5 ms·

... and still so small and slow compared to a human brain.

by sylware 3y ago

mschuster91 3y ago
Give development a few years and it'll be faster than a human.
- jacquesm 3y ago
  Depending on the task it may well already be faster than a human at equivalent performance.
- MrBuddyCasino 3y ago
  Neurons are very complex and not just on/off switches. We can't even fully simulate the ~300 neurons of c. elegans, not even close.
  - TeMPOraL 3y ago
    A lot of that complexity comes from them being living cells, optimized for and functioning in a different environment than our silicon-based machines. We don't need to model it all. (Though we do need to pay attention to evolution cheating by overfitting relative to what we'd consider a clean design. Some of the complexity may be doing double duty.)
    - MrBuddyCasino 3y ago
      > Some of the complexity may be doing double duty. Since we have not succeeded in imitating even the most primitive brains, even though computationally we should have enough juice by now, it would seem that complexity can't be discarded at all, no?
      - TeMPOraL 3y ago
        > Since we have not succeeded in imitating even the most primitive brains, even though computationally we should have enough juice by now looks at the browser tab with GPT-4 in it looks back again here ... we didn't?
        AstralStorm 3y ago
        We didn't. Try to get ChatGPT through a maze a C. Elegans can solve in minutes. (That slow mostly because pond scum moves slowly.) Seriously, get it to successfully play through a text adventure maze game.
        mschuster91 3y ago
        The problem with ChatGPT is it lacks the context depth for more complex tasks - but that is a computation resource limit, not a technical one.
        TeMPOraL 3y ago
        > Seriously, get it to successfully play through a text adventure maze game. I did exactly that the other day in response to a different objection on a HN thread. Or at least similar enough. https://cloud.typingmind.com/share/c0a68cb2-5f59-4e83-b383-bcd7aad497f2 https://cloud.typingmind.com/share/c0a68cb2-5f59-4e83-b383-b... Now, the goal there wasn't to get it to solve a maze, but rather to see how it can come up with a plan of action and adjust it on the fly. But I see no reason a variant of that wouldn't work with a traditional maze game - provided you remember this is a stateless model without volatile memory, so it needs to be fed its memory with every request.
        MrBuddyCasino 3y ago
        No we haven't. ChatGPT is basically a sophisticated Markov chain. It is very good at pattern matching, but it has no understanding of anything, or its own will. People who think is even close to AGI are deluded, fooled by an elaborate Mechanical Turk. This is also the reason why its output sounds convincing, but is very often factually wrong.
    - mrguyorama 3y ago
      Slime molds and single celled creatures can learn things despite having ZERO neurons. Neurons are built on top of an already incredibly complex machine evaluating hundreds of thousands of chemical and physical interactions per second that ALL effect how the cell works. We aren't likely ever going to reduce that to a model as simple as the one used in machine learning, because it probably isn't that simple period. Neurons are not "just" electrical signalling devices. They are complicated processors and systems in their own right.
KeplerBoy 3y ago
I don't know about you guys but computers have always been faster at math than my brain.
- hardware2win 3y ago
  How about learning e.g car driving?
  - firecall 3y ago
    It's just a matter of knowing how to teach it...
    - ed25519FUUU 3y ago
      Why not have it teach itself if it’s so smart!
  - echelon 3y ago
    It took humans 300,000 years to learn to drive the car. Much longer than that, if you consider our lineage.
    - hardware2win 3y ago
      >It took humans 300,000 years to learn to drive the car. False, to invent car. People manage to learn to operate cars in less than 50 hours
      - detrites 3y ago
        You're comparing training a model from scratch in ML, to the equivalent of model fine-tuning in humans. It's unfair and incorrect. Eg, no human can drive without first learning to operate their limbs, recognise shapes etc. The GP is pointing out that training in fine muscle motor skills, self-awareness and ability to project self-awareness to other objects under ones control etc, all took many thousands of years to develop. AI is faster. However, it's again unfair, as AI only knows what it knows from us, so in that sense any comparison is built on shaky ground. But for the purposes of comparing a stock human brain as hardware, versus a current high-end GPU specifically in terms of ingesting information and then perform tasks, the GPU beats the human brain "hands-down" in any category. The only categories it doesn't are simply ones that no one has trained it to yet - so the argument on a pure hardware capability basis stands.

