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Depends on what you mean by "learn the rules, facts, and equations of science". I mean, a language model could memorise it and retell it in poetry if you wanted

Depends on what you mean by "learn the rules, facts, and equations of science". I mean, a language model could memorise it and retell it in poetry if you wanted. On the other hand it's not just a parrot, as some have claimed. AI is useful in accelerating progress in hard scientific problems. For one - a neural network is a universal function approximator. That's got to be really useful in many places where an approximation is good enough.

mdp2021 4y ago
I used in this pages the expression "parroting" for some AI implementations. Let us disambiguate. It is called an Artificial Intelligence that engineering product that can replace a Natural Intelligence in finding solutions - such as, through the automated development of sophisticated function approximators. This is one thing, and akin to the content of the article. And it is one meaning of the term "intelligence" ("intelligence₂"). On the other hand, I have seen algorithms that appear to collect associations by frequency being called Intelligence, in a very different sense of the term, and to that I note: -- that Intelligence₁ is an ontology developer - in my terms; it "investigates what things are". In the very recent terms of James Fodor, which I present here because it is a different formulation by coincidence just published today, «humans learn by making structured mental concepts, in which many different properties and associations are linked together» [actually the crucial part is in /how/ the concepts are refined]; -- and that without operation of "critical reflection" crucial in the ontology development, and instead with a "tendency" to just trust the input and solve conflict without foundational considerations, an inputs-imitating system, which may be said "based on parroting", is the opposite of Intelligence₁ .
- visarga 4y ago
  Very few people are capable of "investigating what things are" with "critical reflection", usually trained professionals in their field of expertise. Most people think by correlation and adjust based on the feedback. I think with the right feedback an AI could get by with parroting and correlations, and parroting becomes critical reflexion at some threshold of complexity.
  - AnimalMuppet 4y ago
    > ... parroting becomes critical reflexion at some threshold of complexity. Strong disagree. ... but I'm having a hard time seeing any way to prove that I'm right. I think it may be that my working definition of "reflection" differs from yours.
  - mdp2021 4y ago
    The topic was Intelligence. If we are talking about dancing, or scuba dive, it does not matter if most people just walk, or do not scuba dive. In fact, what the type in generic population does is more than irrelevant to what specific population does. And there is no direct port from description to prescription. And by the way, explicit critical thinking is expected to be, as it was in some regions, taught in primary school, e.g. behaviourally when children are taught that their peers being just six-years-old, they must be wary in their interaction, using their own judgement if only for safety; e.g. scholarily when they are not just told that '1+1=2', but made see that two items make a couple (and only then made learn menmonics like tables). The «field of expertise» of these "«trained professionals»" is just daily mind operation in general. Of course, implicit critical thinking is just the operation of conceptual revision that would anyway go on unconsciously. And it does: people come up with "ideas". As Prof. Patrick Winston insisted, "«adjustment by feedback»" is virtual, implicit or explicit mental experiments. You do not need to jump out of that window to know something about the consequences. Any entity that repeats a prejudice - which is a clearly consequential and even studied effect of parroting - without "looking into it", internally discussing it, is unintelligent in the defined sense. If you have an idea that some phenomenon can be emergent from some mechanism, there will have to be foundational considerations to be proposed for it.
    - visarga 4y ago
