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Lack of focus is a major problem for companies and we all know that tech debt leads to increased bug counts. Team focus on vision which is by far the highest a

Lack of focus is a major problem for companies and we all know that tech debt leads to increased bug counts. Team focus on vision which is by far the highest accuracy and bandwidth sensor allows for a faster rate of safety innovation given a constant team size.

jeffbee 4y ago
Tesla engineers are currently doing post-commit review of Twitter source code. Focus is the last thing I would credit them with.
- mensetmanusman 4y ago
  All of them?
- jfabre 4y ago
  do you have data to back this claim?
  - mynameisvlad 4y ago
    Have you been keeping up with the Twitter deal? This was covered Friday. https://www.bloomberg.com/news/articles/2022-10-27/tesla-engineers-visit-twitter-office-to-review-code-for-musk https://www.bloomberg.com/news/articles/2022-10-27/tesla-eng... And the corresponding HN thread: https://news.ycombinator.com/item?id=33365065 https://news.ycombinator.com/item?id=33365065
    - jfabre 4y ago
      I have not been following the whole thing super closely, no. Thank you for the links.
- eigenrick 4y ago
  I don't think it was your intent, but your statement makes it seems like all Tesla engineers are looking at Twitter code. I bet this number is closer to 4. Tesla has ca. 1000 software engineers working in various capacities. The ca. 300 that work on car firmware and autonomous driving are probably not participating in the Twitter drama.
  - ClumsyPilot 4y ago
    4 people is enough to review source that was written by thousands of engineers at twitter? thats not even enough for architecture review
    - eigenrick 4y ago
      I don't think the goal is to review all Twitter source. That should be the job of the (new?) development team. I think the goal was to look at the last 6 months of code, especially the last few weeks, for anything devious.
YZF 4y ago
Tesla's cameras often get blocked by rain or blinded by the sun or not see that well in the dark. It's really hard to imagine those cameras replacing the ultrasonic sensors which do a pretty good job at telling you where you are when you're parking etc. I can't see how the camera is going to detect an object at pitch dark and estimate the distance to it better than an ultrasonic sensor. But hey, if people ding their cars it's more revenue. The bottom line seems to be that the part shortages would have slowed production and cost cutting. The rest of the story seems like a fable to me. It was pretty clear Tesla removed the radar because it couldn't get enough radars. The interview didn't really impress me. I'm sure Andrej is bound by NDA and not wanting to sour his relationship with Tesla/Elon but a lot of the answers were weak. (On Tesla and some of the other topics, like AGI).
- galangalalgol 4y ago
  One interesting side effect of only using visual sensors is that the failure modes will be more likely to resemble human ones. So people will say "yeah, I would have crashed in that situation too!". With ultrasonic and radar and ladar it may make far fewer mistakes but it is possible they might not be the same ones people make, so people will say "how did it mess that up?"
  - mbreese 4y ago
    Sadly, that’s the worst way to actually design the system. I’d rather have two different technologies working together, with different failure modes. Not using radar (especially in cars that are already equipped) might make economic sense to Tesla, but I’d feel safer if visual processing was used WITH radar as opposed to instead of radar. I also expect an automated system to be better than the poor human in the drivers seat.
    - xodjmk 4y ago
      You have to eventually decide to trust one or the other, in real-time. So having multiple failure modes doesn't solve the problem entirely. This is called 'Fusion', meaning you have to fuse information coming from multiple sensors together. There are trade offs because while you gain different views of the environment from different sensors, the fusion becomes more complicated and has to be sorted out in software reliably in real-time.
syntaxing 4y ago
Uhh highest accuracy and bandwidth for what? You can have a camera that can see piece of steak at 100K resolution at 1000 FPS but doesn’t mean you can use a camera to replace a thermometer. Blows my mind how people eat up that cameras can replace every sensor in existence without even entertaining basic physics. ML is not omnipotent.
- et2o 4y ago
  For the specific task of (for example) cooking a steak it’s not hard to envision a computer vision algorithm coupled with a model with a some basic knowledge of the system (ambient temperature, oven/stove temperature, time cooking, etc.) doing an excellent job.
  - krapht 4y ago
    No, I can't envision this. Surface texture alone will not tell you if meat is cooked. There is no getting around the temperature probe. Now, simple color matching models are used in some fancy toasters on white bread to determine brownness. That's the most I've ever seen in appliances...
  - katbyte 4y ago
    You cannot use vision to see the state of the side of a steak touching the pan, nor the internal temperature.
freejazz 4y ago
> "Team focus on vision which is by far the highest accuracy and bandwidth sensor allows for a faster rate of safety innovation given a constant team size." By hiding the ball that you are starting from a much more unsafe position
ClumsyPilot 4y ago
> vision which is by far the highest accuracy and babdwidth They are literally the least accurate of all sensors. Radar tells you distance and velocity of each object. Lidar tells you size and distance of each object. Ultrasonic tells you distance. Cameras? They tell you nothing! Everything has to be inferred. Have you tried image recognition algorythms? I can recognise a dog from 6 pixels, the image recognition needs hundreds, and has colossal failures. We have no grip on the results AI will produce and no grasp on it's spectacular failures. Driving will have to be solved without AI

jeffbee 4y ago

Tesla engineers are currently doing post-commit review of Twitter source code. Focus is the last thing I would credit them with.

