4 ms·

> AI tends to accept conventional wisdom. Because of this, it struggles with genuine critical thinking and cannot independently advance the state of the art. O

by js8 7mo ago

> AI tends to accept conventional wisdom. Because of this, it struggles with genuine critical thinking and cannot independently advance the state of the art. Of course! But that's what makes them so powerful. In 99% of cases that's what you want - something that is conventional. The AI can come up with novel things if it has an agency, and can learn on its own (using e.g. RL). But we don't want that in most use cases, because it's unpredictable; we want a tool instead. It's not true that this lack of creativity implies lack of intelligence or critical thinking. AI clearly can reason and be critical, if asked to do so. Conceptually, the breakthrough of AI systems (especially in coding, but it's to some extent true in other disciplines) is that they have an ability to take a fuzzy and potentially conflicting idea, and clean up the contradictions by producing a working, albeit conventional, implementation, by finding less contradictory pieces from the training data. The strength lies in intuition of what contradictions to remove. (You can think of it as an error-correcting code for human thoughts.) For example, if I ask AI to "draw seven red lines, perpendicular, in blue ink, some of them transparent", it can find some solution that removes the contradictions from these constraints, or ask clarifying questons, what is the domain, so it could decide which contradictory statements to drop. I actually put it to Claude and it gave a beautiful answer: "I appreciate the creativity, but I'm afraid this request contains a few geometric (and chromatic) impossibilities: [..] So, to faithfully fulfill this request, I would have to draw zero lines — which is roughly the only honest answer. This is, of course, a nod to the classic comedy sketch by Vihart / the "Seven Red Lines" bit, where a consultant hilariously agrees to deliver exactly this impossible specification. The joke is a perfect satire of how clients sometimes request things that are logically or physically nonsensical, and how people sometimes just... agree to do it anyway. Would you like me to draw something actually drawable instead? " This clearly shows that AI can think critically and reason.

