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Reports of code's death are greatly exaggerated

stevekrouse.com

441–450 of 486 posts

Re: Reports of code's death are greatly exaggerated

#441

Earlier quoted context omitted.

Something I think about frequently is that 20 years ago, there weren’t machines that could do visual object recognition/categorization and we didn’t really have a clue how humans did it either. We knew that neuron built fancier and fancier receptive fields that became “feature detectors”, but h the ere was a sense of “is that all it takes? There has to be something more sophisticated in order to handle illumination c…

Are modern visual recognition & categorisation systems comparable to human capabilities? From what I can tell, they aren't even close (although still impressive!).

They aren't, or capchas wouldn't be a thing any longer.

Re: Reports of code's death are greatly exaggerated

#442
post #373
post #333

Earlier quoted context omitted.

No, but unless you find evidence to suggest we exceed the Turing computable, Turing completeness is sufficient to show that such systems are not precluded from creativity or intelligence.

I believe that quantum oracles are more powerful than Turing oracles, because quantum oracles can be constructed, from what I understand, and Turing oracles need infinite tape. Our brains use quantum computation within each neuron [1]. [1] https://www.nature.com/articles/s41598-024-62539-5

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Re: Reports of code's death are greatly exaggerated

#443
post #398
post #373

Earlier quoted context omitted.

I believe that quantum oracles are more powerful than Turing oracles, because quantum oracles can be constructed, from what I understand, and Turing oracles need infinite tape. Our brains use quantum computation within each neuron [1]. [1] https://www.nature.com/articles/s41598-024-62539-5

There's no evidence to suggest a quantum computer exceeds the Turing computable.

The difference is quantum oracles can be constructed [1] and Turing oracle can't be [2]: "An oracle machine or o-machine is a Turing a-machine that pauses its computation at state "o" while, to complete its calculation, it "awaits the decision" of "the oracle"—an entity unspecified by Turing "apart from saying that it cannot be a machine" (Turing (1939)."

  [1] https://arxiv.org/abs/2303.14959
  [2] https://en.wikipedia.org/wiki/Turing_machine

Re: Reports of code's death are greatly exaggerated

#444

> unless you believe in magic, it's only a matter of time until we reach the point at which machine intelligence is indistinguishable from human intelligence I find this flippancy about the greatest mystery in the universe extremely arrogant and incurious and wish it wouldn't be so prevalent.

Hans Moravec introduced the idea of the "landscape of human competence" , a topology representing the peaks and valleys of human capabilities. Art, writing, coding, game playing. Elevation corresponds to cognitive difficulty, and the landscape maps to everything humans are capable of doing. AI is represented as the rising waterline - when Moravec created the idea, AI was more or less constrained to a few scattered la…

A hiker on a mountain might as well imagine that at the end of their journey they will step off onto the moon. But it's just a mirage. As us humans have externalized more and more of our understanding of the world into books, movies, websites and the like, our methods of plumbing this treasury for just the needed tidbits have developed as well. But it's still just working off that externalized collective understanding. This includes heuristics for combining different facts to produce new ones, sure, but still dependent on brilliant individuals to raise the "island peaks" which ultimately pulls up the level of the collective intelligence as well.

Re: Reports of code's death are greatly exaggerated

#445

> unless you believe in magic, it's only a matter of time until we reach the point at which machine intelligence is indistinguishable from human intelligence I find this flippancy about the greatest mystery in the universe extremely arrogant and incurious and wish it wouldn't be so prevalent.

Hans Moravec introduced the idea of the "landscape of human competence" , a topology representing the peaks and valleys of human capabilities. Art, writing, coding, game playing. Elevation corresponds to cognitive difficulty, and the landscape maps to everything humans are capable of doing. AI is represented as the rising waterline - when Moravec created the idea, AI was more or less constrained to a few scattered la…

I think you would need to work very hard to prove that the topology you are describing is well-formed enough for this analogy to make sense. For one: "cognitive difficulty" is not really a crisply defined quantity such that expressing it as a function of some input vector makes obvious sense (to me anyways). What's the cognitive difficulty of deciding what to have for dinner? What's the cognitive difficulty of making my 5 year plan? What's the cognitive difficulty of imagining a nice gift to get my wife for her birthday? There are so many things humans do which are heavily 'contingent' (in the sense of having sensitivity to the local culture, history, personal experience, etc) that the idea of being able to assign everything a single, decidable scalar to represent 'difficulty' seems like an extremely tall order to me. And that's setting aside whether the ambient vector space of 'human capabilities' is even really a sensible construct (a proposition that I also doubt quite heavily).

