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> I think some of the pushback you receive is because you're calling it a mistake when you yourself just admitted that you can't know if it's a mistake because

> I think some of the pushback you receive is because you're calling it a mistake when you yourself just admitted that you can't know if it's a mistake because of our incomplete knowledge of neurology. I'm not the one making the affirmative claim absent evidence. I'm the one pointing out the lack of evidence for the claim. If someone says "there are aliens on the far side of the moon," it's not a mistake to say "there is no evidence for your extraordinary claim". > On the other side of this, there are straightforward arguments that there is some deep connection here. No, there isn't. Humans can perform math. Computers can perform math. No one would claim that's evidence computers think like humans or vice versa. > they are in a real sense building a statistical model of how the human brain processes language. And there you go, making exactly the kind of claim I'm talking about. I'm going to make this very clear: there is absolutely no evidence that supports this claim. Period.

doctor_eval 3y ago
I agree with most of your points and I think there’s a good argument that CS should use different terms that don’t overlap with biology, but I don’t agree with this: > there is absolutely no evidence that supports this claim. Period The evidence is that it understands and can respond with human language which arose from purely biological processes. So until we understand how this happens we can’t say for sure that these things are not statistical models of part of the human brain. Maybe these models are picking up on Chomsky’s universal grammar or maybe they are emulating brains. We just don’t know. It might not be strong evidence, it might be indirect, but it is not a total and irrefutable absence of evidence.
- BaseballPhysics 3y ago
  > The evidence is that it understands and can respond with human language which arose from purely biological processes If you don't understand how this isn't evidence, I honestly don't know what to do. > So until we understand how this happens we can’t say for sure that these things are not statistical models of part of the human brain. I never said that. I said you can't affirmatively say they are, and further, that because we don't understand how the human brain works, the fact that we can make computers perform tasks that humans can is not evidence in favour of the idea that those computers are in some way modelling the way the human brain actually works. And that is the claim I've seen many people make. In fact that's the claim you just made. > We just don’t know. On that we agree.
- YeGoblynQueenne 3y ago
  >> The evidence is that it understands and can respond with human language which arose from purely biological processes. Well, human mathematical thinking, including logical thinking, also arose from purely biological processes (self-evidently) and yet we have machines that can reproduce all that: digital computers. Perhaps we should consider computers already artificial intelligence? I actually think that yes, totally, 100% we should. But that makes for a definition of artificial intelligence that will disappoint most people who hope for Star Trek like computer-friends.
naasking 3y ago
> Humans can perform math. Computers can perform math. No one would claim that's evidence computers think like humans or vice versa. But we would very sensibly claim that computers can think like humans when suitably programmed, and humans can compute like computers. And the claim here is that learning the relationships between words is an understanding of language, and natural language reflects certain kinds of human cognition, and mimicking that output from the same input is mimicking that cognition. > I'm going to make this very clear: there is absolutely no evidence that supports this claim. Period. Again, that's incorrect. In what other science could you produce a model that nearly 100% accurately reproduces what the system being modelled would generate, and people would insist on saying that that doesn't really model the operation of that system? Inconsistent standards of evidence IMO. In any case, There have been a few studies demonstrating strong correlations in activation patterns between the human brain and neural networks. These are correlations, but correlations are evidence. Furthermore, I think you're failing to understand the argument. Human languages were invented by humans. They are necessarily suited to the human mind, reflecting some fundamental structure and operation of the human brain. It would be a fairly dramatic coincidence if other, random formal systems were well suited to reproducing natural language. In fact, the most obvious inference is that LLMs are likely inferring semantic models that encapsulate how humans categorize and think, which is why LLMs can translate text between human languages. This would not be possible if languages did not have a common underlying semantic structure.
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- YeGoblynQueenne 3y ago
  >> Again, that's incorrect. In what other science could you produce a model that nearly 100% accurately reproduces what the system being modelled would generate, and people would insist on saying that that doesn't really model the operation of that system? Inconsistent standards of evidence IMO. As far as I'm concerned the problem with claims of great accuracy (100%? Surely you jest?) is that they go above and beyond the evidence provided by modelling. For example, take Convolutional Neural Nets used to train classifiers for objects in images. It is easy to agree that such systems model the human labelling of sets of pixels in digital images, but then this observation is taken as evidence that the systems in question are doing something more than that; namely, that they are somehow modelling human perception. You can't just take any evidence you have and use it to support any hypothesis you like, and then call that "science". That's not science, it's wishful thinking. If you want machine learning to be scientific you have to be rigorous. But I'm sure if you start talking about scientific rigour in ICML and NeurIPs people will just laugh at you. "Here", they'll say, "we got systems worth millions, what's rigour got to do with anything?". And they'll be right. Scientific rigour has never made anyone any millions.
ironborn123 3y ago
There is some evidence, though still tentative. https://www.nature.com/articles/s41562-022-01516-2 https://www.nature.com/articles/s41562-022-01516-2 (Evidence of a predictive coding hierarchy in the human brain listening to speech) i view this as the newtonian mechanics vs actual reality debate. the former may not be a very accurate model of the latter, but it is very useful and it would be wrong to say there is no similarity between them.

