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And this is why I'm much less pessimistic than most about robotaxis. Waymo has a working robotaxi in a limited area and they got there with a fleet of 600 cars

by kjksf 5y ago

And this is why I'm much less pessimistic than most about robotaxis. Waymo has a working robotaxi in a limited area and they got there with a fleet of 600 cars and mere millions of driving data. Now imagine they trained on 100x cars i.e. 60k cars and billions of driving data. Guess what, Tesla already has FSD running, under human supervision, in 60k cars and that fleet is driving billions of miles. They are colleting 100x data sets as I write this. We also continue to significantly improve hardware for both NN inference (Nvidia Drive, Tesla FSD chip) and training (Nvidia GPUs, Tesla Dojo, Google TPU and 26 other startups working on AI Hardware https://www.ai-startups.org/top/hardware/ https://www.ai-startups.org/top/hardware/) If the bitter lesson extends to problem of self-driving, we're doing everything right to solve it. It's just a matter of time to collect enough training data, have enough compute to train the neural network and enough compute to run the network in the car.

fxtentacle 5y ago
You're not wrong, but I believe you're so far off on the necessary scale that it'll never solve the problem. For an AI to learn to play Bomberman at an acceptable level, you need to run 2-3 billion training steps with RL learning where the AI is free to explore new actions to collect data about how well they work. I'm part of team CloudGamepad and we'll compete in the Bomberland AI challenge finals tomorrow, so I do have some practical experience there. Before I looked at things in detail, I also vastly overestimated reinforcement learning's capabilities. For an AI to learn useful policy without the ability to confirm what an action does, you need exponentially more data. There's great papers by DeepMind and OpenAI that try to ease the pain a bit, but as-is, I don't think even a trillion miles driven would be enough data. Letting the AI try out things, of course, is dangerous, as we have seen in the past. But the truly nasty part about AI and RL in particular is that the AI will act as if anything that it didn't see often enough during training simply doesn't exist. If it never sees a pink truck from the side, no "virtual neurons" will grow to detect this. AIs in general don't generalize. So if your driving dataset lacks enough examples of 0.1% black swan events, you can be sure that your AI is going to go totally haywire when they happen. Like "I've never seen a truck sideways before => it doesn't exist => boom."
- shadowgovt 5y ago
  The sensors self-driving cars use are far less sensitive to color than human eyes. You can generalize your concept to the other sensors, but sensor fusion compensates somewhat... The odds of an input being something never seen across all sensor modalities become pretty low. (And when it did see something weird, it can generally handle it the way humans do... Drive defensively).
- gwern 5y ago
  > But the truly nasty part about AI and RL in particular is that the AI will act as if anything that it didn't see often enough during training simply doesn't exist. If it never sees a pink truck from the side, no "virtual neurons" will grow to detect this. AIs in general don't generalize. So if your driving dataset lacks enough examples of 0.1% black swan events, you can be sure that your AI is going to go totally haywire when they happen. Like "I've never seen a truck sideways before => it doesn't exist => boom." Let's not overstate the problem here. There are plenty of AI things which would work well to recognize a sideways truck. Look at CLIP, which can also be plugged into DRL agents (per the cake); find an image of your pink truck and text prompt CLIP with "a photograph of a pink truck" and a bunch of random prompts, and I bet you it'll pick the correct one. Small-scale DRL trained solely on a single task is extremely brittle, yes, but trained over a diversity of tasks and you start seeing transfer to new tasks and composition of behaviors and flexibility (look at, say, Hide-and-Seek or XLAND). These are all in line with the bitter hypothesis that much of what is wrong with them is not some fundamental problem that will require special hand-designed "generalization modules" bolted onto them by generations of grad students laboring in the math mines, but simply that they are still trained on too undiverse problems for too short a time with too little data using too little models, and that just as we already see strikingly better results in terms of generalization & composition & rare datapoints from past scaling, we'll see more in the future. What goes wrong with Tesla cars specifically, I don't know, but I will point out that Waymo manages to kill many fewer people and so we shouldn't consider Tesla performance to even be SOTA on the self-driving task, much less tell us anything about fundamental limits to self-driving cars and/or NNs.
  - mattnewton 5y ago
Animats 5y ago
Waymo is not a raw neural network. Waymo has an explicit geometric world model, and you can look at it.
VHRanger 5y ago
More data doesn't help if the additional data points don't add information to the dataset. At some point it's better to add features than simply more rows of observations. Arguably text and images are special cases here because we do self supervised learning (which you cant do for self driving for obvious reasons). What TSLA should have done a long time ago is keep investing in additional sensors to enrich data points, rather than blindly collecting more of the same.

