I used Alex Turners entire shortform for my prompt as context for gpt-4 which worked well enough to make the task difficult for me but maybe I just suck at this task.

By the way, if you want to donate to this but thought, like me, that you need to be an “accredited investor” to fund Manifund projects, that only applies to their impact certificate projects, not this one.

My point is more that 'regular' languages form a core to the edifice because the edifice was built on it, and tailored to it

If that was the point of the edifice, it failed successfully, because those closure properties made me notice that visibly pushdown languages are nicer than context-free languages, but still allow matching parentheses and are arguably what regexp should have been built upon.

My comment was just based on a misunderstanding of this sentence:

The 'regular' here is not well-defined, as Kleene concedes, and is a gesture towards modeling 'regularly occurring events' (that the neural net automaton must process and respond to).

I think you just meant that there's really no satisfying analogy explaining why it's called 'regular'. What I thought you imply is that this class wasn't crisply characterized then or now in terms of math (it is). Thanks to your comment though, I noticed a large gap in the CS-theory understanding I thought I had. I thought that the 4 levels usually mentioned in the chomsky hierarchy are the only strict subsets for languages that are well characterized by a grammar, an automaton and a a whole lot of closure properties. Apparently the emphasis on these languages in my two stacked classes on the subject 2 years ago was a historical accident? (Looking at wikipedia, visibly pushdown languages allow intersection, so from my quick skim more natural than context-free languages). They were only discovered in 2004, so perhaps I can forgive my two classes on the subject to not have included developments 15 years in the past. Anyone has post recommendations for propagating this update?

I noticed some time ago there is a big overlap between lines of hope mentioned in Garret Baker's post and lines of hope I already had. The remaining things he mentions are lines of hope that I at least can't antipredict which is rare. It's currently the top plan/model of Alignment that I would want to read a critique of (to destroy or strengthen my hopes). Since no one else seems to have written that critique yet I might write a post myself (Leave a comment if you'd be interested to review a draft or have feedback on the points below).

  • if singular learning theory is roughly correct in explaining confusing phenomena about neural nets (double descent, grokking), then the things confusing about these architectures are pretty straightforward implications from probability theory (Implying we might expect fewer diffs in priors between humans and neural nets because biases are less architecture dependent).
  • the idea of whether something like "reinforcing shards" can be stable if your internals are part of the context during training even if you don't have perfect interpretability
  • The idea that maybe the two ideas above  can stack? If for both humans and AI training data is the most crucial, then perhaps we can develop methods comparing human brains and AI. If we get to the point of being able to do this in detail (big If, especially on the neuroscience side this seems possibly hopeless?), then we could get further guarantees that the AI we are training is not a "psychopath".

Quite possibly further reflection feedback would change my mind and counterarguments/feedback would be appreciated. I am quite worried about motivated reasoning to think this plan is better than I think because it would give me something tractable to work on. Also to which extent people planning to work on methods that should be robust enough to survive a sharp left turn are pessimistic about lines of research like this only because of the capability externalities. I have a hard time evaluating the capability externalities of publishing research on plans like the above. If someone is interested in writing a post about this or reading it feel free to leave a comment.

Aren't regular languages really well defined as the weakest level in the Chomsky Hierarchy?

Would it change your mind if gpt-4 was able to do the grid tasks if I manually transcribed them to different tokens? I tried to manually let gpt-4 turn the image to a python array, but it indeed has trouble performing just that task alone.

That propagates into a huge difference in worldviews. Like, I walk around my house and look at all the random goods I’ve paid for - the keyboard and monitor I’m using right now, a stack of books, a tupperware, waterbottle, flip-flops, carpet, desk and chair, refrigerator, sink, etc. Under my models, if I pick one of these objects at random and do a deep dive researching that object, it will usually turn out to be bad in ways which were either nonobvious or nonsalient to me, but unambiguously make my life worse and would unambiguously have been worth-to-me the cost to make better.

Based on my 1 deep dive on pens a few years ago this seems true. Maybe that is too high dimensional and too unfocused a post, but maybe there should be a post on "best X of every common product people use every day"? And then we somehow filter for people with actual expertise? Like for pens you want to go with the recommendations of "the pen addict".

For concreteness. In this task it fails to recognize that all of the cells get filled, not only the largest one. To me that gives the impression that the image is just not getting compressed really well and the reasoning gpt-4 is doing is just fine.

I think humans just have a better visual cortex and expect this benchmark too to just fall with scale.

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