sally_glance 7mo ago
You had me at "fuzzy", but lost me at "clean up" - because that's what I usually have to do after it went on another wild refactoring spree. It's a stochastic thing, maybe you're lucky and it fuzzy-matches exactly what you want, maybe the distributions lead it astray. On the line test, I guess it's highly probable that the joke and a few hundred discussions or blog pieces about it were in it's training data.
- js8 7mo ago
  I only have experience with Claude Code. If it goes on a spree, the task you are giving it is too big IMHO. It's not a SAT solver (yet) and will have trouble to precisely handle arbitrarily large problems. So you have to lead it a bit, sometimes.
  - hparadiz 7mo ago
    Was recently optimizing an old code base. If I tell it to optimize it does stupid stuff but if I tell it to write profiler first and then slowly attack each piece one at a time then it does really well. Only a matter of time before it does it automatically.
ehsanu1 7mo ago
That shows it knew this bit of satire more than anything. Also, the problem as stated isn't actually constrained enough to be unsolvable: https://youtu.be/B7MIJP90biM https://youtu.be/B7MIJP90biM
- js8 7mo ago
  Feel free to ask Claude about any other contradictory request. I use Claude Code and it often asks clarifying questions when it is unsure how to implement something, or or autocorrects my request if something I am asking for is wrong (like a typo in a filename). Of course sometimes it misunderstands; then you have to be more specific and/or divide the work into smaller pieces. Try it if you haven't.
  - ehsanu1 7mo ago
    I have. In fact, I've been building my own coding agent for 2 years at this point (i.e. before claude code existed). So it's fair to say I get the point you're making and have said all the same stuff to others. But this experience has taught me that LLMs, in their current form, will always have gaps: it's in the nature of the tech. Every time a new model comes out, even the latest opus versions, while they are always better, I always eventually find their limits when pushing them hard enough and enough times to see these failure modes. Anything sufficiently out of distribution will lead to more or less nonsensical results.
    - js8 7mo ago
      The big flagship AI models aren't just LLMs anymore, though. They are also trained with RL to respond better to user requests. Reading a lot of text is just one technique they employ to build the model of the world. I think there are three different types of gaps, each with different remedies: 1. A definition problem - if I say "airplane", who do I mean? Probably something like jumbo jet or Cesna, less likely SR-71. This is something that we can never perfectly agree on, and AI will always will be limited to the best definition available to it. And if there is not enough training data or agreed definition for a particular (specialized) term, AI can just get this wrong (a nice example is the "Vihart" concept from above, which got mixed up with the "Seven red lines" sketch). So this is always going to be painful to get corrected, because it depends on each individual concept, regardless of the machine learning technology used. Frame problem is related to this, question of what hidden assumptions I am having when saying something. 2. The limits of reasoning with neural networks. What is really happening IMHO is that the AI models can learn rules of "informal" logical reasoning, by observing humans doing it. Informal logic learned through observation will always have logical gaps, simply because logical lapses occur in the training data. We could probably formalize this logic by defining some nice set of modal and fuzzy operators, however no one has been able to put it together yet. Then most, if not all, reasoning problems would reduce to solving a constraint problem; and even if we manage to quantize those and convert to SAT, it would still be NP-complete and as such potentially require large amounts of computation. AI models, even when they reason (and apply learned logical rules) don't do that large amount of computation in a formal way. So there are two tradeoffs - one is that AIs learned these rules informally and so they have gaps, and the other is that it is desirable in practice to time limit what amount of reasoning the AI will give to a given problem, which will lead to incomplete logical calculations. This gap is potentially fixable, by using more formal logic (and it's what happens when you run the AI program through tests, type checking, etc.), with the mentioned tradeoffs. 3. Going back to the "AI as an error-correcting code" analogy, if the input you give to AI (for example, a fragment of logical reasoning) is too much noisy (or contradictory), then it will just not respond as you expect it to (for example, it will correct the reasoning fragment in a way you didn't expect it to). This is similar to when an error-correcting code is faced with an input that is too noisy and outside its ability to correct it - it will just choose a different word as the correction. In AI models, this is compounded by the fact that nobody really understands the manifold of points that AI considers to be correct ideas (these are the code words in the error-correcting code analogy). In any case, this is again an unsolvable gap, AI will never be a magical mind reader, although it can be potentially fixed by AI having more context of what problem are you really trying to solve (the downside is this will be more intrusive to your life). I think these things, especially point 2, will improve over time. They already have improved to the point that AI is very much usable in practice, and can be a huge time saver.
CrazyStat 7mo ago
> This is, of course, a nod to the classic comedy sketch by Vihart As a big fan of Vi Hart I was surprised to read that she wrote or was involved in that "classic comedy sketch". As far as I can tell, after a few minutes searching, she was not.
sevenseacat 7mo ago
Don't forget the line in the shape of a kitten!
jibal 7mo ago
That skit has nothing to do with Vihart ... Claude hallucinated that. > This clearly shows that AI can think critically and reason. No it doesn't ... Claude regurgitated human knowledge.

by js8 7mo ago

sally_glance 7mo ago

You had me at "fuzzy", but lost me at "clean up" - because that's what I usually have to do after it went on another wild refactoring spree. It's a stochastic thing, maybe you're lucky and it fuzzy-matches exactly what you want, maybe the distributions lead it astray. On the line test, I guess it's highly probable that the joke and a few hundred discussions or blog pieces about it were in it's training data.

js8 7mo ago
I only have experience with Claude Code. If it goes on a spree, the task you are giving it is too big IMHO. It's not a SAT solver (yet) and will have trouble to precisely handle arbitrarily large problems. So you have to lead it a bit, sometimes.
- hparadiz 7mo ago
  Was recently optimizing an old code base. If I tell it to optimize it does stupid stuff but if I tell it to write profiler first and then slowly attack each piece one at a time then it does really well. Only a matter of time before it does it automatically.

