Technically all cars have a proper GPU (Tesla's "GPU") and 360 cameras, the reliability is probably in the 90%-99% (which, yes, is pretty terrible to be considered fully autonomous, but impressive from a technical standpoint). Technically is correct to say that all they have to do is to "improve the software". That will be a lot of work, sweat and tears, but still correct.
> "All Tesla vehicles produced in our factory, including Model 3, have the hardware needed for full self-driving capability at a safety level substantially greater than that of a human driver."
Yet the original "HW1" computer was replaced by "HW2", which was replaced by "FSD Computer 2.5", which was replaced by "FSD Computer 3".
If you want to activate the Full Self-Driving package today on a Tesla you just bought in early 2019, they'll tell you that your "FSD Computer 2.5" is not capable enough for even today's level 2 autonomy features, you first have to replace it with "FSD Computer 3". And if your car's a bit older, but still sold as "has the hardware needed for full self-driving", you have to pay $1000 for that upgrade before you can pay for the "full self-driving" that isn't self-driving.
How many times does Tesla have to claim the hardware is good enough, but then change their minds a year later, before any reasonable person stops believing them?
To give you an example, ImageNet for image classification required for many years a descent computer and GPU to run inference. Today, an improved version called MobileNet, runs in phones with no GPU from advancements in AI training with half-precision and why not.
So, how can we be so sure that FSD can't EVER be ported back to HW1?
It doesn't matter whether it can't ever be ported back. It doesn't work today, and that's all there is to it. Musk lied. And it isn't the first time he does so either.
[1] https://en.wikipedia.org/wiki/Development_of_Windows_Vista
Just like you can't be a little bit pregnant you can't have a little bit of FSD. You either have it or you don't. It's a boolean, and it's embedded in that word 'full'.
Windows Vista and software in general are not even remotely comparable: they are not sold today with the promise of features tomorrow - they are generally sold when they're ready. Even pre-order games are usually refunded if they incur too huge delays or ship with significantly altered features (see Cyberpunk 2020).
Are you saying that the possibility is enough to prove they didn't cheat people?
If, some day, Tesla hypothetically says "we have determined it is technically possible to backport FSD, but we aren't gonna, because we like making money, not spending it" is that enough in your mind to demonstrate they didn't defraud anyone way back when?
Or do they actually have to make it run to fulfill the original promise?
That said, if it doesn't happened this year, it doesn't mean it will never happen.
The first problem they need to solve is FSD. If they can't solve FSD even with latest hardware, then well, that would be bad on its own.
The question is, what happens once they solve FSD, do they port? Maybe, I don't think it will be nearly as hard. Now realistically, it's probably cheaper to give free upgrades to everyone and not have to do the port. To me that feels fair, is like "hey, I promised you FSD on HW1, is gonna be a pain to port, but here, take this free HW upgrade", I'd be OK with that.
Now if they don't compensate HW1 users to some degree once FSD is solved, I do feel that would not be cool.
But my point here is that, we are jumping to conclusions, we don't know yet. We can't claim (yet) that FSD will or will not happen.
Okay, compare accuracy of MobileNet to ViT. ViT has 90.45% accuracy and 1.843Bn parameters and MobileNet has 79% with 5.47M parameters (2020, v3 is 75% with 5.4). [0]
These two networks are nowhere near equal. If you could put ViT on a phone and run real-time inference you would. But you can't, so you take a major cut in performance to get what you can.
There are multiple metrics you need to look at when evaluating networks. "Being able to perform on a dataset" is not even that useful of one.
[0] Have fun https://paperswithcode.com/sota/image-classification-on-imag...
https://news.ycombinator.com/item?id=28002731
It's verbatim from Elon Musk on Autonomy Day 2019, per this article:
https://www.engadget.com/2019-04-22-tesla-elon-musk-self-dri...
This is the same event where he promised a million Tesla robotaxis on the road by 2020.
No, that was NOT a verbatim quote. Here is the exact quote with me adding emphasis on the critical (but oft omitted) qualifier:
"All Teslas being produced right now have this computer. We switched over from the Nvidia solution for S and X about a month ago, and we switched over the Model 3 about ten days ago. All cars being produced have the all the hardware necessary, compute and otherwise, for full self-driving. I'll say that again. All Tesla cars being produced right now have everything necessary for full self-driving. All you need to do is improve the software."
https://youtu.be/Ucp0TTmvqOE?t=5663
> This is the same event where he promised a million Tesla robotaxis on the road by 2020.
That is, in my opinion, not a contextually correct reading of what Elon said. Within the context of the speech it's clear (to me) that for him, "robotaxi" is used as shorthand for "vehicles with the hardware capable of being a robotaxi." Obviously I cannot prove this, and I recognise that responsibility ultimately lies with Elon to be clear.
I do agree Elon did say multiple sentences that are absolutely misleading when read in isolation, but his actual words in context (and with some recognition that this is a forward looking statement made with optimism) were far less egregiously wrong than the snipped quote implies on its own. You can watch him say it in context and make up your own mind:
Same thing with the robotaxi quotes. Elon was very clear on autonomy day and the days surrounding it. The promise was not shorthand for "vehicles capable of being a robotaxi", it was Tesla vehicles, and I quote again, "operating robotaxis next year with no one in them", and "I feel very confident predicting that there will be autonomous robotaxis from Tesla next year — not in all jurisdictions because we won’t have regulatory approval everywhere.". You don't need regulatory approval for hardware alone.
