goodtraveler

Ok, thanks for the references.

My claim is that intelligence is more than just statistical associations and abstract symbol shuffling. It's impressive what large statistical models can do but they still can not solve sudoku so something is clearly missing here because neural networks do not have feedback loops and backtracking. It's like saying all we need to do is continue building bigger and bigger abaci and stacking them in just the right way as to emulate the statistical properties of the real world. Dall-e is dazzling but it is still a statistical model with no symbolic understanding (it's still a giant abacus). It's obvious that people have symbolic understanding (e.g. written language, mathematics, solving sudoku, writing code/software, etc.). So if people are the benchmark of intelligence (dubious but let's assume for the sake of argument) then at what point do you suppose there will be statistical models with symbolic understanding? Furthermore, what reason is there to believe that larger and larger statistical models are going to get us closer to non-human intelligent systems that do more than generate stimuli adapted to our senses?

There is also a meta-problem that no one seems to address when discussing AI. All the systems we have built rely on compositional symbolic systems (mathematics) for expressing statistical associations and human interpretation of their inputs/outputs. Clearly there is something people can do that no existing AI system can which is to generate a symbolic description of statistical models that can be adapted to various data sets.

I could say more here but none of what I'm saying is anything new. Others much more capable of describing the issues and shortcoming of the existing approaches to AI have written books exploring the issues in much more detail, e.g. Gary Marcus, Melanie Mitchell, Douglas Hofstadter, Gian-Carlo Rota, etc.

I don't appreciate the sarcasm. If you're confused then say so, save the snark for someone else. If you're asking a sincere question then you can go outside and watch a few animals navigate the world. Squirrels are a good one, as are crows and ravens. Or as the kids say these days, you should touch some real grass instead of virtual ones.

Because if an algorithm is capable of setting up the conceptual machinery for making sense of and explaining Cauchy's integral formula then that algorithm is revealing something about the structure of the human brain and as such it would be a good benchmark for understanding how human intelligence actually works (not just in the 1% of people). Moreover, mathematics is a fantastic proving ground for testing algorithms that purport to explain symbolic intelligence because if something can not work in a mathematical setting/context then there is no hope it will ever work in the real world since real world intelligence is much more than symbol shuffling. Cauchy's integral formula is a reasonable proxy for proving understanding of symbolic systems and not just juggling their statistical properties/associations.

If you think that Cauchy's integral formula is too complicated then there are probably simpler problems that would also serve as reasonable proxies of symbolic understanding, e.g. elementary group theory and linear algebra.

OpenAI shuttered their robotics division because they did not see any viable path to commercial applications so they pivoted to generating pixel art. Similarly, DeepMind has not been making any claims of achieving AGI because they're smart enough to realize that statistical modeling of physical systems is a very small subset of what counts as intelligence.

I'm not dismissive of the progress in the field. What I find confusing is why so many people are convinced that what we are seeing with these abstract symbol shuffling systems is intelligence. All it does is confuse the average person about what these tools are capable of because at the moment they are only capable of amplifying biases in existing data sets. No statistical model can escape this trap and at the moment we essentially have automated bias amplifiers that are being sold as some kind of revolution in designing intelligent systems.

And yet none of these systems can do anything beyond generating statistical patterns adapted to our visual sense. It's certainly a worthwhile achievement but I have yet to see anyone make a convincing case that there is a way to scale these systems to anything resembling intelligence. My current test for whether these systems are showing us how intelligence actually works in the human brain is a convincing proof of Cauchy's integral formula and I'm certain this benchmark will remain unsolved for the foreseeable future.

OP makes great points that are not being addressed here. If you believe that animal intelligence is reducible to abstract symbol shuffling then it should be possible to extend the existing approaches to actual robotic automatons but it is clear that these models are unable to deal with the dynamic structure of the world and there is no reasonable avenue to actually make that happen. So either intelligence is reducible to abstract symbol shuffling or there is a semantic fib happening here that substitutes "abstract symbol shuffling" for "intelligence". The fact that we imbue these abstract symbols with meaning is where the intelligence comes from, i.e. people attribute intelligent behavior to these systems because we have learned to attribute intelligent behavior to the production of abstract symbols.

It's clear to me that current mathematics is insufficient for creating intelligence (let alone general intelligence). If you know of anyone that makes a coherent case for the mathematical basis of intelligence then I would like to see those references.

Mathematics is a useful way to make sense of the world but it's certainly possible that the universe is not itself a mathematical structure which would mean that mathematics is only an approximation (albeit a very good one when it comes to making sense of and predictions about the world). So I disagree with the author's conclusion that the universe must be mathematical and that we could not make rational sense of it if that wasn't the case. The universe is a big place and even the best theory we have of describing its large scale structure (general relativity) is mathematically incomplete (singularities). Similarly the small scale structure is described by another incomplete theory (quantum mechanics) and progress on reconciling the two has been stuck for some time now.

> Just as there are odors that dogs can smell and we cannot, as well as sounds that dogs can hear and we cannot, so too there are wavelengths of light we cannot see and flavors we cannot taste. Why then, given our brains wired the way they are, does the remark, “Perhaps there are thoughts we cannot think,” surprise you? Evolution, so far, may possibly have blocked us from being able to think in some directions; there could be unthinkable thoughts. If you recall that modern science is only about 400 years old, and that there have been from 3 to 5 generations per century, then there have been at most 20 generations since Newton and Galileo. If you pick 4,000 years for the age of science, generally, then you get an upper bound of 200 generations. Considering the effects of evolution we are looking for via selection of small chance variations, it does not seem to me that evolution can explain more than a small part of the unreasonable effectiveness of mathematics. [1]

Hamming elaborates more on what he means by "the unreasonable effectiveness of mathematics" in Chapter 23 of "The Art of Doing Science and Engineering: Learning to Learn" and the entire chapter is worth reading in full because it's full of other useful comments on mathematics and symbolic systems of reasoning.

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1: The Unreasonable Effectiveness of Mathematics (1980) - Richard Hamming

My prediction is he will not buy Twitter and he is using the hype around his announcement to make a point about social media censorship and algorithmic control of engagement. Whether he will sell his stake or not is still unclear but I think he will if the price goes up.