by kjksf 5y ago

fxtentacle 5y ago

You're not wrong, but I believe you're so far off on the necessary scale that it'll never solve the problem. For an AI to learn to play Bomberman at an acceptable level, you need to run 2-3 billion training steps with RL learning where the AI is free to explore new actions to collect data about how well they work. I'm part of team CloudGamepad and we'll compete in the Bomberland AI challenge finals tomorrow, so I do have some practical experience there. Before I looked at things in detail, I also vastly overestimated reinforcement learning's capabilities. For an AI to learn useful policy without the ability to confirm what an action does, you need exponentially more data. There's great papers by DeepMind and OpenAI that try to ease the pain a bit, but as-is, I don't think even a trillion miles driven would be enough data. Letting the AI try out things, of course, is dangerous, as we have seen in the past. But the truly nasty part about AI and RL in particular is that the AI will act as if anything that it didn't see often enough during training simply doesn't exist. If it never sees a pink truck from the side, no "virtual neurons" will grow to detect this. AIs in general don't generalize. So if your driving dataset lacks enough examples of 0.1% black swan events, you can be sure that your AI is going to go totally haywire when they happen. Like "I've never seen a truck sideways before => it doesn't exist => boom."

shadowgovt 5y ago
The sensors self-driving cars use are far less sensitive to color than human eyes. You can generalize your concept to the other sensors, but sensor fusion compensates somewhat... The odds of an input being something never seen across all sensor modalities become pretty low. (And when it did see something weird, it can generally handle it the way humans do... Drive defensively).
gwern 5y ago
> But the truly nasty part about AI and RL in particular is that the AI will act as if anything that it didn't see often enough during training simply doesn't exist. If it never sees a pink truck from the side, no "virtual neurons" will grow to detect this. AIs in general don't generalize. So if your driving dataset lacks enough examples of 0.1% black swan events, you can be sure that your AI is going to go totally haywire when they happen. Like "I've never seen a truck sideways before => it doesn't exist => boom." Let's not overstate the problem here. There are plenty of AI things which would work well to recognize a sideways truck. Look at CLIP, which can also be plugged into DRL agents (per the cake); find an image of your pink truck and text prompt CLIP with "a photograph of a pink truck" and a bunch of random prompts, and I bet you it'll pick the correct one. Small-scale DRL trained solely on a single task is extremely brittle, yes, but trained over a diversity of tasks and you start seeing transfer to new tasks and composition of behaviors and flexibility (look at, say, Hide-and-Seek or XLAND). These are all in line with the bitter hypothesis that much of what is wrong with them is not some fundamental problem that will require special hand-designed "generalization modules" bolted onto them by generations of grad students laboring in the math mines, but simply that they are still trained on too undiverse problems for too short a time with too little data using too little models, and that just as we already see strikingly better results in terms of generalization & composition & rare datapoints from past scaling, we'll see more in the future. What goes wrong with Tesla cars specifically, I don't know, but I will point out that Waymo manages to kill many fewer people and so we shouldn't consider Tesla performance to even be SOTA on the self-driving task, much less tell us anything about fundamental limits to self-driving cars and/or NNs.
- mattnewton 5y ago

Animats 5y ago

Waymo is not a raw neural network. Waymo has an explicit geometric world model, and you can look at it.

VHRanger 5y ago

More data doesn't help if the additional data points don't add information to the dataset. At some point it's better to add features than simply more rows of observations. Arguably text and images are special cases here because we do self supervised learning (which you cant do for self driving for obvious reasons). What TSLA should have done a long time ago is keep investing in additional sensors to enrich data points, rather than blindly collecting more of the same.