All this to say that describing what's happening as a 'rising tide' seems misleading to me. Techno-sociological development is super messy already, let's not make it more complex by pinning ourselves to inaccurate and potentially misleading analogies. The introduction of the car did not 'push humans higher onto a set of capability peaks', it implied a total reorganization of behavior and technologies (highways, commuting, and suburban sprawl); using the terms of your analogy humans built new landmasses on top of the water.

Re: Reports of code's death are greatly exaggerated

#446

This morning a person posted a question to the Reddit group r/Mathematica ( https://www.reddit.com/r/Mathematica/comments/1s1fin2/can_ho... ). I asked GPT to write code to address their question and the code was quite acceptable drawing the circle and finding the correct intersection point. It would have take me about 40 minutes to write the code, so I would not have done it myself. Currently, GPT is great for writin…

This take was accurate about 2 years ago, up until perhaps one year ago. Current capabilities far exceed what you are outlining, for example using Claude Opus models in a harness such as Claude Code or OpenCode.

Re: Reports of code's death are greatly exaggerated

#447
post #349

Earlier quoted context omitted.

Compilers are a hobby of mine, and I'd extend that to argue that the majority of compilers do not contain anything innovative either.

Also: if that one particular AI-produced compiler has nothing innovative, that only means that the human "director" behind the AI didn't ask it to produce anything innovative; what it does not mean is that AI can never produce anything innovative in a compiler.

> if that one particular AI-produced compiler has nothing innovative, that only means that the human "director" behind the AI didn't ask it to produce anything innovative

Couldn't it also be true that the AI didn't produce innovative output even though the human asked it to produce something innovative?

Otherwise you're saying an AI always produces innovative output, if it is asked to produce something innovative. And I don't think that is a perfection that AI has achieved. Sometimes AI can't even produce correct output even when non-innovative output is requested.

Re: Reports of code's death are greatly exaggerated

#448

Chris Lattner, inventor of the Swift programming language recently took a look at a compiler entirely written by Claude AI. Lattner found nothing innovative in the code generated by AI [1]. And this is why humans will be needed to advance the state of the art. AI tends to accept conventional wisdom. Because of this, it struggles with genuine critical thinking and cannot independently advance the state of the art. AI…

I think Lattner was too generous and missed a couple of crucial points in the CCC experiment. He wrote:

> CCC shows that AI systems can internalize the textbook knowledge of a field and apply it coherently at scale.

Except that's not what happened. There was neither (just) textbook knowledge nor a "coherent application at scale":

1. The agents relied on thousands of human written tests embodying many person-years of "preparation effort", not to mention a complete spec. Furthermore, their models were also trained not only on the spec (and on the tests) but also on a reference implementation and the agents were given access to the reference implementation as a test oracle. None of that is found in a textbook.

2. Despite the extraordinary effort required to help the agents in this case - something that isn't available for most software - the models ultimately failed to write a workable C compiler, and couldn't converge. They reached a point where any bug fix caused another bug and that's when the people running the agents stopped the experiment.

The main issue wasn't that there was nothing innovative in the code but that even after embibing textbooks and relying on an impractical amount of preparation effort of help, the agents couldn't write a workable C compiler (which isn't some humongous task to begin with).

Re: Reports of code's death are greatly exaggerated

#450

Earlier quoted context omitted.

In software, they can and do perform experiments (make a change then observe the log output). I don't think they possess a "world model" or that it's worth spending too much thought on... My reasoning is more along the lines that our brains are also just [very advanced] inference machines. We also hallucinate and mis-identify images (there are image/video classification tasks where humans have lower scores). For me t…

Software is not the world. It’s a tiny bit of what humans do. The lack of online learning is a critical fault. Much of what humans learn (such as anything based on mathematics) has a dependency tree of stuff to learn. But even mundane stuff involves a lot of dependent learning. For example, ask an LLM to write a cookbook and it can synthesize from recipes that are already out there but good luck having it invent new…

I guess we'll just have to wait and see how things turn out. Currently it seems we have examples of where it seems like the technology allows some amount of innovation (AlphaGo, software, math proofs) and examples where they seem surprisingly stupid (recipes?).

Btw, it looks like there is a growing body of research evaluating exactly this. I found this nice overview with even some benchmarks specifically for scientific innovation: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Dis...

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