mensetmanusman 4y ago
All of them?
jfabre 4y ago
do you have data to back this claim?
- mynameisvlad 4y ago
  Have you been keeping up with the Twitter deal? This was covered Friday. https://www.bloomberg.com/news/articles/2022-10-27/tesla-engineers-visit-twitter-office-to-review-code-for-musk https://www.bloomberg.com/news/articles/2022-10-27/tesla-eng... And the corresponding HN thread: https://news.ycombinator.com/item?id=33365065 https://news.ycombinator.com/item?id=33365065
  - jfabre 4y ago
    I have not been following the whole thing super closely, no. Thank you for the links.
eigenrick 4y ago
I don't think it was your intent, but your statement makes it seems like all Tesla engineers are looking at Twitter code. I bet this number is closer to 4. Tesla has ca. 1000 software engineers working in various capacities. The ca. 300 that work on car firmware and autonomous driving are probably not participating in the Twitter drama.
- ClumsyPilot 4y ago
  4 people is enough to review source that was written by thousands of engineers at twitter? thats not even enough for architecture review
  - eigenrick 4y ago
    I don't think the goal is to review all Twitter source. That should be the job of the (new?) development team. I think the goal was to look at the last 6 months of code, especially the last few weeks, for anything devious.

YZF 4y ago

Tesla's cameras often get blocked by rain or blinded by the sun or not see that well in the dark. It's really hard to imagine those cameras replacing the ultrasonic sensors which do a pretty good job at telling you where you are when you're parking etc. I can't see how the camera is going to detect an object at pitch dark and estimate the distance to it better than an ultrasonic sensor. But hey, if people ding their cars it's more revenue. The bottom line seems to be that the part shortages would have slowed production and cost cutting. The rest of the story seems like a fable to me. It was pretty clear Tesla removed the radar because it couldn't get enough radars. The interview didn't really impress me. I'm sure Andrej is bound by NDA and not wanting to sour his relationship with Tesla/Elon but a lot of the answers were weak. (On Tesla and some of the other topics, like AGI).

galangalalgol 4y ago
One interesting side effect of only using visual sensors is that the failure modes will be more likely to resemble human ones. So people will say "yeah, I would have crashed in that situation too!". With ultrasonic and radar and ladar it may make far fewer mistakes but it is possible they might not be the same ones people make, so people will say "how did it mess that up?"
- mbreese 4y ago
  Sadly, that’s the worst way to actually design the system. I’d rather have two different technologies working together, with different failure modes. Not using radar (especially in cars that are already equipped) might make economic sense to Tesla, but I’d feel safer if visual processing was used WITH radar as opposed to instead of radar. I also expect an automated system to be better than the poor human in the drivers seat.
  - xodjmk 4y ago
    You have to eventually decide to trust one or the other, in real-time. So having multiple failure modes doesn't solve the problem entirely. This is called 'Fusion', meaning you have to fuse information coming from multiple sensors together. There are trade offs because while you gain different views of the environment from different sensors, the fusion becomes more complicated and has to be sorted out in software reliably in real-time.

syntaxing 4y ago

Uhh highest accuracy and bandwidth for what? You can have a camera that can see piece of steak at 100K resolution at 1000 FPS but doesn’t mean you can use a camera to replace a thermometer. Blows my mind how people eat up that cameras can replace every sensor in existence without even entertaining basic physics. ML is not omnipotent.

et2o 4y ago
For the specific task of (for example) cooking a steak it’s not hard to envision a computer vision algorithm coupled with a model with a some basic knowledge of the system (ambient temperature, oven/stove temperature, time cooking, etc.) doing an excellent job.
- krapht 4y ago
  No, I can't envision this. Surface texture alone will not tell you if meat is cooked. There is no getting around the temperature probe. Now, simple color matching models are used in some fancy toasters on white bread to determine brownness. That's the most I've ever seen in appliances...
- katbyte 4y ago
  You cannot use vision to see the state of the side of a steak touching the pan, nor the internal temperature.

freejazz 4y ago

> "Team focus on vision which is by far the highest accuracy and bandwidth sensor allows for a faster rate of safety innovation given a constant team size." By hiding the ball that you are starting from a much more unsafe position

ClumsyPilot 4y ago

> vision which is by far the highest accuracy and babdwidth They are literally the least accurate of all sensors. Radar tells you distance and velocity of each object. Lidar tells you size and distance of each object. Ultrasonic tells you distance. Cameras? They tell you nothing! Everything has to be inferred. Have you tried image recognition algorythms? I can recognise a dog from 6 pixels, the image recognition needs hundreds, and has colossal failures. We have no grip on the results AI will produce and no grasp on it's spectacular failures. Driving will have to be solved without AI