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.
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.