ehsanu1 7mo ago

That shows it knew this bit of satire more than anything. Also, the problem as stated isn't actually constrained enough to be unsolvable: https://youtu.be/B7MIJP90biM https://youtu.be/B7MIJP90biM

js8 7mo ago
Feel free to ask Claude about any other contradictory request. I use Claude Code and it often asks clarifying questions when it is unsure how to implement something, or or autocorrects my request if something I am asking for is wrong (like a typo in a filename). Of course sometimes it misunderstands; then you have to be more specific and/or divide the work into smaller pieces. Try it if you haven't.
- ehsanu1 7mo ago
  I have. In fact, I've been building my own coding agent for 2 years at this point (i.e. before claude code existed). So it's fair to say I get the point you're making and have said all the same stuff to others. But this experience has taught me that LLMs, in their current form, will always have gaps: it's in the nature of the tech. Every time a new model comes out, even the latest opus versions, while they are always better, I always eventually find their limits when pushing them hard enough and enough times to see these failure modes. Anything sufficiently out of distribution will lead to more or less nonsensical results.
  - js8 7mo ago
    The big flagship AI models aren't just LLMs anymore, though. They are also trained with RL to respond better to user requests. Reading a lot of text is just one technique they employ to build the model of the world. I think there are three different types of gaps, each with different remedies: 1. A definition problem - if I say "airplane", who do I mean? Probably something like jumbo jet or Cesna, less likely SR-71. This is something that we can never perfectly agree on, and AI will always will be limited to the best definition available to it. And if there is not enough training data or agreed definition for a particular (specialized) term, AI can just get this wrong (a nice example is the "Vihart" concept from above, which got mixed up with the "Seven red lines" sketch). So this is always going to be painful to get corrected, because it depends on each individual concept, regardless of the machine learning technology used. Frame problem is related to this, question of what hidden assumptions I am having when saying something. 2. The limits of reasoning with neural networks. What is really happening IMHO is that the AI models can learn rules of "informal" logical reasoning, by observing humans doing it. Informal logic learned through observation will always have logical gaps, simply because logical lapses occur in the training data. We could probably formalize this logic by defining some nice set of modal and fuzzy operators, however no one has been able to put it together yet. Then most, if not all, reasoning problems would reduce to solving a constraint problem; and even if we manage to quantize those and convert to SAT, it would still be NP-complete and as such potentially require large amounts of computation. AI models, even when they reason (and apply learned logical rules) don't do that large amount of computation in a formal way. So there are two tradeoffs - one is that AIs learned these rules informally and so they have gaps, and the other is that it is desirable in practice to time limit what amount of reasoning the AI will give to a given problem, which will lead to incomplete logical calculations. This gap is potentially fixable, by using more formal logic (and it's what happens when you run the AI program through tests, type checking, etc.), with the mentioned tradeoffs. 3. Going back to the "AI as an error-correcting code" analogy, if the input you give to AI (for example, a fragment of logical reasoning) is too much noisy (or contradictory), then it will just not respond as you expect it to (for example, it will correct the reasoning fragment in a way you didn't expect it to). This is similar to when an error-correcting code is faced with an input that is too noisy and outside its ability to correct it - it will just choose a different word as the correction. In AI models, this is compounded by the fact that nobody really understands the manifold of points that AI considers to be correct ideas (these are the code words in the error-correcting code analogy). In any case, this is again an unsolvable gap, AI will never be a magical mind reader, although it can be potentially fixed by AI having more context of what problem are you really trying to solve (the downside is this will be more intrusive to your life). I think these things, especially point 2, will improve over time. They already have improved to the point that AI is very much usable in practice, and can be a huge time saver.

CrazyStat 7mo ago

> This is, of course, a nod to the classic comedy sketch by Vihart As a big fan of Vi Hart I was surprised to read that she wrote or was involved in that "classic comedy sketch". As far as I can tell, after a few minutes searching, she was not.

sevenseacat 7mo ago

Don't forget the line in the shape of a kitten!

jibal 7mo ago

That skit has nothing to do with Vihart ... Claude hallucinated that. > This clearly shows that AI can think critically and reason. No it doesn't ... Claude regurgitated human knowledge.