strangecosmos
Non-Member
IMO, the rate of progress depends on whether — particularly after HW3 launches — Tesla can use its training fleet of hundreds of thousands of HW3 cars to do for autonomous driving what AlphaStar did for StarCraft. That is, use imitation learning on the state-action pairs from real world human driving. Then augment in simulation with reinforcement learning.
AlphaStar took about 3 years of development, with little to no publicly revealed progress. The version of AlphaStar that beat MaNa — one of the world’s top professional StarCraft II players — was trained with imitation learning for 3 days, and reinforcement learning for 14 days (on a compute budget estimated around $4 million). So that’s a total of 17 days of training.
In June, Karpathy will have been at Tesla for 2 years. He joined in June 2017. Since at least around that time (perhaps earlier, I don’t know), Tesla has been looking for Autopilot AI interns with expertise in (among other things) reinforcement learning. Karpathy himself spent a summer as an intern at DeepMind working on reinforcement learning. He also worked on reinforcement learning at OpenAI.
The internship job postings also mention working with “enormous quantities of lightly labelled data”. I can think of at least two interpretations:
1. State-actions pairs for supervised learning (i.e. imitation learning) of path planning and driving policy.
2. Sensor data weakly labelled by driver input (e.g. image of traffic light labelled as red by driver braking) for weakly supervised learning of computer vision tasks.
Autopilot and FSD are different from AlphaStar in that Tesla has a plan to roll out features to customers incrementally, so progress is a lot more publicly visible. We didn’t get to see the agents that DeepMind trained, say, 6 months ago. So, we don’t really know how fast the agents went from completely incompetent to superhuman. What’s cool and interesting, though, is that Demis Hassabis seemed totally surprised after AlphaStar beat MaNa:
I don’t think I would be super surprised if, 3 years from now, Tesla is way behind schedule and progress has been plodding and incremental. I would be amazed, but necessarily taken totally off guard, if 3 years from now Tesla’s FSD is at an AlphaStar-like level of performance on fully autonomous (unsupervised) driving.
We can’t predict how untried machine learning projects will turn out. That’s why researchers publish surprising results—we wouldn’t be surprised if we could predict what would happen in advance. The best I can do in my lil’ brain is draw analogies to completed projects like AlphaStar to what Tesla is doing (or might be doing). Then try to identify what relevant differences might change the outcome in Tesla’s case.
AlphaStar took about 3 years of development, with little to no publicly revealed progress. The version of AlphaStar that beat MaNa — one of the world’s top professional StarCraft II players — was trained with imitation learning for 3 days, and reinforcement learning for 14 days (on a compute budget estimated around $4 million). So that’s a total of 17 days of training.
In June, Karpathy will have been at Tesla for 2 years. He joined in June 2017. Since at least around that time (perhaps earlier, I don’t know), Tesla has been looking for Autopilot AI interns with expertise in (among other things) reinforcement learning. Karpathy himself spent a summer as an intern at DeepMind working on reinforcement learning. He also worked on reinforcement learning at OpenAI.
The internship job postings also mention working with “enormous quantities of lightly labelled data”. I can think of at least two interpretations:
1. State-actions pairs for supervised learning (i.e. imitation learning) of path planning and driving policy.
2. Sensor data weakly labelled by driver input (e.g. image of traffic light labelled as red by driver braking) for weakly supervised learning of computer vision tasks.
Autopilot and FSD are different from AlphaStar in that Tesla has a plan to roll out features to customers incrementally, so progress is a lot more publicly visible. We didn’t get to see the agents that DeepMind trained, say, 6 months ago. So, we don’t really know how fast the agents went from completely incompetent to superhuman. What’s cool and interesting, though, is that Demis Hassabis seemed totally surprised after AlphaStar beat MaNa:
I don’t think I would be super surprised if, 3 years from now, Tesla is way behind schedule and progress has been plodding and incremental. I would be amazed, but necessarily taken totally off guard, if 3 years from now Tesla’s FSD is at an AlphaStar-like level of performance on fully autonomous (unsupervised) driving.
We can’t predict how untried machine learning projects will turn out. That’s why researchers publish surprising results—we wouldn’t be surprised if we could predict what would happen in advance. The best I can do in my lil’ brain is draw analogies to completed projects like AlphaStar to what Tesla is doing (or might be doing). Then try to identify what relevant differences might change the outcome in Tesla’s case.
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