it still seems weird that it’s not possible to Increase the Trust in the third party through financial means, dramatic PR stunts (auditor promises to commit sepuku if they are found to have lied)

• Ben Goldhaber 28 Apr 2025 18:15 UTC

  1 point

  0

  in reply to: ryan_greenblatt’s comment on: bgold’s Shortform

  source needed, but I recall someone on the community notes team saying it was very similar but there are some small differences between prod and the open source version (it’s difficult to maintain exact compatibility). For the point of the comment and context I agree open source does a good job of this, though given the number of people on twitter who still allege its being manipulated, I think you need some additional juice (a whistleblower prize?)

• Ben Goldhaber 26 Apr 2025 16:55 UTC

  16 points

  0

  on: bgold’s Shortform

  Why so few third party auditors of algorithms? for instance, you could have an auditing agency make specific assertions about what the twitter algorithm is doing, whether the community notes is ‘rigged’
  

  • It could be that this is too large of a codebase, too many people can make changes, it’s too hard to verify the algorithm in production is stable. This seems unlikely to me with most modern devops stacks

  • It could be that no one will trust the third party agency. I guess this seems most likely… but really, have we even tried? Could we not have some group of monk like Auditors who would rather die than lie (my impression is some cyber professionals have this ethos already)

  If Elon wanted to spend a couple hundred thousand on insanely commited high integrity auditors, it’d be a great experiment

• Ben Goldhaber 24 Apr 2025 14:26 UTC

  4 points

  0

  on: bgold’s Shortform

  epistemic status: thought about this for like 15 minutes + two deep research reports
  
  a contrarian pick for underrated technology area is lie detection through brain imaging. It seems like it will become much more robust and ecologically valid through compute scaled AI techniques, and it’s likely to be much better at lie detection than humans because we didn’t have access to images of the internals of other peoples brains in the ancestral environment.
  
  On the surface this seems like it would be transformative—brain scan key employees to make sure they’re not leaking information! test our leaders for dark triad traits (ok that’s a bit different than specific lies but still) - however there’s a cynical part of me that sounds like some combo of @ozziegooenand Robin Hanson which notes we have methods now (like significantly increased surveillance and auditing) which we could use for greater trust and which we don’t employ.
  
  So perhaps this won’t be used except for the most extreme natsec cases, where there are already norms of investigations and reduced privacy.
  
  Related quicktake: https://​​www.lesswrong.com/​​posts/​​hhbibJGt2aQqKJLb7/​​shortform-1#25tKsX59yBvNH7yjD

• Ben Goldhaber 24 Apr 2025 14:14 UTC

  3 points

  0

  in reply to: ozziegooen’s comment on: bgold’s Shortform

  Good points! I agree that actual prototyping is necessary to see if an idea works, and as a demo it can be far more convincing. Especially w/​ the decreased cost of building web apps, leveraging them for fast demos of techniques seems valuable.

• Ben Goldhaber 22 Apr 2025 15:35 UTC

  14 points

  9

  on: bgold’s Shortform

  AI for improving human reasoning seems promising; I’m uncertain whether it makes sense to invest in new custom applications, as maybe improvements in models are going to do a lot of the work.

  I’m more bullish on investing in exploration of promising workflows and design patterns. As an example, a series of youtube videos and writeups on using O3 as a forecasting aid for grantmaking, with demonstrations. Or a set of examples of using LLMs to aid in productive meetings, with a breakdown of the tech used and social norms that the participants agreed to.
  - I think these are much cheaper to do in terms for time and money.
  - A lot of epistemics seems to be HCI bottlenecked.
  - Good design patterns are easily copyable, which also means they’re probably underinvested in relative to their returns.
  - Social diffusion of good epistemic practices will not necessarily hapepn as fast as AI improvements.
  - Improving the AIs themselves to be more truth seeking and provide good advice—with good benchmarks—is another avenue.

  I imagine a fellowship for prompt engineers and designers, prize competitions, or perhaps retroactive funding for people who have already developed good patterns.

• Ben Goldhaber 14 Apr 2025 15:48 UTC

  7 points

  −1

  on: bgold’s Shortform

  I think people should write a bunch of their own vignettes set in the AI 2027 universe. Small snippets of life predictions as things get crazy, on specific projects that may or may not bend the curve, etc.

• Ben Goldhaber 1 Apr 2025 20:34 UTC

  5 points

  2

  in reply to: Ben Goldhaber’s comment on: Prov­ably Safe AI: Wor­ld­view and Projects

  fyi @Zac Hatfield-Dodds my probability has fallen below 10% - I expected at least one relevant physical<>cyber project to have started in the past six months, since it hasn’t I doubt this will make the timeline. While not conceding (because I’m still unsure how far AI uplift alone gets us), seems right to note the update.