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A lot of folks have been saying that Tesla will win, because they will gather more data from real conditions. This never made sense to me. You certainly need

A lot of folks have been saying that Tesla will win, because they will gather more data from real conditions. This never made sense to me. You certainly need enough data, but how you interpret and process that data is far more important.

datguacdoh 10y ago
Also, how much of that data can you realistically send back to yourself? With Waymo they can pull the arrays from the cars nightly if they need to. Tesla, on the other hand, has to send the data back over a customers network connection which would be much more limiting. So Tesla might be getting tons of miles but if that's a billion not very detailed miles, it might not be as useful as a million incredibly detailed miles.
- robszumski 10y ago
  I think Tesla pays for the network connection to all of its cars. This sends back remote telemetry and other self-driving data. It also serves up software updates and monitors the cars network for intrusions via a VPN.
  - datguacdoh 10y ago
    That network connection is using cellular, so at most it's an LTE connection but if I remember right, is actually just 3G. I can't believe they are pushing the kind of data people are talking about over that kind of connection.
adevine 10y ago
It makes sense because virtually all machine-learning algorithms work better, and can learn faster, if you have more data. Think of it this way: if Tesla wants to test a particular algorithm for a particular driving situation, they can "play it back" over an enormous amount of real-world situations. They will have tons more potential edge cases with which they can validate their algorithms.
- flylib 10y ago
  the data they have is not detailed enough though has been the criticism
- renesd 10y ago
  This used to be true. Modern machine learning needs way less data. The classic example is taking images and then transforming them in hundreds of ways (scaling, rotation, skew, etc) for training. Big data is no where near as much a competitive advantage as it was three years ago. It seems not everyone outside the field has noticed that though.
  - adevine 10y ago
    I wonder if this would be true in the case of self-driving car algorithms, though (which I know nothing about). It always seemed like the hard part about self-driving cars was the 0.1% edge cases where something out of the ordinary could result in a catastrophe if not handled correctly. Image classification seems like it would be very different, most importantly that 99.9% "correct" would be a great achievement, but for self-driving cars a .1% failure rate would be completely unacceptable.
    - elmar 10y ago
      We are 30 to 50 years away of a level 5 car.
  - eanzenberg 10y ago
    Lul thinking machine learning is only image classification
    - eanzenberg 10y ago
      Thank you for the down-votes. I guess all the experts on HN know how easy it is to simulate training data because they all took the 101 course on how to rotate/resample images. That is uniquely a image classification technique. Please oh wise ones how do we simulate nlp data, numeric data, finance data, biological data and anything else machine learning is used for. Oh you are able to classify dogs and cats in images after a 2 hour youtube. How nice.
stuckagain 10y ago
The popular idea that Tesla will just keep stuffing matrices down the throat of their training pipeline until a self-driving inference model emerges from the other end doesn't make a lot of sense.
- Fricken 10y ago
  Neural nets can take you most of the way there, but pattern recognition alone will not solve the driving problem to completion. Waymo's autonomous platform is a frankenstein of various machine learning techniques, and much of it isn't glamorous, it's less contigent on big breakthroughs than it is on elbow grease. Google demoed as proof-of concept full autonomy in 2012, and much of what they've been doing in the 4 years between then and now is the tedious job of addressing and validating their system across the full spectrum of edge cases that must be dealt with if they ever hope to foist their safety critical software upon the public. It's not clear to me that Tesla's current development paradigm will ever be sufficient to completely take the human out of the loop. Tesla's approach is incremental, and I suspect they'll have to make some big changes if they wish to fully close the gap. Waymo has kept their eye on the prize from day 1.
asdfasdfa11112 10y ago
Argument is that Tesla has more representative / real world data. Big public opinion perspective here too.

datguacdoh 10y ago

Also, how much of that data can you realistically send back to yourself? With Waymo they can pull the arrays from the cars nightly if they need to. Tesla, on the other hand, has to send the data back over a customers network connection which would be much more limiting. So Tesla might be getting tons of miles but if that's a billion not very detailed miles, it might not be as useful as a million incredibly detailed miles.

robszumski 10y ago
I think Tesla pays for the network connection to all of its cars. This sends back remote telemetry and other self-driving data. It also serves up software updates and monitors the cars network for intrusions via a VPN.
- datguacdoh 10y ago
  That network connection is using cellular, so at most it's an LTE connection but if I remember right, is actually just 3G. I can't believe they are pushing the kind of data people are talking about over that kind of connection.

adevine 10y ago

It makes sense because virtually all machine-learning algorithms work better, and can learn faster, if you have more data. Think of it this way: if Tesla wants to test a particular algorithm for a particular driving situation, they can "play it back" over an enormous amount of real-world situations. They will have tons more potential edge cases with which they can validate their algorithms.

flylib 10y ago
the data they have is not detailed enough though has been the criticism
renesd 10y ago
This used to be true. Modern machine learning needs way less data. The classic example is taking images and then transforming them in hundreds of ways (scaling, rotation, skew, etc) for training. Big data is no where near as much a competitive advantage as it was three years ago. It seems not everyone outside the field has noticed that though.
- adevine 10y ago
  I wonder if this would be true in the case of self-driving car algorithms, though (which I know nothing about). It always seemed like the hard part about self-driving cars was the 0.1% edge cases where something out of the ordinary could result in a catastrophe if not handled correctly. Image classification seems like it would be very different, most importantly that 99.9% "correct" would be a great achievement, but for self-driving cars a .1% failure rate would be completely unacceptable.
  - elmar 10y ago
    We are 30 to 50 years away of a level 5 car.
- eanzenberg 10y ago
  Lul thinking machine learning is only image classification
  - eanzenberg 10y ago
    Thank you for the down-votes. I guess all the experts on HN know how easy it is to simulate training data because they all took the 101 course on how to rotate/resample images. That is uniquely a image classification technique. Please oh wise ones how do we simulate nlp data, numeric data, finance data, biological data and anything else machine learning is used for. Oh you are able to classify dogs and cats in images after a 2 hour youtube. How nice.

stuckagain 10y ago

The popular idea that Tesla will just keep stuffing matrices down the throat of their training pipeline until a self-driving inference model emerges from the other end doesn't make a lot of sense.

Fricken 10y ago
Neural nets can take you most of the way there, but pattern recognition alone will not solve the driving problem to completion. Waymo's autonomous platform is a frankenstein of various machine learning techniques, and much of it isn't glamorous, it's less contigent on big breakthroughs than it is on elbow grease. Google demoed as proof-of concept full autonomy in 2012, and much of what they've been doing in the 4 years between then and now is the tedious job of addressing and validating their system across the full spectrum of edge cases that must be dealt with if they ever hope to foist their safety critical software upon the public. It's not clear to me that Tesla's current development paradigm will ever be sufficient to completely take the human out of the loop. Tesla's approach is incremental, and I suspect they'll have to make some big changes if they wish to fully close the gap. Waymo has kept their eye on the prize from day 1.

asdfasdfa11112 10y ago

Argument is that Tesla has more representative / real world data. Big public opinion perspective here too.