These are people really, in my experience, with no science background -- c-sci programmers who dont have any conceptual foundations in applied mathematics or science (outside of the discrete math taught in csci) -- and who take these properties as genuinely quite magical.
"Intelligence" is, to them, just some function and if NNs can approximate any fn, then presumably they'll be intelligent.
They arent aware that in a sense, everything is a dynamical function of space and time (say, stuff(x, t)) and to instantite it, one requires the entire universe.
In otherwords, csci people are not used to thinking about applied mathematics in the sense of science (, implementation of functions, dynamical functions, and so on). I think it is important for this audience to demystify these properties.
Being a universal fn approximator, in my view, is neither a necessary nor sufficient property of any system implementing intelligence. It's really a misdirection.
All you see is numbers and functions, and all I see is problems being solved by these AI/ML methods and often in quantifiably better ways than intelligent humans can.
NN-s are the building blocks. Neurons in your brain are dumb as well, it's the quality of the network that matters. So instead of moving goalposts, what do you think is required to implement intelligence?
I don't know if NNs are sufficiently powerful escape that argument because my formal understanding of "the world" simply isn't good enough, but it's not obvious to me that they are.
It can be quite practical though as we failed to come up with solutions that were previously out of reach before deep neural networks (and massive increases in hardware capabilities).
This is not a practical approach. It's a maddenlingly resource-intensive approach that spams programs at an insane rate and hopes to get lucky and catch a few that solve a programming task. In the evaluations listed in the AlphaCode paper, it performs awfully against the test set of its training dataset and a pre-existing benchmark. DeepMind have drummed up their systems ability to do about as well as the average participant in a code contest, but that's no measure of coding ability.
Deep neural nets are certainly very far from automating human problem solving, like you say in your comment. Your comment is exactly the kind of overhyped misunderstanding of the state-of-the-art that the OP is warning against.
Problem solving is getting automated, but to what extent is debatable, we are indeed in the early stages. I haven't suggested that it's already AGI.
The approach taken by DeepCode -- generate-and-test -- was always possible and is a staple in inductive program synthesis. DeepCode's version is a particularly crude and backwards-looking attempt at implementing that approach. It's no advance of any kind and it only looks like it is because DeepMind's preprint reporting on it carefully ignores most of what has gone before in inductive program synthesis, the better to show off its new system. It's a scandalous tactic that is a staple of DeepMind's publications and I'm very sorry that I can't find anything positive to say about it.
Also, I'm not talking about AGI. At this point, AGI is a fantasy. What we can hope for is to create systems that we can control well, for example to make them learn what we want them to learn and not to overfit to their dataset. In this, also, there seems to be a regression, recently, as the tendency is to train systems end-to-end while trying to remove "the human in the loop" (which is, of course, impossible).
I thought you did, because fully automated problem solving is AGI, but I don't claim that we are anywhere near to reach it.
I agree it's probably far away, but it's a fantasy I like to imagine and I don't think it's an impossible goal. Don't be sorry, I appreciate a fair criticism.
> What we can hope for is to create systems that we can control well, for example to make them learn what we want them to learn and not to overfit to their dataset.
I wish you strong nerves, because it will have AGI written all over it :) Again, this is not a claim, just a prediction.
There's a lot which is odd (and dubious) in AI/ML that traces is origins to this peculilar situation of a discipline (csci) in the "early phases" of modelling an empirical phenomenon, without yet, an intra-disciplinary theoretical support for it -- csci people take geometry and classical physics to make video games. They dont yet take the equivalent to make intelligent systems (which would include stuff on learning in animals, humans; and in my view, more applied math).
In any case, to answer your question about implementation, see https://news.ycombinator.com/threads?id=mjburgess#30579711
One of the single approximated "neurons" in a AI/ML NN doesn't even operate anywhere close to a real neuron. Real neurons are oscillators which exhibit nonlinear dynamic behaviors
The current DNN based approach do not do that. Sure you can create an autonomous vehicle that uses NN to help navigate - but it does not update it's inference if it is incorrect. That immediate feedback doesn't exist or rather it is not accessible to the system itself. One has to collect the data, retrain the network and deploy it again to close the loop.
>> All you see is numbers and functions, and all I see is problems being solved by these AI/ML methods and often in quantifiably better ways than intelligent humans can.
