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I am responding to Hinton's transparently false claim that LLMs know more than we do despite having less synapses. This is only true if you ignore non-textual i

I am responding to Hinton's transparently false claim that LLMs know more than we do despite having less synapses. This is only true if you ignore non-textual information in the human brain. This is stupid and dishonest. Really not sure what your point is, it has nothing to do with Hinton's claim, and nothing to do with my comment.

williamcotton 2y ago
An LLM like ChatGPT “knows” more than either than us on a very large amount of topics. Not human knowledge, but some kind of knowledge. And there’s no reason why the concept of intuition is limited to humans. Again, not human intuition. Our artificial constructs are not going to be completely like their biological analogs but they can indeed express certain qualities and have certain shared abilities. Did we watch the same clip of Hinton?
- inference-lord 2y ago
  I recently saw Hinton give a talk where he very, very, very excitedly and confidently gave us an example to demonstrate how incredibly intelligent and creative LLMS are. He asked an LLM a question, but he didn't give us the answer. He let us have time to answer it ourselves. Personally I knew the answer instantly. He gave us the answer and sort of assumed no one would've known the answer and then used it as as justification for how smart these systems were. It honestly didn't feel very reassuring to me and honestly, I'd be surprised if it wasn't a topic covered somewhere on the internet before. With all due respect to Mr Hinton, I felt it showed his age a bit honestly. What is difficult about Hinton's statements is that he can't really give evidence to back up these sort of claims. How do you measure how much a person knows, and how do you objectively measure how much an LLM knows? How smart is an LLM? You can't really know. It seems almost rhetorical. How many notes in a saxophone ? We can make observations but that's not a great way to measure anything precisely. There is a limit to language and I think this is one of those topics where that limit is touched or even breached.I don't even know if "intelligence" is a sufficient enough word to describe what's going on with these systems. It's the best word we have but it doesn't seem to adequately describe what we're observing.
  - williamcotton 2y ago
    How do you measure how much a person knows, and how do you objectively measure how much an LLM knows? Here’s a very basic example of where an LLM is clearly more capable than a human: language translation. I would bet $10k at 10:1 that there are no humans who can reliably translate to and from as many languages as an LLM can. It is very easy to measure knowledge: test the subject. Personally, I can’t ever imagine scoring higher on a general knowledge test than a contemporary LLM. Also, I don’t know of any humans that can run as fast as a car so I don’t know why any of this is surprising or farfetched.
    - inference-lord 2y ago
      I think you misinterpreted what I mean. I'm not saying that they can't be more capable, I'm saying the guy can get a little overly excited about things which are hard to measure or quantify. We're observing these systems and making up our own interpretations about how good they are at certain tasks, but it's not really easy to measure how much better or worse these things can be overall. Your example about language translation is a good example of where these things aren't really "better", just different. I speak multiple languages and while these systems are fantastic, they can fail in ways a professional translator wouldn't and it doesn't seem to automatically know it failed and should fix itself. The car example is also great because it again proves my point. We can easily measure a car and a person and workout a car is faster, but we can also see that a car can't walk. So it's faster but it;s also entirely different.
      - williamcotton 2y ago
        I'm saying the guy can get a little overly excited about things which are hard to measure or quantify. Let's back this up a little bit. We've got Marvin Minksy who comes along and destroys the perceptron. Then we have decades of knowledge systems that go nowhere. All the while Geoff Hinton is tirelessly working on neural networks. Finally after decades of hard work the fruits of his labor are recognized with ImageNet. And then a bunch of people in a comment section criticize the guy for getting "a little overly excited" about the stunning range of neural networks that validates his life's work. Great job, all around!

williamcotton 2y ago

An LLM like ChatGPT “knows” more than either than us on a very large amount of topics. Not human knowledge, but some kind of knowledge. And there’s no reason why the concept of intuition is limited to humans. Again, not human intuition. Our artificial constructs are not going to be completely like their biological analogs but they can indeed express certain qualities and have certain shared abilities. Did we watch the same clip of Hinton?

inference-lord 2y ago
I recently saw Hinton give a talk where he very, very, very excitedly and confidently gave us an example to demonstrate how incredibly intelligent and creative LLMS are. He asked an LLM a question, but he didn't give us the answer. He let us have time to answer it ourselves. Personally I knew the answer instantly. He gave us the answer and sort of assumed no one would've known the answer and then used it as as justification for how smart these systems were. It honestly didn't feel very reassuring to me and honestly, I'd be surprised if it wasn't a topic covered somewhere on the internet before. With all due respect to Mr Hinton, I felt it showed his age a bit honestly. What is difficult about Hinton's statements is that he can't really give evidence to back up these sort of claims. How do you measure how much a person knows, and how do you objectively measure how much an LLM knows? How smart is an LLM? You can't really know. It seems almost rhetorical. How many notes in a saxophone ? We can make observations but that's not a great way to measure anything precisely. There is a limit to language and I think this is one of those topics where that limit is touched or even breached.I don't even know if "intelligence" is a sufficient enough word to describe what's going on with these systems. It's the best word we have but it doesn't seem to adequately describe what we're observing.
- williamcotton 2y ago
  How do you measure how much a person knows, and how do you objectively measure how much an LLM knows? Here’s a very basic example of where an LLM is clearly more capable than a human: language translation. I would bet $10k at 10:1 that there are no humans who can reliably translate to and from as many languages as an LLM can. It is very easy to measure knowledge: test the subject. Personally, I can’t ever imagine scoring higher on a general knowledge test than a contemporary LLM. Also, I don’t know of any humans that can run as fast as a car so I don’t know why any of this is surprising or farfetched.
  - inference-lord 2y ago
    I think you misinterpreted what I mean. I'm not saying that they can't be more capable, I'm saying the guy can get a little overly excited about things which are hard to measure or quantify. We're observing these systems and making up our own interpretations about how good they are at certain tasks, but it's not really easy to measure how much better or worse these things can be overall. Your example about language translation is a good example of where these things aren't really "better", just different. I speak multiple languages and while these systems are fantastic, they can fail in ways a professional translator wouldn't and it doesn't seem to automatically know it failed and should fix itself. The car example is also great because it again proves my point. We can easily measure a car and a person and workout a car is faster, but we can also see that a car can't walk. So it's faster but it;s also entirely different.
    - williamcotton 2y ago
      I'm saying the guy can get a little overly excited about things which are hard to measure or quantify. Let's back this up a little bit. We've got Marvin Minksy who comes along and destroys the perceptron. Then we have decades of knowledge systems that go nowhere. All the while Geoff Hinton is tirelessly working on neural networks. Finally after decades of hard work the fruits of his labor are recognized with ImageNet. And then a bunch of people in a comment section criticize the guy for getting "a little overly excited" about the stunning range of neural networks that validates his life's work. Great job, all around!