I'm joined by James Douma and guests K10, Matt Smith and Nicolas Gibbs, as we view Tesla's FSD presentation (part 1) at Tesla AI Day 2 and ask concerns.
Jump to 2:09:41 to hear summary ideas from visitors
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Damn! Dave and James are on🔥 just pumping out episodes 😅👌🏻
Thank you for all your great insight!
As if understanding the presenters wasn’t hard enough already. It’s flying over my head 1.5x faster!
tough to listen to him at that speed and understand.
Note to Dave Lee. . . A speaker from India should NEVER be presented at 1.5. Having worked with a few, I can say many are hard enough to understand at regular speed. At least for me.
The replay of the Tesla presenters at 1.5x speed really detracts from this podcast. Disappointing.
I think it’s a work around so that YouTube doesn’t pull the vid for copyright or something.
I must ask you to reconsider this 1.5 x speed up thing you’re doing.
James is just extraordinary at explaining complex technology.
Yes, and for that reason these videos should include Dave
and James only and if the presentation is accelerated, it should be to 1.25 only so I can understand what the presenters are saying.
Potential TOC (once I’m done watching):
0:00:00 Introduction
0:02:00 Questions
0:04:30 James highlights to keep in mind
0:05:20 How video is going to be shown
0:05:33 Video starts
0:08:15 Interruption for a couple seconds, then video continues
0:08:55 They review the summary
0:09:29 Explain the compiler
0:12:48 Other questions?
0:12:58 FSD can’t read, but probably will in the future
0:15:48 Lane detection progression
0:21:30 Lane detection is not separate from occupancy network
0:24:14 Lane detection builds on the occupancy network
0:24:55 Video continues
0:27:50 Did the example make sense?
0:28:11 That was not a neural network training problem. The plan is not NN.
0:30:49 Those were 3 scenarios that FSD came up with
0:32:40 Humans can be labeled as higher priority… And are labeled as VRUs (Vulnerable Road Users)
0:33:45 FSD needs to make fast decisions
0:34:34 Will the planning heuristic be converted to a NN? No, only where it’s the best option
0:37:08 Video continues
0:38:48 Interaction search description
0:41:05 FSD latency can be improved by improving the code, it doesn’t need improved hardware
0:42:15 NN planner
not all heroes wear capes
It’s unbelievable how many great panels you and others do after AI Day 2022.
Thanks Dave and all participants for the great conversation. 👏👏👏
Dave and Jimmah are such a nice combo!
I allways listen to every one at 2 times speed and in the beginning I thought something was wrong with me because I had a real hard time understanding what the tesla guys were saying makes me feel better for even getting a word or two at 3x speed
That dude is hard to understand at normal speed. He was like on nitro speed. Great video Dave
Without James Douma explanations I would have no clue. 👍👍🇺🇸
Dave, I really appreciate these videos, but……
In order to confirm if Tesla is winning, we need to get information about the closest competitor.
Who are the competitors, and where are they compared to Tesla?
17:45 There’s wayyyy too much human knowledge built into this system. I want to see a debate between George Hotz and James Douma. I remember a video where FSD should have been more willing to get really close to grass, but because it thinks all sides of road are equally undrivable, it created an awkward situation requiring an intervention.
23:20 An e2e neural net would have developed this abstraction FIRST. And it would have been way more efficient overall, since it would not need to know all the different non-driving-centric, human-centric abstractions that humans have shoved into it.
37:20 Yes, a neural network that mimics the average human in every situation, which doesn’t perform physics simulations, would be superhuman.
Watching George’s stream, he was joking that the occupancy network looks suspiciously like LIDAR lol.
I was wondering (idk if this is even close to reality in any way), will this human coded knowledge be an intermediate step.
James talked about this idea a little but would be super interested to hear Hotz and Douma talk.
@Caleb yes it would.
I think the ideal solution is 80% CommaAI and 20% Tesla. Constantly underestimating the difficulty of driving makes Tesla’s approach more appealing. That’s what Tesla has done. I used to be uber optimistic, but after driving with FSD for a year and seeing hyped up architecture underwhelm, I think it won’t be solved for at _least_ 2 years.
Dave, thanks for this all. It would be cool if you did a live stream with James, because I think there are still questions people would ask that your panel hasn’t asked.
At next AI Day, Tesla should give James a sack full of money to do the presentation. 🇺🇸
Tesla is a muscle car with a lot of brain power behind it. Thanks Dave Lee on Investing, for this great multi-
perspective presentation. I recommend playing at speed .75 😁
I’ve got 2 questions on FSD!
#1 On 2nd test drive I got a photo enforced School zone speed limit ticket for 26 in a 20 in the mail a month later! Why doesn’t FSD recognize more road signs?
#2 on 4th test drive my Model 3 took a right turn then hit a Bus curb Seattle is putting everywhere? It destroyed my tire gouged my wheel and dented side panel. Why don’t they feed these curbs into FSD training?
1.5x speed is just dumb for learning. James is amazing.
Wow! My main takeaway was a basic understanding how a nerve works manifesting the radiance fields. And how Tesla went from 2D to 3D structural changes in a year! And how the repeat inputs create the interdepence for a new reality for the human condition. And I grew to love James Douma how he translates all that to us. Thank you all involved
This is a great format Dave. I was just thinking of something else you might wanna do with James. Just take interesting drives from testers and analyze the decisions the car makes based on what it sees and what it’s capable of. James technical insight will be even easier to understand for folks that are not technically advanced.
I prefer to view the AI processor installed in the car as the end effector to produce the result of better than human safe driving. The super computer processing does the heavy lifting of creating the successful action patterns to have the car take correct actions with the information presented to it and respond as quickly as needed in real-time.