I thought it was telling that Andrej immediately "reframed" the question because Lex asked the "wrong question". This is a classic evasion technique one learns from experience and/or media training. Lex's comment immediately after was a clever and gentle dig at Andrej's response. It seemed like all the "full cost" negatives Andrej mentioned were related to Tesla's ability to execute, and not what would actually produ…
That was hilarious. Basically (unless this needs a reframing/realignment/repositioning/reorienting):
Q: "are less sensors less safe/effective?"
A: "well more sensors are costly to the organization and add more tech debt so safety is orthogonal and not worth answering".
I thought it was telling that Andrej immediately "reframed" the question because Lex asked the "wrong question". This is a classic evasion technique one learns from experience and/or media training. Lex's comment immediately after was a clever and gentle dig at Andrej's response. It seemed like all the "full cost" negatives Andrej mentioned were related to Tesla's ability to execute, and not what would actually produ…
It was an ominous answer. We really should be focusing on what is the best solution and trying to solve price issues through existing techniques e.g. economies of scale, competition, miniaturisation. Instead they are trying to build whatever solution they can that fits in a pre-defined cost window. Except this isn't a new phone or sneakers we are trying to take to market it's something that will directly impact peopl…
You can get to this conclusion if you're sure Andrej is lying, and that the risks cited are smoke; but only then. BTW, I've upgraded my sneakers after a couple falls on a rough beach with tangled driftwood (drift trees, really) proved their cheap too-slick surface had real world consequences. I was lucky not to break a bone. I'm going to bet he isn't lying, but I can understand someone making the opposite bet, market competition being market competition.
I didn't find his answers particularly convincing. His answer focused on costs mainly, and how "the best part is no part". We have already seen multiple accidents caused by camera's limitations [1] which would not have happened if Tesla used Lidars. Cameras have poor dynamic range and can be easily blinded by bright surfaces. While it is true that humans do fine with only eyes, our eyes are significantly better than…
You're right to note the advantages of lidar and (narrow range of) contrast problem for cameras (they arent eyes.) This is why the Uber human driver shouldn't have trusted the machine at night, in particular.
But you still have to address his system argument, which was that adding geegaws that added little would actually increase overall risks along the supply chain (plus maintenance) while distracting the team and adding more risk that way, for very little apparent (but only apparent) gain. The team does believe that they'll get to better than human driving, and do that without lidar.
I thought it was telling that Andrej immediately "reframed" the question because Lex asked the "wrong question". This is a classic evasion technique one learns from experience and/or media training. Lex's comment immediately after was a clever and gentle dig at Andrej's response. It seemed like all the "full cost" negatives Andrej mentioned were related to Tesla's ability to execute, and not what would actually produ…
I think the key point he‘s trying to make is that the size of the fleet is more important than the quality of the sensor. The risk would be reduced by a better system and he seems to be convinced that rolling out vision to more and cheaper cars would get you there.
Full on agreement. There are literally videos of Teslas smashing into stationary vehicles on the highway at night using only vision camera for FSD. No way any rational actor could claim the visible pixel space is sufficient in that scenario compared to LIDAR, Radar, etc
It's funny you use Radar as an example of 'good sensor' while it is well known that most or maybe almost all?) of the stationary vehicle accidents you're talking about happened because of Radars inability to detect a stationary obstacle. On the other hand, RGB data does have that information, we use it everyday to avoid obstacles, even under foggy and rainy conditions (I'm no LIDAR expert but I know it sucks in rainy…
You mean some radars inability to detect stationary obstacles. Clutter rejection has a lot of more sophisticated algorithms to apply with greater compute power to throw at the problem.
This whole question about the vision boils down to "humans don't need it so cars should not need it too" the problem with this statement is that humans does not have wheels to move around, they have legs, but wheels are ridiculously simple compared to 4 legs tapping 160km/h on a highway. Same for birds - they also does not need jet engines to fly around, but imagine Airbus A380 flapping its wings and what kind of com…
No, he precisely said that the difference Lidar made was tested, and the delta (difference made) was quite small; not enough to outweigh the downsides. Elon has noted that humans do well, and that's relevant, but that observation was also tested, re lidar.
i'm not sure if i buy his argument that the "delta is not big enough." i have some experience with realtime ai systems and i've noticed something interesting about them. they have a non-smooth capability curve, where they can demonstrate proficiency in activities that in regular computer programs or people would imply a complete and continuous path of capability that has been mastered to achieve the demonstration, bu…
They do "fall off the edge of the world" a lot; but so do human neural networks; I've seen a bad crash as a result of a human simply pulling out of a driveway right in front of a motorcycle, 'cause they're rare. She had tagged the motorcycle as a bike while it was farther away, then boom. Her interpolation (while checking the other side) didn't work, and her averaging over uncertainty didn't work either because motorcycles are rather rare up north, they aren't the average vehicle. I've made a similar error re a kid on a wall (he suddenly jumped directly into the bikepath) but managed to avoid him (my bike zoomed to his left and I tumbled past his right. He wasn't hurt, although I got a severe wrist sprain from throwing the bike to the left.) As a driver I behave very differently around kids on walls, now. It was just an edge case I'd never encountered, and I didn't have enough data to calculate under uncertainty.
Seems like a very political answer from Andrej. Of course he’s not going to outright say “yeah, we’re prioritizing the profit margin over accuracy and safety considerations” if he wants to keep his job, but that seems to be the short of it. Others may choose to follow, at least in the short term, but it won’t be because of “entropy” making the system worse (you can always build a model without a data source and then…
The entropty he's taking about comes from many sources, in particular opening yourself up to maintenance or supply side errors, and just overloading team attention (always at a premium) for no net return. It's not just CPU cycles (although that's part of it, that hardware could be doing something else useful.)
I still suspect it's because they need to preserve compute resources for vision processing. Sensor fusion is likely eating up too much of their current HW and limiting their progress in other areas. I suspect Tesla will have to admit they need to upgrade the current HW before they ever 'solve' FSD.
The amount of compute that sensor fusion uses is miniscule compared to running a NN or computing stereo depth maps. Sensor fusion runs in the background of your phone the whole time to power things like [0] for example. 0. https://sensor-js.xyz/demo.html
Well, yes and no. Integrating the data and adjudicating conflicts between sensors is a real task, too. Also having just two opinions doesn't necessarily help if they conflict, and the lidar is the thinnest source of data. How do you coin flip that? You likely end up just discarding the Lidar's conflicting opinion.