4 ms·

F1 drivers can instinctively respond to a minor bump on a curve and make a decision in probably ~100 milliseconds, so the lower bound is just 10 computational s

F1 drivers can instinctively respond to a minor bump on a curve and make a decision in probably ~100 milliseconds, so the lower bound is just 10 computational steps. A more complex decision such as swerving to avoid a wheel that just came off the car in front, probably takes around ~500 milliseconds, or 50 computational steps. Considering that modern computers need billions of computational steps to start a calculator app and draw it on the desktop, we have a long way to go. Though there has been regression on this point, a 1990 NeXTStation Color could do the same with ~50 million cycles on a 1120×832 display. (on 12 MB of RAM and 1.5 MB of VRAM.)

TeMPOraL 4y ago
I think the trick is, our computational steps are massively parallel. Take the visual system. By the time you notice - consciously - the pothole you're about to drive into, it's already processed into high-level concepts. Our visual senses aren't camera feeds - the image we are aware of isn't raw input from the eyes, but rather it's generated out of some combination of raw inputs, low-level hacks to hide limited FOV and discontinuities caused by saccades, and higher-level interpretations based on how we feel, what seems to fit the context based on our prior experiences, etc. I think it makes sense to assume that this process has many extra off-ramps that can trigger high-level reactions while bypassing conscious awareness. I.e. imagine that half-way through the post-processing stack, the relevant parts of the brain are somewhat convinced you're looking at a fast-moving object about to hit you in the face. There's a point at which it makes sense for the brain to make your body start a dodging move, even if it may be a misprediction, rather than waste a few dozen milliseconds to make sure, at the risk of getting a ball (or a rock, or a fist) up your nose.
- marcosdumay 4y ago
  > I think the trick is, our computational steps are massively parallel. Yes, one trick is that. Our computers have to serialize everything. We don't know how to build or program massively parallel computers with distributed memory. But the GP already touched on that point.
  - MichaelZuo 4y ago
    Well it seems like Nvidia is taking some baby steps in that direction with the 'Grace Hopper Superchip'.
- daveguy 4y ago
  This is an extremely important point. "10 computational steps" is obviously underestimated. Each neuron can communicate with 10s to 100s of additional neurons, so if you're talking about 10 ms per step 100ms being 10 steps you are missing the fact that each step can be an exponential increase in the amount of computation involved. This is one of the main reasons I believe we are nowhere near human or even mouse level AGI. Many of the simple neuron count metrics do not include the synapse counts or the incredible amount of parallel information that is processed. We have network interfaces with 100-400 Gb throughput whereas the communications between synapses represents exabits of throughput between computational units. We are very far from AGI just on a fundamental processing scale. Not to mention we know very little about the algorithms involved.
  - TeMPOraL 4y ago
    I would almost agree with you on the AGI angle, but my beliefs are still recovering from the shock of how we kind of randomly stumbled into solving multiple classes of seemingly intractable cognitive problems, just by feeding a program half the Internet and letting it position pieces of words in a hundred thousand dimensional vector space. Human brain has to do a lot of work processing and integrating all the senses we use, which could cover at least for part of the neural capacity you mention, but I feel that where thinking is concerned, we may have accidentally solved the core problems. I somewhat expect that we'll look back and see that current LLMs were to human minds what the ol' Model T is to Tesla Model S - vastly different in levels of advancement, but also fundamentally the same thing.
    - daveguy 4y ago
      > the shock of how we kind of randomly stumbled into solving multiple classes of seemingly intractable cognitive problems... Another excellent point. The idea that we may stumble onto massive computational leaps based on imprecise data processing requirements is terrifying. I do think we are nowhere near where we will need to be with respect to computation and, more important, information throughput. But the fact that we "oops"-ed into such a strong statistical model of conversation is deeply unsettling. I do believe GPT based language models will be part of an AGI.
taeric 4y ago
That feels like an odd example. Going on a google for speeds, that means they are responding to a bump after they have traveled 30 feet past it. Impressive, to be sure, but I'm not clear what the learning is there. More engineering went into the vehicles so that, by design, many other things happened before the driver is aware of it.

TeMPOraL 4y ago

I think the trick is, our computational steps are massively parallel. Take the visual system. By the time you notice - consciously - the pothole you're about to drive into, it's already processed into high-level concepts. Our visual senses aren't camera feeds - the image we are aware of isn't raw input from the eyes, but rather it's generated out of some combination of raw inputs, low-level hacks to hide limited FOV and discontinuities caused by saccades, and higher-level interpretations based on how we feel, what seems to fit the context based on our prior experiences, etc. I think it makes sense to assume that this process has many extra off-ramps that can trigger high-level reactions while bypassing conscious awareness. I.e. imagine that half-way through the post-processing stack, the relevant parts of the brain are somewhat convinced you're looking at a fast-moving object about to hit you in the face. There's a point at which it makes sense for the brain to make your body start a dodging move, even if it may be a misprediction, rather than waste a few dozen milliseconds to make sure, at the risk of getting a ball (or a rock, or a fist) up your nose.

marcosdumay 4y ago
> I think the trick is, our computational steps are massively parallel. Yes, one trick is that. Our computers have to serialize everything. We don't know how to build or program massively parallel computers with distributed memory. But the GP already touched on that point.
- MichaelZuo 4y ago
  Well it seems like Nvidia is taking some baby steps in that direction with the 'Grace Hopper Superchip'.
daveguy 4y ago
This is an extremely important point. "10 computational steps" is obviously underestimated. Each neuron can communicate with 10s to 100s of additional neurons, so if you're talking about 10 ms per step 100ms being 10 steps you are missing the fact that each step can be an exponential increase in the amount of computation involved. This is one of the main reasons I believe we are nowhere near human or even mouse level AGI. Many of the simple neuron count metrics do not include the synapse counts or the incredible amount of parallel information that is processed. We have network interfaces with 100-400 Gb throughput whereas the communications between synapses represents exabits of throughput between computational units. We are very far from AGI just on a fundamental processing scale. Not to mention we know very little about the algorithms involved.
- TeMPOraL 4y ago
  I would almost agree with you on the AGI angle, but my beliefs are still recovering from the shock of how we kind of randomly stumbled into solving multiple classes of seemingly intractable cognitive problems, just by feeding a program half the Internet and letting it position pieces of words in a hundred thousand dimensional vector space. Human brain has to do a lot of work processing and integrating all the senses we use, which could cover at least for part of the neural capacity you mention, but I feel that where thinking is concerned, we may have accidentally solved the core problems. I somewhat expect that we'll look back and see that current LLMs were to human minds what the ol' Model T is to Tesla Model S - vastly different in levels of advancement, but also fundamentally the same thing.
  - daveguy 4y ago
    > the shock of how we kind of randomly stumbled into solving multiple classes of seemingly intractable cognitive problems... Another excellent point. The idea that we may stumble onto massive computational leaps based on imprecise data processing requirements is terrifying. I do think we are nowhere near where we will need to be with respect to computation and, more important, information throughput. But the fact that we "oops"-ed into such a strong statistical model of conversation is deeply unsettling. I do believe GPT based language models will be part of an AGI.

taeric 4y ago

That feels like an odd example. Going on a google for speeds, that means they are responding to a bump after they have traveled 30 feet past it. Impressive, to be sure, but I'm not clear what the learning is there. More engineering went into the vehicles so that, by design, many other things happened before the driver is aware of it.