doctor_eval 3y ago

I agree with most of your points and I think there’s a good argument that CS should use different terms that don’t overlap with biology, but I don’t agree with this: > there is absolutely no evidence that supports this claim. Period The evidence is that it understands and can respond with human language which arose from purely biological processes. So until we understand how this happens we can’t say for sure that these things are not statistical models of part of the human brain. Maybe these models are picking up on Chomsky’s universal grammar or maybe they are emulating brains. We just don’t know. It might not be strong evidence, it might be indirect, but it is not a total and irrefutable absence of evidence.

BaseballPhysics 3y ago
> The evidence is that it understands and can respond with human language which arose from purely biological processes If you don't understand how this isn't evidence, I honestly don't know what to do. > So until we understand how this happens we can’t say for sure that these things are not statistical models of part of the human brain. I never said that. I said you can't affirmatively say they are, and further, that because we don't understand how the human brain works, the fact that we can make computers perform tasks that humans can is not evidence in favour of the idea that those computers are in some way modelling the way the human brain actually works. And that is the claim I've seen many people make. In fact that's the claim you just made. > We just don’t know. On that we agree.
YeGoblynQueenne 3y ago
>> The evidence is that it understands and can respond with human language which arose from purely biological processes. Well, human mathematical thinking, including logical thinking, also arose from purely biological processes (self-evidently) and yet we have machines that can reproduce all that: digital computers. Perhaps we should consider computers already artificial intelligence? I actually think that yes, totally, 100% we should. But that makes for a definition of artificial intelligence that will disappoint most people who hope for Star Trek like computer-friends.

naasking 3y ago

> Humans can perform math. Computers can perform math. No one would claim that's evidence computers think like humans or vice versa. But we would very sensibly claim that computers can think like humans when suitably programmed, and humans can compute like computers. And the claim here is that learning the relationships between words is an understanding of language, and natural language reflects certain kinds of human cognition, and mimicking that output from the same input is mimicking that cognition. > I'm going to make this very clear: there is absolutely no evidence that supports this claim. Period. Again, that's incorrect. In what other science could you produce a model that nearly 100% accurately reproduces what the system being modelled would generate, and people would insist on saying that that doesn't really model the operation of that system? Inconsistent standards of evidence IMO. In any case, There have been a few studies demonstrating strong correlations in activation patterns between the human brain and neural networks. These are correlations, but correlations are evidence. Furthermore, I think you're failing to understand the argument. Human languages were invented by humans. They are necessarily suited to the human mind, reflecting some fundamental structure and operation of the human brain. It would be a fairly dramatic coincidence if other, random formal systems were well suited to reproducing natural language. In fact, the most obvious inference is that LLMs are likely inferring semantic models that encapsulate how humans categorize and think, which is why LLMs can translate text between human languages. This would not be possible if languages did not have a common underlying semantic structure.

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YeGoblynQueenne 3y ago
>> Again, that's incorrect. In what other science could you produce a model that nearly 100% accurately reproduces what the system being modelled would generate, and people would insist on saying that that doesn't really model the operation of that system? Inconsistent standards of evidence IMO. As far as I'm concerned the problem with claims of great accuracy (100%? Surely you jest?) is that they go above and beyond the evidence provided by modelling. For example, take Convolutional Neural Nets used to train classifiers for objects in images. It is easy to agree that such systems model the human labelling of sets of pixels in digital images, but then this observation is taken as evidence that the systems in question are doing something more than that; namely, that they are somehow modelling human perception. You can't just take any evidence you have and use it to support any hypothesis you like, and then call that "science". That's not science, it's wishful thinking. If you want machine learning to be scientific you have to be rigorous. But I'm sure if you start talking about scientific rigour in ICML and NeurIPs people will just laugh at you. "Here", they'll say, "we got systems worth millions, what's rigour got to do with anything?". And they'll be right. Scientific rigour has never made anyone any millions.

ironborn123 3y ago

There is some evidence, though still tentative. https://www.nature.com/articles/s41562-022-01516-2 https://www.nature.com/articles/s41562-022-01516-2 (Evidence of a predictive coding hierarchy in the human brain listening to speech) i view this as the newtonian mechanics vs actual reality debate. the former may not be a very accurate model of the latter, but it is very useful and it would be wrong to say there is no similarity between them.