Maybe in playing chess and go, but in any other task that neural networks have been compared to humans directly, the ability to outperform humans is more informative of the limitations of the measures of performance, than of the capabilities of the tested systems (typically, deep neural networks).
Take large language models (LLMs) for example. Last time I looked -it was a while ago but- LLMs like BERT, ERNIE and their famous friends scored very highly and even above humans in natural language understanding tasks, such as those in the GLUE and SUPERGLUE benchmarks.
You'd think that a system that scores 85% in a language understanding task has developed at least _some_ ability to understand language. And yet this is not the case. LLMs, despite their ability to score highly in formal benchmarks, remain dumb as bricks and as incapable of understanding human language as a rock.
We know this because every once in a while someone in the field of NLP looks away from their favourite leaderboard for long enough to ask serious questions about the performance of systems, as for example [1] who started by asking why a language model like BERT would be any good at argument comprehension (BERT scored only three points below humans in a relevant benchmark task). Everytime anyone undertakes this kind of analysis of the abiltiy of LLMs to understand language, the conclusion is that they don't, and instead they learn "cheap tricks" and overfit to statistical regularities of the benchmark datasets. Similar analyses have found similar results in machine vision (e.g. [2], [3]) etc.
The question then is, in what sense, exactly, is an automated system solving problems "in quantifiably better ways than intelligent humans can", when that system is not even solving the problem it's supposed to be solving, like the problem of understanding language by LLMs? The idea that those systems "solve problems", when they 're really only overfitting to their training sets, is an artifact of the shoddy scholarship that is typical in deep learning research, and the OP is right to be concerned that there is little understanding between machine learning researchers of what, exactly, it is that their systems are doing- or even trying to do.
And, as the OP points out, I'm afraid that really is the result of a poor background, not only in sciences and mathematics, but in AI and even machine learning itself, among the current generation of machine learning researchers. Contrary to what the OP says, though, this paucity of relevant background is the result of an influx to the field of people from the sciences, who have a background in continuous mathematics, but not in AI or computer science. It is, indeed, a background in computer science that can best guard against misconceptions about computers being wonder machines with magickal intelligence abilities. At the very least, you don't have to explain to a computer scientist why solving one instance of a class of problems does not mean a general solution for the entire class. That comes built-in to most who have graduated in the discipline. But is it built-in to phycisists, or biologists, who study very different systems?
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[1] Probing Neural Network Comprehension of Natural Language Arguments
https://aclanthology.org/P19-1459/
[2] Intriguing properties of neural networks
https://research.google/pubs/pub42503/
[3] The Elephant in the Room
Perhaps "scientist" is too vague a term. I mean people with a "in their bones" appreciation of experimentalism, empirical adequecy etc.
What you're saying overall aligns with my view -- no on here is defining empirical adequacy on ML systems as "theories of natural language". As soon as you do this, which would be step1 in any experimental endeavour, the whole show collapses.
No one here is being taught to target empirical systems and develop criteria of empirical adequacy... thinking somehow "accuracy on benchmarks" tracks "ability to understand language" -- this, as we both think, is mad.
I guess my cultural issue is better described then as "pureish mathematicians" having come to own an empirical field, with all the predicatable nonesenses which follow (whether they are discrete or continous).
>> (...) the older generation of csci academics and much more aligned with my view).
That's true and hardbreaking. I don't know what we can do about that. The success of big tech corporations has broken computer science education, I'm afraid, and now all that graduates care about is how to make a six-figure salary at a FAANG.
I think the guilty party is whoever chose the label "neural" to describe high dimensional regression.
I don't see a rebuttal in your reply. NNs can make for good classifiers but they certainly get overused trying to make predictions in spaces that are not predictable.
This is like calling calculus useless, because it doesn't help you pick one kind of milk versus another in a supermarket.
Universal function approximation is a great tool if used correctly. From what I understand, it comes in handy when our puny human brain can't come up with an algorithm to produce such function. Let's stick to image recognition/processing. Can you write a function that recogizes a cat or a dog on a 64x64 greyscale image? Or write a function to remove the background from a picture of a person. Can you write a function to generate a 3D depth map for a 2D picture?
> These theorems only show that NNs are, like many computational statistical techniques, sufficient for a wide class of "mere approximation" problems that are only narrowly useful within the whole field of learning-as-such.
That's exactly what people are looking for in most cases, the mere approximation can be good enough to consider the problem solved.