In another investor presentation Elon Musk made in 2019, per a reporter covering it, they were promising the Tesla Network to allow Tesla owners to send their functioning robotaxis out to pick people up for money would launch in 2020.
> Starting next year, owners will be able to flip a switch inside the Tesla app, and send out their car to pick up and drop off passengers autonomously, earning an estimated 65 cents per mile in fares. By Tesla’s estimates, owners might be able to earn $30,000 in gross revenue from their cars per year, or more than $300,000 in revenue over the 11-year lifespan of their car.
Not included in your response, I should note, was any acknowledgement that you misrepresented a quote as being verbatim was in fact not verbatim. This makes me entirely disinterested in further interaction.
So it's a bold claim given what we know. It's also clear that next generation hardware will be safer regardless of if the previous generation "works."
Training is the process of generating the parameters for the neural network, it usually takes significant resources to train and is a very hard process.
Inference is the process of using a pre-trained neural network to perform predictions.
Tesla's car have hardware to perform inference. From working on the field for several years, I do believe that they have hardware capable of performing inference (driving a car).
However, the hardware that trains the neural network is comprised of hundreds, if not thousands or tens of thousands of GPUs, that hardware lives in Tesla's data centers (or some other cloud provider).
The really hard part of this project is on training, not inference. So while there is some risk that the car (inference) hardware might require an upgrade, this seems very unlikely to me.
While you're right, inference is not training but accuracy and inference speed is still important and hardware dependent. YOLO still can be faster and more accurate. Everything I said still holds true. You want to do inference on larger images because larger images have more information. More information helps you get better accuracy. You want bigger networks because bigger networks correlate strongly with higher accuracy. If you read any object detection paper you will see different sized models showing these tradeoffs. You want faster hardware because you still want your algorithms to be fast. Inference is still done in the car. Better hardware still makes all that perform better.
> The really hard part of this project is on training, not inference.
If you pay attention to the YOLO series or other real time object detection you will know that inference is still something being worked on. Inference speed matters. Throughput (different metric) matters. Hardware helps with both even if we use the same pretrained network. A network will have faster inference and higher throughput on an Ampere card than on a Kepler. So I'm not sure what you're debating here, it really feels like you're just trying to tell me you know machine learning but missed strong clues that you're talking to someone else who is aware.
TLDR: hardware still matters.
From the outside I’ve wondered if the missed schedule targets around autonomous driving is because we’ve conflated “solving” the perception problem with solving self-driving. Just curious what your take is on that
Since it has never been done we still don't know what's needed, so even if you worked in the field that's only intuition, a bit light for big commercial claims IMHO.
So what's wrong with the statement is it fits a pattern of statements with reckless disregard for the truth.
Going back to AlexNet, while GPUs helped the training process, it included significant software improvements that made a huge difference: training with dropout, convolutional neural networks, using rectified linear units, etc.
Also, training hardware is much more relevant than hardware for inference. Tesla's cars do not include hardware to train the neural network but just for inference. The training hardware is actually a server cluster that Tesla owns. So if hardware is insufficient, Tesla might have to buy a larger training cluster, but not necessarily upgrade the inference hardware.
That say, I do agree that there is some risk in requiring a hardware upgrade for inference, but to me that seems very unlikely.
However, I'm NOT stating that this won't ever work. I do believe that FSD will work in the future. Are there risks? For sure. But I'm very optimistic.
I'm trying to communicate that, even with current in-car hardware, this should be sufficient. Why? Cause driving the car with a neural-network is actually quite trivial. Now, training the neural-model is actually REALLY hard. But the training does not happen in the car, car just collects data and sends it to a data center with GPUs that trains the network. If any hardware will need upgrade, is the data center were training happens, not the actual in-car hardware.
Are you really saying that FSD will never work? Cause that's a pretty bold claim.
This claim is quite obviously true, because despite the claims of Tesla's people, we don't know of any vision system that works without relying on general intelligence. Human/animal vision in particular is only accurate because we inherently use our knowledge of the world and physical intuition to resolve ambiguity. Put a human (or other mammal or a bird) in a fully artifical environment, without recognizable objects and without shadows, and you'll see that they are about as bad at vision and navigation tasks as many current algorithms. The effect will be even worse if you recreate realistic objects but with unrealistic proportions and behaviors.
Since we can't hope that computers will gain animal level intelligence in understanding the world (no one is even really working on the necessary problems), the only path to self driving is through more advanced sensors, as everyone but Tesla has realized for a long time. This is why Waymo has self-driving cars on the road today (in perfect ideal conditions), and Teslas can sometimes barely navigate an empty intersection without a human driver intervening.
This is an extremely bold claim. You're also ignoring the fact that driving is an extremely heavy tailed distribution. We know that both processing heavy tailed distributions is difficult (not training, but pre-processing) as well as we see a strong correlation between larger networks and performance on heavy tailed distributions (more normal like distributions can get away with substantially smaller networks). But larger networks means more MACs. More MACs means slower inference. There's still a tradeoff between accuracy and speed. In driving you need both.
> Are you really saying that FSD will never work?
No one here is saying that. Everyone here is calling you out on your BS that we _know_ that FSD will work on _current_ hardware. We don't _know_ that. Maybe it will, but it's not a pony I'm willing to put money on.