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> the adoption of end-to-end AI within the robotics and automotive community remains low What do you think how else lane assistance, traffic sign recognition,

> the adoption of end-to-end AI within the robotics and automotive community remains low What do you think how else lane assistance, traffic sign recognition, highway and traffic jam assist, emergency braking or pedestrian or any traffic object detection and tracking is actually implemented? All of them are backed up in one way or another by neural networks, which by today definition is I guess "AI", and this has been the case for at least ~10 years in all major car manufacturers. That said, I don't believe we will see Transformers architecture anytime soon implemented in resource and $$$ constrained environments such as automotive industry.

a2128 2y ago
I don't know if automotive is that resource and money constrained. People will spend tens of thousands on upgrades if they see the value, and there's plenty of space in there to put a powerful GPU cluster. But of course reliability constraints will be stricter
- menaerus 2y ago
  Generally speaking, hardware found in commercial vehicles is very cheap and therefore constrained - e.g. slow CPUs and slow memory, low amount of memory, limited (flash) storage, and technology is generally lagging behind - you would for example still be able to find higher-end cars that had been produced recently that are unable to stream the content from a smartphone to an infotainment system without using a USB cable. This is a feature we had elsewhere for at least a decade. There are other such examples but when compared to the commodity smartphone hardware, automotive hardware is lagging in many respects. Both BMW and Mercedes-Benz produce ~2.5M vehicles/year so I guess when you need to scale your production out to such quantities, saving even a few cents per component means a noticeable margin. Competition today is bigger than ever with Chinese companies entering the market, especially for European car makers, so I envision that cutting costs will become even more important than it was before.
anon373839 2y ago
I took "end-to-end AI" here to refer to whole systems trained end-to-end as black boxes (pictures in, throttle/steering/braking signals out). Which, to me, is not something I would ever want to see deployed in safety-critical applications.

a2128 2y ago

I don't know if automotive is that resource and money constrained. People will spend tens of thousands on upgrades if they see the value, and there's plenty of space in there to put a powerful GPU cluster. But of course reliability constraints will be stricter

menaerus 2y ago
Generally speaking, hardware found in commercial vehicles is very cheap and therefore constrained - e.g. slow CPUs and slow memory, low amount of memory, limited (flash) storage, and technology is generally lagging behind - you would for example still be able to find higher-end cars that had been produced recently that are unable to stream the content from a smartphone to an infotainment system without using a USB cable. This is a feature we had elsewhere for at least a decade. There are other such examples but when compared to the commodity smartphone hardware, automotive hardware is lagging in many respects. Both BMW and Mercedes-Benz produce ~2.5M vehicles/year so I guess when you need to scale your production out to such quantities, saving even a few cents per component means a noticeable margin. Competition today is bigger than ever with Chinese companies entering the market, especially for European car makers, so I envision that cutting costs will become even more important than it was before.

anon373839 2y ago

I took "end-to-end AI" here to refer to whole systems trained end-to-end as black boxes (pictures in, throttle/steering/braking signals out). Which, to me, is not something I would ever want to see deployed in safety-critical applications.