Yes, more data will help. More data did help with all other Deep Learning problems, from Go to speech synthesis. GPT-3 is orders of magnitude better than GPT-2 because it has more data.
It's really Deep Learning 101.
Furthermore, it's a non-sequitur of an argument.
Dojo is generic neural network training chip. It can be used to speed up any and all Deep Learning problems. Self driving is one such problem.
During AI Day Tesla also presented, in depth, the architecture of the Deep Learning network they use.
If you have a critique of their approach or can show how a competitor's approach is better, then do share with the class. If you don't it's just lazy "Tesla bad because they solved a problem that no-one else solved"
1 - you should prove why you think that this problem is just like the other deep learning problems
2 - it's not true that "no-one else solved" this problem. If we define this problem as: have a self-driving vehicle which safely navigates its environment and does not endanger its passengers or the people outside. See the Waymo whitepapers: waymo.com/safety/performance-data
Ultimately, even if we agree that what Tesla is working on is orders of magnitude better than what's currently available from them, that might not cut it:
Let's assume that the current obstacle detection is 99% accurate... if the newer versions/improved models are 99.9%, 99.99%, etc. accurate...
we'll still have a 0.1, 0.01, 0.001%, etc of chance of an obstacle not being recognized. Tesla cars are a small fraction of those on the road, and there've been several deaths already. If million of Teslas will be sold, those small percentages will still mean a significant number of accidents and deaths caused by Tesla.
If instead a Lidar reliably detects all obstacles, well in advance of the vehicle approaching it (and the car will stop/disable self driving if the Lidar becomes unoperable, e.g. due to bad weather)... it would be irresponsible to persuade huge swaths of the population to let a machine drive the vehicle, without providing the extra safety that a Lidar enables.
It's not just more data, it's more unique scenarios with more samples of each. More edge cases to train on, that's got to help when training on more complex dimensions on the existing model.
They're not just going to bloat the dataset with straight driving.
It's really Deep Learning 101.
Furthermore, it's a non-sequitur of an argument.
Dojo is generic neural network training chip. It can be used to speed up any and all Deep Learning problems. Self driving is one such problem.
During AI Day Tesla also presented, in depth, the architecture of the Deep Learning network they use.
If you have a critique of their approach or can show how a competitor's approach is better, then do share with the class. If you don't it's just lazy "Tesla bad because they solved a problem that no-one else solved"