... and still so small and slow compared to a human brain.

mschuster91 3y ago

Give development a few years and it'll be faster than a human.

jacquesm 3y ago
Depending on the task it may well already be faster than a human at equivalent performance.
MrBuddyCasino 3y ago
Neurons are very complex and not just on/off switches. We can't even fully simulate the ~300 neurons of c. elegans, not even close.
- TeMPOraL 3y ago
  A lot of that complexity comes from them being living cells, optimized for and functioning in a different environment than our silicon-based machines. We don't need to model it all. (Though we do need to pay attention to evolution cheating by overfitting relative to what we'd consider a clean design. Some of the complexity may be doing double duty.)
  - MrBuddyCasino 3y ago
    > Some of the complexity may be doing double duty. Since we have not succeeded in imitating even the most primitive brains, even though computationally we should have enough juice by now, it would seem that complexity can't be discarded at all, no?
    - TeMPOraL 3y ago
      > Since we have not succeeded in imitating even the most primitive brains, even though computationally we should have enough juice by now looks at the browser tab with GPT-4 in it looks back again here ... we didn't?
      - AstralStorm 3y ago
        We didn't. Try to get ChatGPT through a maze a C. Elegans can solve in minutes. (That slow mostly because pond scum moves slowly.) Seriously, get it to successfully play through a text adventure maze game.
        mschuster91 3y ago
        The problem with ChatGPT is it lacks the context depth for more complex tasks - but that is a computation resource limit, not a technical one.
        TeMPOraL 3y ago
        > Seriously, get it to successfully play through a text adventure maze game. I did exactly that the other day in response to a different objection on a HN thread. Or at least similar enough. https://cloud.typingmind.com/share/c0a68cb2-5f59-4e83-b383-bcd7aad497f2 https://cloud.typingmind.com/share/c0a68cb2-5f59-4e83-b383-b... Now, the goal there wasn't to get it to solve a maze, but rather to see how it can come up with a plan of action and adjust it on the fly. But I see no reason a variant of that wouldn't work with a traditional maze game - provided you remember this is a stateless model without volatile memory, so it needs to be fed its memory with every request.
      - MrBuddyCasino 3y ago
        No we haven't. ChatGPT is basically a sophisticated Markov chain. It is very good at pattern matching, but it has no understanding of anything, or its own will. People who think is even close to AGI are deluded, fooled by an elaborate Mechanical Turk. This is also the reason why its output sounds convincing, but is very often factually wrong.
  - mrguyorama 3y ago
    Slime molds and single celled creatures can learn things despite having ZERO neurons. Neurons are built on top of an already incredibly complex machine evaluating hundreds of thousands of chemical and physical interactions per second that ALL effect how the cell works. We aren't likely ever going to reduce that to a model as simple as the one used in machine learning, because it probably isn't that simple period. Neurons are not "just" electrical signalling devices. They are complicated processors and systems in their own right.

KeplerBoy 3y ago

I don't know about you guys but computers have always been faster at math than my brain.

hardware2win 3y ago
How about learning e.g car driving?
- firecall 3y ago
  It's just a matter of knowing how to teach it...
  - ed25519FUUU 3y ago
    Why not have it teach itself if it’s so smart!
- echelon 3y ago
  It took humans 300,000 years to learn to drive the car. Much longer than that, if you consider our lineage.
  - hardware2win 3y ago
    >It took humans 300,000 years to learn to drive the car. False, to invent car. People manage to learn to operate cars in less than 50 hours
    - detrites 3y ago
      You're comparing training a model from scratch in ML, to the equivalent of model fine-tuning in humans. It's unfair and incorrect. Eg, no human can drive without first learning to operate their limbs, recognise shapes etc. The GP is pointing out that training in fine muscle motor skills, self-awareness and ability to project self-awareness to other objects under ones control etc, all took many thousands of years to develop. AI is faster. However, it's again unfair, as AI only knows what it knows from us, so in that sense any comparison is built on shaky ground. But for the purposes of comparing a stock human brain as hardware, versus a current high-end GPU specifically in terms of ingesting information and then perform tasks, the GPU beats the human brain "hands-down" in any category. The only categories it doesn't are simply ones that no one has trained it to yet - so the argument on a pure hardware capability basis stands.