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I'm missing how you own unsupported claim in any way refutes the claim in the article that you assert to be bs. I have in fact been involved in projects that d

I'm missing how you own unsupported claim in any way refutes the claim in the article that you assert to be bs. I have in fact been involved in projects that did exactly what you claim is BS - read a large corpus of scientific papers (and written medical records), and discerned connections worth further scientific investigation that had not been detected by the body of scientists following the field, ultimately leading to new drug discovery science.

AnimalMuppet 4y ago
"Worth further scientific investigation" is one thing. But mateo1 called BS on the claim that AI could "learn the fundamental rules, facts, and equations of science" by reading the literature, which is a different claim. You claim that the AI can find patterns and correlations; mateo1 disputes that the AI can actually learn the rules of the domain from the papers. So while you are right that mateo1 also did not prove his claim, your experience doesn't disprove his claim.
- visarga 4y ago
  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.
  - 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
- walnutclosefarm 4y ago
  I don't agree. The specific techniques we used (a variant of the transformer architecture behind most large language models) means, among other things, that you can ask the model questions, and get answers about what the model "knows" in return. So, if you train a model on thousands of scientific papers that contain information about drug effects on biomarkers, side effects, and the like, the model will learn, e.g., that drug A is typically used to treat cancer of type beta, and that it is frequently associated with lowering of biomarker X. It can then answer questions such as "what drugs are useful for lowering biomarker X in patients?" It can further answer a question "what mechanisms could be associated with this effect?" Of course, it can only answer with mechanisms that are identified in the literature it analyzed - it's not a PhD biochemist / M.D. able to infer or speculate on mechanism from first biochemical and medical principles. But there is no way to interpret what it can do, in my mind, other than to say that the model has learned drug biochemistry at some significant level, and can represent that learning to a user. In the case of what Deep Mind has done with protein folding, I don't know how you interpret its capabilities other than to say their model has learned much of the fundamental body of rules and facts that govern how amino acid sequences role up into proteins. There is a very real sense in which it embodies a theory of protein folding, much as say, Maxwell's equations and a knowledge of calculus embodies a theory of electromagnetism. That it cannot as yet articulate the rules of that theory is clearly a profound limitation, but it's a limitation of conveyance, not of utility.
  - AnimalMuppet 4y ago
    Did Deep Mind get that from reading a bunch of papers, though? Or did it get it from having those rules and facts programmed into it? (The original claim was about the ability of AIs to extract the rules from the literature.) But your last sentence... if it had the rules programmed in, shouldn't it have also programmed in the ability to name the rules? If it learned them from reading papers, shouldn't it know the terms from the papers? Or is the problem that it doesn't have at all the ability to trace out the chain it used to reach the conclusion? Is the problem not so much expressing the chain as much as it is knowing the chain?
    - 4y ago

AnimalMuppet 4y ago

"Worth further scientific investigation" is one thing. But mateo1 called BS on the claim that AI could "learn the fundamental rules, facts, and equations of science" by reading the literature, which is a different claim. You claim that the AI can find patterns and correlations; mateo1 disputes that the AI can actually learn the rules of the domain from the papers. So while you are right that mateo1 also did not prove his claim, your experience doesn't disprove his claim.

visarga 4y ago
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.
- 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
walnutclosefarm 4y ago
I don't agree. The specific techniques we used (a variant of the transformer architecture behind most large language models) means, among other things, that you can ask the model questions, and get answers about what the model "knows" in return. So, if you train a model on thousands of scientific papers that contain information about drug effects on biomarkers, side effects, and the like, the model will learn, e.g., that drug A is typically used to treat cancer of type beta, and that it is frequently associated with lowering of biomarker X. It can then answer questions such as "what drugs are useful for lowering biomarker X in patients?" It can further answer a question "what mechanisms could be associated with this effect?" Of course, it can only answer with mechanisms that are identified in the literature it analyzed - it's not a PhD biochemist / M.D. able to infer or speculate on mechanism from first biochemical and medical principles. But there is no way to interpret what it can do, in my mind, other than to say that the model has learned drug biochemistry at some significant level, and can represent that learning to a user. In the case of what Deep Mind has done with protein folding, I don't know how you interpret its capabilities other than to say their model has learned much of the fundamental body of rules and facts that govern how amino acid sequences role up into proteins. There is a very real sense in which it embodies a theory of protein folding, much as say, Maxwell's equations and a knowledge of calculus embodies a theory of electromagnetism. That it cannot as yet articulate the rules of that theory is clearly a profound limitation, but it's a limitation of conveyance, not of utility.
- AnimalMuppet 4y ago
  Did Deep Mind get that from reading a bunch of papers, though? Or did it get it from having those rules and facts programmed into it? (The original claim was about the ability of AIs to extract the rules from the literature.) But your last sentence... if it had the rules programmed in, shouldn't it have also programmed in the ability to name the rules? If it learned them from reading papers, shouldn't it know the terms from the papers? Or is the problem that it doesn't have at all the ability to trace out the chain it used to reach the conclusion? Is the problem not so much expressing the chain as much as it is knowing the chain?
  - 4y ago