Tesla AI Day 2021
Why Hacker News recommends it
Treated as a primary source on how Tesla builds its self-driving stack, cited by timestamp for transformers, synthetic data, Dojo and the humanoid bot.
What it is
Tesla's 2021 AI Day presentation covering vision, planning and control, auto-labeling, simulation, the Dojo training hardware and the Tesla Bot announcement, followed by a Q&A.
Recommended by 0 HN commenters across 0 different months, 2021 to 2025.
What Hacker News said
Cites bko · 2021-08 · on “Tesla AI Day Live Stream [video]”
The newly announced Tesla humanoid bot is around 2:05:11. Expected prototype in 2022. The demo was an obvious troll but it looks pretty exciting. I'd rather have resources put into actual innovation than learning how to increase the likelihood someone will click a button by 0.01% [this video]
Cites gpm · 2021-08 · on “John Carmack visits Starbase”
Elon's been giving some lip service to closer-to-GI capabilities anyways, e.g. when describing how it would be optimal to be able to control Tesla's humanoid robot by saying "please go to the store and get me the following groceries"... [this video] Whether Tesla wants to fund serious work towards that may be a different question (but they do seem to be serious about at least building the robot).
Cites dexter89_kp3 · 2021-08 · on “Do vision transformers see like convolutional neural networks?”
They do. Karpathy spoke about it in on Tesla AI day. They use it for transforming image space to a vector space. See: [this video] (timestamp 54.40 onwards)
Cites gamegoblin · 2021-08 · on “Do vision transformers see like convolutional neural networks?”
In Karpathy’s recent AI day presentation he specifically stated they use transformers. But not on the raw camera input — they use regnets for that. The transformers come higher up the stack: [this video] Transformers mentioned on the slide at timestamp 1:00:18.
Cites sillysaurusx · 2021-08 · on “Do vision transformers see like convolutional neural networks?”
Can you provide a more specific timestamp? 54.40 doesn't seem to mention anything about transformers, and "onwards" is two hours. I'd be really surprised if they use transformers due to how computationally expensive they are for anything involving vision. EDIT: Found it. 1h: [this video] Fascinating. I guess transformers are efficient.
Cites tandem5000 · 2021-08 · on “Do vision transformers see like convolutional neural networks?”
They use the key-value lookup/routing mechanism from Transformers to predict pixel-wise labels in bird view (lane, car, obstacle, intersection etc.). The motivation here is that some of the predictions may temporarily be occluded, so for predicting these occluded areas it may be particularly helpful to attend to remote regions in the input images which requires long-range dependencies that highly depend on the input itself (e.g. on whether there is an occlusion), which is exactly where the key-value mechanism excels. Not sure they even process past camera frames at this point. They only mention that later in the pipline they have an LSTM-like NN incorporating past camera frames (Schmidhuber
Cites Dunedan · 2021-08 · on “Tesla Packs 50B Transistors onto D1 Dojo Chip”
> […] AI has a massive value to every aspect of the company's work. That's also just wrong. During the recent "Tesla AI Day", when asked during Q/A, Elon Musk specifically mentioned that they intentionally use machine learning only for very few cases: Q: "Is Tesla using machine learning within its manufacturing, design or any other engineering processes?" Elon: "I discourage use of machine learning, because it's really difficult. Unless you have to use machine learning, don't do it. It's usually a red flag when somebody is saying 'We wanna use machine learning to solve this task'. I'm like: That sounds like bullshit. 99.9% of the time you don't need it." [this video]
Cites thesausageking · 2021-08 · on “The Tesla Dojo Chip Is Impressive, but There Are Some Major Technical Issues”
The Q&A section on their compiler and software that the author links to is very interesting: [this video] It sounds like they're going to have write a ton of custom software in order to use this hardware at scale. And, based on the team being speechless when asked a follow up question, it doesn't sound like they know (yet) how they're going to solve this. Nvidia gets a lot of credit for their hardware advances, but what really what their chips work so well for deep learning was the huge software stack they created around CUDA. Underestimating the software investment required has plagued a lot of AI chip startups. It doesn't sound like Tesla is immune to this.