      The magic is this: more is different. [1] Parrots just repeat simple phrases, these language models recombine words to fit new contexts in ways that make sense. They memorise facts and common sense knowledge and use it to successfully solve hundreds of language based tasks. They are problem solvers, not parrots. This term was invented by an anti language model activist to discredit the big language model approach. Fortunately people still forge ahead with research and we get the likes of GPT-3, PaLM and DALL-E. [1] Sutton's "Bitter Lesson" - the importance of scale in AI models. Even AI researchers don't like to accept this fact. No wonder people are incredulous. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html Richard Sutton is one of the top AI researchers with 115,498 paper citations. https://en.wikipedia.org/wiki/Richard_S._Sutton https://en.wikipedia.org/wiki/Richard_S._Sutton
YeGoblynQueenne 4y ago
>> For one - a neural network is a universal function approximator. And Magic: the Gathering is Turing-Complete [1], but we are not communicating over an implementation of the http protocol in Magic: the Gathering. Because that would be awfully inconvenient. ______________ [1] https://arxiv.org/abs/1904.09828 https://arxiv.org/abs/1904.09828
credit_guy 4y ago
> For one - a neural network is a universal function approximator. Pretty much any way you turn you'll find classes of universal function approximators. The almost 200-year old Weierstrass-Stone theorem says that polynomials have this property. More prosaically, locally constant functions have this property (that's how the Riemann integral is defined). Trees and random forests exploit just this fact. Trigonometric polynomials have this property (via Fourier series). Wavelets. Now, here's a mind-blowing fact: did you know that addition and 1-d functions form such a class of universal approximators. It's called Kolmogorov-Arnold theorem [1], and it's the origin of the generalized additive models (GAM) in statistics. Back to neural networks: the universal approximation theorem is valid with a single layer. But nobody uses them with a single layer. Which means, people don't use NN's because of this (quite useless) property. Separately, this theorem is just an existence theorem, and it's not concerned with the complexity if the approximation. The approximation eventually relies in some form of optimization (see all the flavors of stochastic descent). But optimization, even convex optimization, is known to be NP-hard. So, these are all good reason to discard the universal approximation theorem when talking about neural networks. [1] https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Arnold_representation_theorem https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Arnold_repr...
- mdp2021 4y ago
  I think there is an ambiguity in the use of the expression. One is the mathematical property of a function to almost repeat the results of another. An somehow different one is conveying the idea that there are systems to "almost repeat the intended results" of a function unavailable to us - "you could build a well-defined function for that or approximate its results building a function approximator". It is more of a suggestive introductory idea and core concept.
  - YeGoblynQueenne 4y ago
    (Not the OP) Yes, that's right, but the problem with trying to approximate a function you don't know is that you don't know how well you've approximated it. You're left having to, well, approximate, the accuracy of the approximation. That's why everyone in machine learning trains their models and then tests them on a held-out test set. In theory, the error on the test set should predict the error on the true distribution (and that's under PAC-Learning assumptions). Unfortunately the way this is done in practice is sloppy and uninformative. For example, researchers usually have access to the test set during training, they split the training/test set to 80/20% so that the error calculated on the test set does not generalise well to truly unseen data, etc. Bottom line: all this approximation stuff is great in theory, but not so good in practice. Or, rather, in theory machine learning systems have almost perfect accuracies, in practice we have no idea how good they are.
shreyshnaccount 4y ago
"universal function approximator" is probably the most useless and actively harmful phrase that's taught by courses "teaching AI" when all they really do is import sklearn. A universal function approximator doesn't guarantee how long you'll have to train a net, or if you'll get good results, just that's it's possible to show any function as a series of mat mults and non linearities. Could say the same about polynomials, or even adding terms of sines and cosines like the Fourier series. This is a load of BS that needs to be removed from beginner courses

