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What's wrong with the latest statement? Technically all cars have a proper GPU (Tesla's "GPU") and 360 cameras, the reliability is probably in the 90%-99% (whi

What's wrong with the latest statement? 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.

ac29 5y ago
Until it actually works, how can you say the hardware is sufficient?
- javierluraschi 5y ago
  Historically, there have been significant bottlenecks on algorithms, I'd argue software plays a bigger role than hardware; especially during inference. 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.
  - deleted 5y ago
    [deleted]
  - cat199 5y ago
    so, you're saying it doesn't work then?
    - javierluraschi 5y ago
      Correct! I'm saying FSD only works on 90%-99% of cases, which is currently insufficient to be actually considered FSD. Keyword, currently. 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.
      - javierluraschi 5y ago
        I'd add, we can be more specific about what a "case" is. I'd say, a trip might describe this better. As in, 90-99% of trips FSD Beta 9 will take you there without interventions. That's still lame, but still quite impressive. Achieving 90-99% accuracy on trips still seemed impossible one year ago, I don't think that's the case anymore. The remaining risk is, how fast can this improve between 99% to 99.9% and then to 99.99%, etc.
      - deleted 5y ago
        [deleted]
      - tsimionescu 5y ago
        The reasonable expectation is that cars will not be able to achieve actual self-driving without either AGI or more specialized hardware than a camera. AGI is almost certainly a 'next century' kind of technology. Adding sensors other than cameras (lidar, others) to a Tesla means that they don't have the hardware. 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.
godelski 5y ago
Because the claim can't be backed by evidence. There's still a camera alone vs camera +lidar debate. But besides that it's not hard to imagine that the neural net needed to process whatever vision you need isn't powerful enough, so you need next generation GPUs/TPUs/whatever-Us. We just don't know until it's been done. But we know that the more frames you can process per second the better. We do know that the more resolution an image has the more information it has and the higher potential accuracy a network has (if you're following ViTs you'll notice people are upping the image resolution and getting better classification results). 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."
- javierluraschi 5y ago
  AI algorithms are split into training and inference. 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.
  - godelski 5y ago
    Thanks? I'm a ML researcher. I thought the comment about ViTs would have been a dead giveaway. Is this nerd-splaining? Because it is really demeaning and you're missing the entire point. 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.
dangrossman 5y ago
Since the fall of 2016, Tesla's website has said: > "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?
- simondotau 5y ago
  Had the GP quoted Elon saying this in fall 2016 then you would have an entirely valid point. But the GP did not do this. The GP dated the quote as being made in 2020, when all new cars did in fact contain HW3.
  - dangrossman 5y ago
    The quote is not "all Tesla cars have HW3", it's "all Tesla cars right now have everything necessary for self-driving". He said that same thing about HW2 and about HW2.5. There are no self-driving Tesla robotaxis running any computer version. It was as wrong to say it in 2020 as it was in 2016, 2017, 2018 and 2019. Maybe at next month's "Tesla AI Day" (not to be confused with 2019's "Tesla Autonomy Day") he'll debut HW4 which will definitely be the one that turns your car into a robotaxi.
    - simondotau 5y ago
      You appear to be misquoting. Can you please cite this 2020 quote verbatim and in context? If you can, and the context is consistent with what you have inferred, I will retract everything and offer my most generous and profuse apology.
wpietri 5y ago
You have no idea whether that's sufficient hardware for full self driving. Nobody does, because we don't know how to make it happen. We can't even prove that it's doable short of AGI. So what's wrong with the statement is it fits a pattern of statements with reckless disregard for the truth.
bumby 5y ago
“I’ve cured cancer, just got to finish the research. Am actively looking for investors. This is your chance to get in on the ground floor.”

ac29 5y ago

Until it actually works, how can you say the hardware is sufficient?

javierluraschi 5y ago
Historically, there have been significant bottlenecks on algorithms, I'd argue software plays a bigger role than hardware; especially during inference. 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.
- deleted 5y ago
  [deleted]
- cat199 5y ago
  so, you're saying it doesn't work then?
  - javierluraschi 5y ago
    Correct! I'm saying FSD only works on 90%-99% of cases, which is currently insufficient to be actually considered FSD. Keyword, currently. 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.
    - javierluraschi 5y ago
      I'd add, we can be more specific about what a "case" is. I'd say, a trip might describe this better. As in, 90-99% of trips FSD Beta 9 will take you there without interventions. That's still lame, but still quite impressive. Achieving 90-99% accuracy on trips still seemed impossible one year ago, I don't think that's the case anymore. The remaining risk is, how fast can this improve between 99% to 99.9% and then to 99.99%, etc.
    - deleted 5y ago
      [deleted]
    - tsimionescu 5y ago
      The reasonable expectation is that cars will not be able to achieve actual self-driving without either AGI or more specialized hardware than a camera. AGI is almost certainly a 'next century' kind of technology. Adding sensors other than cameras (lidar, others) to a Tesla means that they don't have the hardware. 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.

godelski 5y ago

Because the claim can't be backed by evidence. There's still a camera alone vs camera +lidar debate. But besides that it's not hard to imagine that the neural net needed to process whatever vision you need isn't powerful enough, so you need next generation GPUs/TPUs/whatever-Us. We just don't know until it's been done. But we know that the more frames you can process per second the better. We do know that the more resolution an image has the more information it has and the higher potential accuracy a network has (if you're following ViTs you'll notice people are upping the image resolution and getting better classification results). 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."

javierluraschi 5y ago
AI algorithms are split into training and inference. 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.
- godelski 5y ago
  Thanks? I'm a ML researcher. I thought the comment about ViTs would have been a dead giveaway. Is this nerd-splaining? Because it is really demeaning and you're missing the entire point. 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.

dangrossman 5y ago

Since the fall of 2016, Tesla's website has said: > "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?

simondotau 5y ago
Had the GP quoted Elon saying this in fall 2016 then you would have an entirely valid point. But the GP did not do this. The GP dated the quote as being made in 2020, when all new cars did in fact contain HW3.
- dangrossman 5y ago
  The quote is not "all Tesla cars have HW3", it's "all Tesla cars right now have everything necessary for self-driving". He said that same thing about HW2 and about HW2.5. There are no self-driving Tesla robotaxis running any computer version. It was as wrong to say it in 2020 as it was in 2016, 2017, 2018 and 2019. Maybe at next month's "Tesla AI Day" (not to be confused with 2019's "Tesla Autonomy Day") he'll debut HW4 which will definitely be the one that turns your car into a robotaxi.
  - simondotau 5y ago
    You appear to be misquoting. Can you please cite this 2020 quote verbatim and in context? If you can, and the context is consistent with what you have inferred, I will retract everything and offer my most generous and profuse apology.

wpietri 5y ago

You have no idea whether that's sufficient hardware for full self driving. Nobody does, because we don't know how to make it happen. We can't even prove that it's doable short of AGI. So what's wrong with the statement is it fits a pattern of statements with reckless disregard for the truth.

bumby 5y ago

“I’ve cured cancer, just got to finish the research. Am actively looking for investors. This is your chance to get in on the ground floor.”