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Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

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Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#231

Interesting that they don't have a full 3D world model. I'm certainly not a machine learning expert. I'm still amazed the route from image recognition to a 2D map of "what's drivable" to autonomous driving is so direct. One would expect to hit a ceiling really soon with that approach. To me it seems we're still in really early days.

They're doing 3D for the road path, and even predicting it beyond corners:

https://youtu.be/Ucp0TTmvqOE?t=8137

And later in the video they show 3D reconstruction from cameras and saying they use it in the car.

Watching the full talk is recommended if you have the time (talk starts around 1:10:00 in the video)

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#232
post #223
post #180

Earlier quoted context omitted.

Thanks for the reply. Living in an apartment is not a big issue for me... we have two, live in an apartment with no charger. That's interesting that you relate something you do in the AV industry to not ever getting a car... what's that about? I do think that it's possible in the future the majority of people will never need to own a car.

I think for a good majority of people especially in America will go along the lines of "If it ain't broke, don't fix it" for gas cars. Regardless, I think Ghost Locomotion and Comma.ai have a lot of potential for what they're doing now. I think they'll coincide with fully driverless cars like Cruise, Waymo, or Aurora. Regardless of if they're electric or not (I think electric cars will be more heavily adapted if we u…

>I can spend more time having the perspective of a person in...

This is awesome. I've always found that only by really experiencing the future (or some part of the leading edge that is soon going to become the future for most people) you gain much more understanding of it, ahead of others, and can apply it in your life planning. It would be worth living this way even if only temporarily just to get that as you say perspective... Not ready to give up driving forever, heh, but it I might have to try a short sample of that non-driving lifestyle sometime.

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#233
post #227

Earlier quoted context omitted.

To me, the fatal flaw of HBP (or USA's competing HBI) is that they are akin to "cargo cult science", the idea that we can replicate the superficial structures to a significant enough degree that they system they impart will suddenly somehow become activated. But just like the Melanesians with their coconut-shell headsets, there won't be anyone listening on the other end...

If we replicate a car with a sufficient accuracy it will start. I don’t see why this wouldn’t apply to any other piece of machinery, including a brain. HBP should have tried a simpler task first, e.g. replicate a fruit fly’s brain.

Having replicated the fruit fly's brain, what are you going to do?

Replicate the rest of the fruit fly so that you can install the brain in it? And then replicate the world so that your software-simulated fruit fly has a natural context in which to operate?

Or just stimulate the brain with random inputs not associated with any real-world stimulus?

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#234
post #68
post #63

Earlier quoted context omitted.

You are using the 9s term wrong. It is supposed to be used with unitless numbers. Not crashes per hour or per mile. Silly example of why: humans are zero nines of reliability if you talk about crashes per parsec.

> Not crashes per hour or per mile. But that's not what I measured. I measured safe minutes of driving as a ratio to unsafe minutes. Which is a unitless number. For 7 9's I assumed a crash had a 5 minute lead-in of unsafe driving before the actual crash, and that average driving speed was 30 mph. If you assume the bad driving is 30 seconds (for example the accident in Tuscon the Uber car saw the pedestrian around 30…

Thanks for the clarification. I haven't checked your numbers but they seem reasonable.

FWIW, I think deaths, regardless of fault, is probably the best number for an apples-to-apples comparison. If the Uber car were driven by a person without a dash-cam, then there is no way they would have been ruled at fault.

Similarly, I have been involved in 5 collisions (most were not my fault). Of the 5, 3 were reported to insurance and only 1 was recorded by the police (For 1 other the police were called, but the dispatcher (on the non-emergency police line) said "Are both cars driveable? (yes) Is anyone injured? (No), then don't bother us!" (By comparison, in California even a single car collision on private property must, in theory, be reported to the police).

Deaths, at least, will get recorded by the CDC, if nobody else.

[edit]

However, I don't think that deaths are necessarily the best number for considering "failures" all deaths are in some way a failure, but most failures do not result in deaths. Nobody even had to see a doctor for the 5 collisions I mention. I've fallen asleep at the wheel and crossed the center-line without getting in an accident, &c. humans make a lot of mistakes, but most of them do not result in a collision, and most of the ones that result in a collision do not result in a death.

