A teenager can learn to drive in a few dozen hours; self-driving systems are trained for years on billions of miles of data. …
Steven Byrnes appears to read the gap as evidence that current algorithms are far from what the brain is doing, such that much better algorithms must be waiting to be found.
I think you’re attributing an argument to me which I wasn’t making (in the context of that post that you copied the diagram from). I agree that comparing 30 hours of teen driving practice to umpteen gazillion hours of Waymo training data is apples-and-oranges because the teen also has life experience.
But I was making a different point, which (in my own words) was: “…we don’t have AGI (artificial general intelligence) yet—not as I use the term…”. (I’m not even sure you disagree with that??)
Anyway, it is NOT the case that it’s possible to make self-driving cars by taking some generic learning algorithm that we already know about, and letting it spend the equivalent of 18 years roaming around and doing stuff in various virtual environments like VR & MineCraft, and watching YouTube videos, and reading books, and whatever, and THEN have it spend 30 hours with minimal instruction driving actual cars, and bam, now you have a human-level self-driving car. There is no generic learning algorithm today that can do that, right? If there were such an algorithm, then surely somebody would have done that already. That would have been way way way easier than what Waymo and Tesla etc. have been actually doing. So I think this example is fair game: the brain can do things that no existing AI algorithm can do, even in an apples-to-apples comparison that holds data availability fixed.
Maybe your response would be: “Oh yeah that’s easy, someone could totally do that, it’s just that nobody has bothered because the resulting AI would be too big to fit in a car computer”?? Or “Oh yeah, we totally know how to do that, it’s just that it would require more compute than would be affordable or practical at the present time”?? If so, I disagree with both of those possible objections, and we can get into why if it’s crux-y.
I think you’re attributing an argument to me which I wasn’t making (in the context of that post that you copied the diagram from). I agree that comparing 30 hours of teen driving practice to umpteen gazillion hours of Waymo training data is apples-and-oranges because the teen also has life experience.
But I was making a different point, which (in my own words) was: “…we don’t have AGI (artificial general intelligence) yet—not as I use the term…”. (I’m not even sure you disagree with that??)
Yes, sorry, I can see how that might give people the wrong impression of your views. The diagram was just meant to illustrate the reading that the self-driving case points to a missing major algorithmic ingredient that makes humans very sample efficient learners. The later line (“Humans don’t learn to drive in thirty hours, they are fine-tuned on driving after a roughly two decade-long pretraining run”) was aimed at a more naive version of the argument someone might hold, not at you specifically.
I agree that we don’t have AGI yet, but given what current deep learning architectures/algorithms have already achieved, I don’t think that the ingredients will look too exotic or different from the types of algorithms we have now.
On the narrow self-driving car case, I find it plausible that a model that’s basically a significantly scaled up version of EZ-V2 (requiring >3 OOMs more memory than current Tesla FSD hardware at inference), and given maybe ~2 years of diverse real driving experience (and perhaps alongside a few non-exotic additional regularization and data augmentation techniques), could learn to drive as well as a human. Well, I’d put it at maybe 60% chance that this is true.
On why we can’t teach a model to drive the way a human would, I agree that we’re still missing a few algorithmic ideas, especially related to how to train a good world model for generalizable/transferable online learning and spatial navigation, but even if we did have the right algorithm, I disagree that it’d be “way way way easier” than what Waymo and Tesla have been doing, and I think “the resulting AI would be too big to fit in a car computer” is one major factor. Beyond the scale of the frozen policy itself though, I do expect that the algorithmic thing that is missing for human-like learning will require more compute of some form. Though I may just be lacking imagination about what alternative algorithms are possible.
I think you’re attributing an argument to me which I wasn’t making (in the context of that post that you copied the diagram from). I agree that comparing 30 hours of teen driving practice to umpteen gazillion hours of Waymo training data is apples-and-oranges because the teen also has life experience.
But I was making a different point, which (in my own words) was: “…we don’t have AGI (artificial general intelligence) yet—not as I use the term…”. (I’m not even sure you disagree with that??)
Anyway, it is NOT the case that it’s possible to make self-driving cars by taking some generic learning algorithm that we already know about, and letting it spend the equivalent of 18 years roaming around and doing stuff in various virtual environments like VR & MineCraft, and watching YouTube videos, and reading books, and whatever, and THEN have it spend 30 hours with minimal instruction driving actual cars, and bam, now you have a human-level self-driving car. There is no generic learning algorithm today that can do that, right? If there were such an algorithm, then surely somebody would have done that already. That would have been way way way easier than what Waymo and Tesla etc. have been actually doing. So I think this example is fair game: the brain can do things that no existing AI algorithm can do, even in an apples-to-apples comparison that holds data availability fixed.
Maybe your response would be: “Oh yeah that’s easy, someone could totally do that, it’s just that nobody has bothered because the resulting AI would be too big to fit in a car computer”?? Or “Oh yeah, we totally know how to do that, it’s just that it would require more compute than would be affordable or practical at the present time”?? If so, I disagree with both of those possible objections, and we can get into why if it’s crux-y.
Yes, sorry, I can see how that might give people the wrong impression of your views. The diagram was just meant to illustrate the reading that the self-driving case points to a missing major algorithmic ingredient that makes humans very sample efficient learners. The later line (“Humans don’t learn to drive in thirty hours, they are fine-tuned on driving after a roughly two decade-long pretraining run”) was aimed at a more naive version of the argument someone might hold, not at you specifically.
I agree that we don’t have AGI yet, but given what current deep learning architectures/algorithms have already achieved, I don’t think that the ingredients will look too exotic or different from the types of algorithms we have now.
On the narrow self-driving car case, I find it plausible that a model that’s basically a significantly scaled up version of EZ-V2 (requiring >3 OOMs more memory than current Tesla FSD hardware at inference), and given maybe ~2 years of diverse real driving experience (and perhaps alongside a few non-exotic additional regularization and data augmentation techniques), could learn to drive as well as a human. Well, I’d put it at maybe 60% chance that this is true.
On why we can’t teach a model to drive the way a human would, I agree that we’re still missing a few algorithmic ideas, especially related to how to train a good world model for generalizable/transferable online learning and spatial navigation, but even if we did have the right algorithm, I disagree that it’d be “way way way easier” than what Waymo and Tesla have been doing, and I think “the resulting AI would be too big to fit in a car computer” is one major factor. Beyond the scale of the frozen policy itself though, I do expect that the algorithmic thing that is missing for human-like learning will require more compute of some form. Though I may just be lacking imagination about what alternative algorithms are possible.