So on the -meta-level you need to correct weakly in the other direction again.
Morpheus
Karma: 677
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.
Looking at how gpt-4 did on the benchmark when I gave it some screenshots, the thing it failed at was the visual “pattern matching” (things completely solved by my system 1) rather than the abstract reasoning.
Thanks for clarifying! I just tried a few simple ones by prompting gpt-4o and gpt-4 and it does absolutely horrific job! Maybe trying actually good prompting could help solving it, but this is definitely already an update for me!
LLMs have failed at ARC for the last 4 years because they are simply not intelligent and basically pattern-match and interpolate to whatever is within their training distribution. You can say, “Well, there’s no difference between interpolation and extrapolation once you have a big enough model trained on enough data,” but the point remains that LLMs fail at the Abstract Reasoning and Concepts benchmark precisely because they have never seen such examples.
No matter how ‘smart’ GPT-4 may be, it fails at simple ARC tasks that a human child can do. The child does not need to be fed thousands of ARC-like examples; it can just generalize and adapt to solve the novel problem.
I don’t get it. I just looked at ARC and it seemed obvious that gpt-4/gpt-4o can easily solve these problems by writing python. Then I looked it up on papers-with-code and it seems close to solved? Probably the ones remaining would be hard for children also. Did the benchmark leak into the training data and that is why they don’t count them?
Feel free to write a post if you find something worthwhile. I didn’t know how likely the whole Biden leaving the race thing was so 5% seemed prudent. At those odds, even if I belief the fivethirtyeight numbers I’d rather leave my money in etfs. I’d probably need something like >>1,2 multiplier in expected value before I’d bother. Last year when I was betting on Augur I was also heavily bitten by gas fees (150$ transaction costs to get my money back because gas fees exploded for eth), so would be good to know if this is a problem on polymarket also.
Heuristics I heard: cutting away moldy bits is ok for solid food (like cheese, carrot). Don’t eat moldy bread, because of mycotoxins (googeling this I don’t know why people mention bread in particular here). Gpt-4 gave me the same heuristics.
Has anyone here investigated before if washing vegetables/fruits is worth it? Until recently I never washed my vegetables, because I classified that as a bullshit value claim.
Intuitively, if I am otherwise also not super hygienic (like washing my hands before eating) it doesn’t seem that plausible to me that vegetables are where I am going to get infected from other people having touched the carrots etc… . Being in quarantine during a pandemic might be an exception, but then again I don’t know if I am going to get rid of viruses if I am just lazily rinsing them with water in my sink. In general washing vegetables is a trivial inconvenience I’d like to avoid, because it leads me to eat less vegetables/fruits (like raw carrots or apples).
Also I assume a little pesticides and dirt are not that bad (which might be wrong).
Sounds like the right kind of questions to ask, but without more concrete data on what questions your predictions were off by how much, it is hard to give any better advice than: if your gut judgement tends to be 20% off after considering all evidence, move the number 20% up.
Personally me and my partner have a similar bias, but only for ourselves, so making predictions together on things like “Application for xyz will succeed. Y will read, be glad about and reply to the message I send them” can be helpful in cases where there are large disagreements.
I’ve recently tried to play this again with @Towards_Keeperhood. We think it was still working a year ago. He would be happy to pay a 50$ bounty for this to get fixed by reverting it to the previous version (or whatever happened there). If the code was public that would also be helpful, because then I might get to fixing it.
What (human or not) phenomena do you think are well explained by this model? I tried to think of any for 5 minutes and the best I came up with was the strong egalitarianism among hunter gatherers. I don’t actually know that much about hunter gatherers though. In the modern world something where “high IQ” people are doing worse is sex, but it doesn’t seem to fit your model.