Are you on Beta 10.69.2.3?
If I had to articulate my reasons:
* Judging by the videos available online, my perception is that many situations that were impossible for Tesla FSD Beta a year ago have become uneventful in recent weeks. Take a look at Chuck Cook's videos for example (I like the fact that he always highlights the failures).
* Judging again by the videos available online, my perception is that Tesla FSD Beta has encountered and had to deal with more crazy edge cases than any other system. A possible explanation for this is that for a long time Tesla FSD Beta hasn't been geofenced or restricted only to certain types of roads, like highways. You can test it anywhere in North America.
* Tesla FSD Beta currently has 160,000 individuals testing it without road restrictions. As far as I know, no other system has been exposed to similar open-ended large-scale testing.
* Occupancy networks look like a real breakthrough to me -- DNNs that predict whether each voxel in a 3D model is occupied by an object, using only video data as an input. I understood the high-level explanation of these DNNs on AI Day 2. I haven't seen anything like it from anyone else.
* Tesla's DOJO also looks like a breakthrough to me. I understood the high-level explanation of it on AI Day 2. IIRC, DOJO cabinets are 6x faster at training existing neural networks than Nvidia rigs, at 6x lower cost, so call it ~36x more efficient.
Occupancy networks: waymo has published research on this before Tesla announced this at AI day (not clear to me who got there first though https://arxiv.org/pdf/2203.03875v1.pdf)
Tesla's Dojo -> Waymo has TPUs to train on
To me all of this is outweighed by the fact that Waymo has a driverless deployment and Tesla does not. I am pretty biased because as a Tesla owner I am pretty pissed off at this point at how the false positives on the system in detecting close following are stopping my safety score from getting high enough to even be able to access the product I purchased.
But it is pretty hard to say one way or another.
I'd sum up those three points as "more data and more real-world, open-ended, large-scale testing by regular people." Big difference.
> Occupancy networks: waymo has published research on this before Tesla announced this at AI day (not clear to me who got there first though https://arxiv.org/pdf/2203.03875v1.pdf)
AFAIK, Tesla FSD Beta is the only system that has been using these DNNs for open-ended testing.
> Tesla's Dojo -> Waymo has TPUs to train on
I've trained AI models on TPUs. They're nowhere near close to 36x more efficient than Nvidia GPUs.
> I am pretty biased because as a Tesla owner I am pretty pissed off at this point at how the false positives on the system in detecting close following are stopping my safety score from getting high enough to even be able to access the product I purchased.
Oh, I get your frustration... but I also understand why Tesla is being so strict with safety scores at this point. It wouldn't be fair to blame them for that.
Maybe you should consider that when watching YouTube videos of people using it....
Also if Tesla actually published numbers on an MlPerf benchmark, I would be more inclined to believe claims about 36x better efficiency.
https://mlcommons.org/en/training-normal-20/
The fastest times I'm seeing here for image classification and for object detection (not the same, but probably closest proxy out of the tasks benchmarked) are for TPUs.
To know who has better training technology I don't think you should be using a cost-efficiency metric, it seems to me the best thing to use would be who can train networks the fastest. Cost metrics are easy to game especially if you are the ones making the chips (Of course them making chips is cheaper than buying Nvidia chips for them once the capital investment is made). To measure who is ahead in technology, I think you have to look at who can train models the fastest, and right now as far as I can tell, TPUs are unbeat for this. (Although practically speaking it's hard to pull off these large topology things externally and there are also other caveats with ML perf related to how the training setups are optimized, but nonetheless, it's a better signal than what Elon says in a presentation :) )
Tesla fsd in its current state will either crash or do some serious fuck up if you let it unattended for a few hours or maybe less (based on the disengagements in those videos). Forget about driverless Tesla with the current fsd. Waymo has been operating driverless since 2019.
I do agree that it is progressing very nicely. Imo tesla fsd needs 2 more years and a hardware update and it will be there.
Otherwise, I agree that Tesla FSD Beta has been progressing nicely. I don't know if it will take 1, 2, or 5 years to get FSD Beta to an acceptable rate of graceful failures, but I agree it looks likely to get there before the end of the decade!
> within a factor of 100x of Waymo
What's your evidence for this? Is there anybody who has done a systematic comparison of Tesla performance in Arizon zone of Waymo?
https://www.dmv.ca.gov/portal/vehicle-industry-services/auto...
Here is a more human digestible summary:
https://thelastdriverlicenseholder.com/2022/02/09/2021-disen...
In 2021, Waymo averaged ~7,900 miles per disengagement and Cruise averaged ~41,000. In 2020, Waymo averaged ~30,000 and Cruise around 28,500.
Tesla is absent from those reports as they have deliberately declared that all of their vehicles do not even qualify as L4/5 autonomous vehicles. They have furthermore not released any 3rd party auditable metrics for any of their claims. So, from an official perspective, Tesla is infinitely worse than Waymo.
From an unofficial standpoint, we can use Youtube videos and self-reported tracking by invested fans such as here https://www.teslafsdtracker.com/
Both of those classes of unofficial metrics by positively biased groups consistently demonstrate around 10-20 miles per disengagement at max.
As Waymo averaged 8,700 last year, that makes Tesla around 400-800x worse than Waymo as of last year and around 2,000-4,000x worse than Cruise as of last year.
We can also see from the unofficial Tesla fan metrics that FSD Beta has seen no material improvement from around one year ago.
But you have seen how Tesla performs in such environments, and you aren't allowed to take your hands off the wheel.
What makes you assume Tesla has the right approach and the other have companies have to be measured against it?
All fully autonomous cars are in a different legal situation then Tesla. Tesla sells Joe Shmoe a car and then tells him he can rub FSD but he's responsible and has to remain attentive then they get info about every disengagement and (mostly) avoid legal responsibility or accidents in many cases.
Waymo is fully responsible for every accident, etc so they HAVE to proceed more cautiously or they'll lose the ability to run their cars. As someone else pointed out they often are only operated in very specific areas, and often even specific streets within a geofence. So while on the surface Waymo may have full self driving operating more effectively with less problems, they're doing so in a much more controlled environment and not getting the variety of data that Tesla has from cars disengaging Literally anywhere in the US.
i didn't sign up to be killed by some idiot tech bro testing a class project where they plumbed alexnet into the steering wheel of a 2000kg vehicle and took a couple of steps downhill
the streets are already dangerous enough for pedestrians
However, to my knowledge, Tesla is still in the "bring up" step. I think that was a mistake to pack 25 dies on a single system-on-wafer. Very hard to cool.
Do you think the minisub and Tesla ventilators also existed?
Have you considered that other companies don't make it a priority to market these things? Elon knows his audience: people who will go on message boards and talk about it. Most people don't care about the underlying AI tech.
Do you think the other companies aren't making any breakthroughs? How do they have Robotaxis then?
Your entire claimed expertise seems to come from YouTube promotional videos. Maybe take a step back from marketing hype.
It’s a completely different problem space, like claiming someone built a train and therefore they can easily build self driving cars since they are both “driverless”.
Edit: and surely Waymo/Cruise could launch everywhere with performance that's lower than their current launch cities, but they choose not to. I don't think there's any compelling reason to assume their tech doesn't work outside of SF or Arizona or wherever, they just don't want to be in the news for their cars plowing someone into a highway divider or running over a pedestrian.
I work at another autonomous car company (as a security engineer not ML related work) and I know we have a Lot of simulated situations that we run the ML against and add more from situations collected from actual driving.
If you are modeling scenarios like a game engine, a "discriminator" model isn't necessary: you just check whether a simulation doesn't result in a crash.