DeepMind’s AlphaStar Supervised is better than 84% of ranked human players at StarCraft II and it trained purely via imitation learning on about 1 million human-played StarCraft II games. At an average game time of 30 minutes (a high estimate), that’s about 60 years of continuous play.
Those 1 million games were selected from the top 22% of players on the European ranked servers.
How long will it take Tesla to collect 60 years of continuous driving data from the top 20% of drivers in the Hardware 3 fleet for the purposes of imitation learning? I made a spreadsheet to find out.
I start counting in July 2020. My assumption for the purposes of this spreadsheet is that the requisite neural networks and software to do imitation learning for “feature complete” Full Self-Driving will be running in the latest production software update by July 2020.
The biggest caveat is that the computer vision networks will have to be good enough for “feature complete” FSD by this point. That remains to be seen. The comparison to AlphaStar Supervised only pertains to using imitation learning to solve the decision-making component of “feature complete” FSD.
Note: this spreadsheet is just about “feature complete” FSD with human monitoring and interventions, not eyes-off, robotaxi FSD.
The assumption that is by far the hardest to give any kind of reasonable or evidence-based estimate for is the long tail for competent Level 2 highway, city, country, and parking lot decision-making versus StarCraft II. If my thinking is correct, the length of the long tail determines what percentage of data would be useful to collect (via active learning) for driving versus StarCraft II. The way I tried to handle this was to simply give a range of figures:
This allows me to compute a corresponding month by which 60 years of continuous driving data could be collected:
Link to the spreadsheet:
Tesla FSD Computer Fleet Years of Continuous Driving (projected)
Constructive feedback welcome. Feel free to make a copy of the spreadsheet and input your own assumptions.
Those 1 million games were selected from the top 22% of players on the European ranked servers.
How long will it take Tesla to collect 60 years of continuous driving data from the top 20% of drivers in the Hardware 3 fleet for the purposes of imitation learning? I made a spreadsheet to find out.
I start counting in July 2020. My assumption for the purposes of this spreadsheet is that the requisite neural networks and software to do imitation learning for “feature complete” Full Self-Driving will be running in the latest production software update by July 2020.
The biggest caveat is that the computer vision networks will have to be good enough for “feature complete” FSD by this point. That remains to be seen. The comparison to AlphaStar Supervised only pertains to using imitation learning to solve the decision-making component of “feature complete” FSD.
Note: this spreadsheet is just about “feature complete” FSD with human monitoring and interventions, not eyes-off, robotaxi FSD.
The assumption that is by far the hardest to give any kind of reasonable or evidence-based estimate for is the long tail for competent Level 2 highway, city, country, and parking lot decision-making versus StarCraft II. If my thinking is correct, the length of the long tail determines what percentage of data would be useful to collect (via active learning) for driving versus StarCraft II. The way I tried to handle this was to simply give a range of figures:
5% of cumulative years collected = decision-making long tail is 20x longer for “feature complete” FSD than for StarCraft II
1% of cumulative years collected = decision-making long tail is 100x longer for “feature complete” FSD than for StarCraft II
0.5% of cumulative years collected = decision-making long tail is 200x longer for “feature complete” FSD than for StarCraft II
0.1% of cumulative years collected = decision-making long tail is 1,000x longer for “feature complete” FSD than for StarCraft II
1% of cumulative years collected = decision-making long tail is 100x longer for “feature complete” FSD than for StarCraft II
0.5% of cumulative years collected = decision-making long tail is 200x longer for “feature complete” FSD than for StarCraft II
0.1% of cumulative years collected = decision-making long tail is 1,000x longer for “feature complete” FSD than for StarCraft II
This allows me to compute a corresponding month by which 60 years of continuous driving data could be collected:
5% of cumulative years collected = November 2020
1% of cumulative years collected = October 2021
0.5% of cumulative years collected = July 2022
0.1% of cumulative years collected = July 2025
1% of cumulative years collected = October 2021
0.5% of cumulative years collected = July 2022
0.1% of cumulative years collected = July 2025
Link to the spreadsheet:
Tesla FSD Computer Fleet Years of Continuous Driving (projected)
Constructive feedback welcome. Feel free to make a copy of the spreadsheet and input your own assumptions.
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