mdp2021 4y ago

I used in this pages the expression "parroting" for some AI implementations. Let us disambiguate. It is called an Artificial Intelligence that engineering product that can replace a Natural Intelligence in finding solutions - such as, through the automated development of sophisticated function approximators. This is one thing, and akin to the content of the article. And it is one meaning of the term "intelligence" ("intelligence₂"). On the other hand, I have seen algorithms that appear to collect associations by frequency being called Intelligence, in a very different sense of the term, and to that I note: -- that Intelligence₁ is an ontology developer - in my terms; it "investigates what things are". In the very recent terms of James Fodor, which I present here because it is a different formulation by coincidence just published today, «humans learn by making structured mental concepts, in which many different properties and associations are linked together» [actually the crucial part is in /how/ the concepts are refined]; -- and that without operation of "critical reflection" crucial in the ontology development, and instead with a "tendency" to just trust the input and solve conflict without foundational considerations, an inputs-imitating system, which may be said "based on parroting", is the opposite of Intelligence₁ .

visarga 4y ago
Very few people are capable of "investigating what things are" with "critical reflection", usually trained professionals in their field of expertise. Most people think by correlation and adjust based on the feedback. I think with the right feedback an AI could get by with parroting and correlations, and parroting becomes critical reflexion at some threshold of complexity.
- AnimalMuppet 4y ago
  > ... parroting becomes critical reflexion at some threshold of complexity. Strong disagree. ... but I'm having a hard time seeing any way to prove that I'm right. I think it may be that my working definition of "reflection" differs from yours.
- mdp2021 4y ago
  The topic was Intelligence. If we are talking about dancing, or scuba dive, it does not matter if most people just walk, or do not scuba dive. In fact, what the type in generic population does is more than irrelevant to what specific population does. And there is no direct port from description to prescription. And by the way, explicit critical thinking is expected to be, as it was in some regions, taught in primary school, e.g. behaviourally when children are taught that their peers being just six-years-old, they must be wary in their interaction, using their own judgement if only for safety; e.g. scholarily when they are not just told that '1+1=2', but made see that two items make a couple (and only then made learn menmonics like tables). The «field of expertise» of these "«trained professionals»" is just daily mind operation in general. Of course, implicit critical thinking is just the operation of conceptual revision that would anyway go on unconsciously. And it does: people come up with "ideas". As Prof. Patrick Winston insisted, "«adjustment by feedback»" is virtual, implicit or explicit mental experiments. You do not need to jump out of that window to know something about the consequences. Any entity that repeats a prejudice - which is a clearly consequential and even studied effect of parroting - without "looking into it", internally discussing it, is unintelligent in the defined sense. If you have an idea that some phenomenon can be emergent from some mechanism, there will have to be foundational considerations to be proposed for it.
  - visarga 4y ago
    The magic is this: more is different. [1] Parrots just repeat simple phrases, these language models recombine words to fit new contexts in ways that make sense. They memorise facts and common sense knowledge and use it to successfully solve hundreds of language based tasks. They are problem solvers, not parrots. This term was invented by an anti language model activist to discredit the big language model approach. Fortunately people still forge ahead with research and we get the likes of GPT-3, PaLM and DALL-E. [1] Sutton's "Bitter Lesson" - the importance of scale in AI models. Even AI researchers don't like to accept this fact. No wonder people are incredulous. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html Richard Sutton is one of the top AI researchers with 115,498 paper citations. https://en.wikipedia.org/wiki/Richard_S._Sutton https://en.wikipedia.org/wiki/Richard_S._Sutton

YeGoblynQueenne 4y ago

>> For one - a neural network is a universal function approximator. And Magic: the Gathering is Turing-Complete [1], but we are not communicating over an implementation of the http protocol in Magic: the Gathering. Because that would be awfully inconvenient. ______________ [1] https://arxiv.org/abs/1904.09828 https://arxiv.org/abs/1904.09828

credit_guy 4y ago

> For one - a neural network is a universal function approximator. Pretty much any way you turn you'll find classes of universal function approximators. The almost 200-year old Weierstrass-Stone theorem says that polynomials have this property. More prosaically, locally constant functions have this property (that's how the Riemann integral is defined). Trees and random forests exploit just this fact. Trigonometric polynomials have this property (via Fourier series). Wavelets. Now, here's a mind-blowing fact: did you know that addition and 1-d functions form such a class of universal approximators. It's called Kolmogorov-Arnold theorem [1], and it's the origin of the generalized additive models (GAM) in statistics. Back to neural networks: the universal approximation theorem is valid with a single layer. But nobody uses them with a single layer. Which means, people don't use NN's because of this (quite useless) property. Separately, this theorem is just an existence theorem, and it's not concerned with the complexity if the approximation. The approximation eventually relies in some form of optimization (see all the flavors of stochastic descent). But optimization, even convex optimization, is known to be NP-hard. So, these are all good reason to discard the universal approximation theorem when talking about neural networks. [1] https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Arnold_representation_theorem https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Arnold_repr...

mdp2021 4y ago
I think there is an ambiguity in the use of the expression. One is the mathematical property of a function to almost repeat the results of another. An somehow different one is conveying the idea that there are systems to "almost repeat the intended results" of a function unavailable to us - "you could build a well-defined function for that or approximate its results building a function approximator". It is more of a suggestive introductory idea and core concept.
- YeGoblynQueenne 4y ago
  (Not the OP) Yes, that's right, but the problem with trying to approximate a function you don't know is that you don't know how well you've approximated it. You're left having to, well, approximate, the accuracy of the approximation. That's why everyone in machine learning trains their models and then tests them on a held-out test set. In theory, the error on the test set should predict the error on the true distribution (and that's under PAC-Learning assumptions). Unfortunately the way this is done in practice is sloppy and uninformative. For example, researchers usually have access to the test set during training, they split the training/test set to 80/20% so that the error calculated on the test set does not generalise well to truly unseen data, etc. Bottom line: all this approximation stuff is great in theory, but not so good in practice. Or, rather, in theory machine learning systems have almost perfect accuracies, in practice we have no idea how good they are.

shreyshnaccount 4y ago

"universal function approximator" is probably the most useless and actively harmful phrase that's taught by courses "teaching AI" when all they really do is import sklearn. A universal function approximator doesn't guarantee how long you'll have to train a net, or if you'll get good results, just that's it's possible to show any function as a series of mat mults and non linearities. Could say the same about polynomials, or even adding terms of sines and cosines like the Fourier series. This is a load of BS that needs to be removed from beginner courses