I also think it's inevitable that computers will become better at driving than not just the mean or median driver, but 90th percentile or more, at which point it would be immoral to not, in some way, encourage self-driving cars over human-driven cars. My guess is that we are less than a decade away from that point, but I could be wrong.

Certainly the lack of a safety-culture in the automotive field (as compared to say, avionics) is a huge barrier to be overcome, and that's a social hurdle, not a technical one and I can only make wild-ass guesses as to when that will change.

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#235
post #227

Earlier quoted context omitted.

If we replicate a car with a sufficient accuracy it will start. I don’t see why this wouldn’t apply to any other piece of machinery, including a brain. HBP should have tried a simpler task first, e.g. replicate a fruit fly’s brain.

Having replicated the fruit fly's brain, what are you going to do? Replicate the rest of the fruit fly so that you can install the brain in it? And then replicate the world so that your software-simulated fruit fly has a natural context in which to operate? Or just stimulate the brain with random inputs not associated with any real-world stimulus?

I’m pretty sure simulating fly’s neural inputs would be a far easier task than simulating its brain. After verifying it works correctly we would proceed to simulating a more complex brain, say a frog. And so on.

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#236

Earlier quoted context omitted.

While LIDAR's are certainly 'better' from a technological standpoint than not having anything, from a business standpoint it's less clear. LIDAR's are cheap, but not cheap enough yet to not seriously affect the bottom line if you put them into every car. It also will kill the resale price of cars without it, which in turn hurts the companies image and stock price.

If you are first to market with a level 4 autonomous taxi, unit economics will likely be great regardless of whether you put LIDAR or cameras in.

Yes, but the current reality is they need to pay for the hardware, and still make a profit, without any guarantee of that autonomous taxi market.

It all depends what you see are the chances of the autonomous taxi market developing within the lifetime of these cars.

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#237
post #204

Earlier quoted context omitted.

> There is so much duplication going on In the self-driving world, the duplication is necessary - different companies are taking different directions, and nobody really knows which will work out. In the ML hardware world, the duplication is mostly unnecessary. People are developing their own inference hardware ASIC's because they're relatively simple (compared to designing a CPU from scratch, designing a TPU is prett…

I do research in ML hw field: there are currently a couple hundred designs to run a convolutional NN inference. A couple of dozen have been built. They have pretty different underlying technologies (CMOS, floating gate, ReRAM/memristors, etc), different ideas (systolic arrays, analog crossbars, cache organization, lookup tables, data reuse, TDM, using spikes, etc), wildly different power (from microwatts to hundreds…

The duplication I see in the commercial world in ML inference hardware is in designs similar to the TPU... So a big ~128x28 accumulating mat-mul array, with enough memory throughput to get one operand in and out fast, and enough cache to store the other operand (weights) and switch between which weights are used so the mat-mul array can very efficiently do larger matrix sizes.

Also lookup tables for a bunch of activation functions.

That basic design can efficiently implement nearly any neural net architecture as long as the layer sizes are at least 128x128 and fixed point is okay.

The other exotic designs you suggested are more academic research things, and not yet deployed at scale in anyone's datacenters.

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#238
post #231

Interesting that they don't have a full 3D world model. I'm certainly not a machine learning expert. I'm still amazed the route from image recognition to a 2D map of "what's drivable" to autonomous driving is so direct. One would expect to hit a ceiling really soon with that approach. To me it seems we're still in really early days.

They're doing 3D for the road path, and even predicting it beyond corners: https://youtu.be/Ucp0TTmvqOE?t=8137 And later in the video they show 3D reconstruction from cameras and saying they use it in the car. Watching the full talk is recommended if you have the time (talk starts around 1:10:00 in the video)

Thanks, seems my original comment is wrong then!

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#239

Fun fact for all of you: Some time ago (around ~10 years) this guy (the presenter) was internet famous for being a Rubik cube speed solver and making tutorials and videos about that: https://www.youtube.com/watch?v=609nhVzg-5Q

I'll always know him as badmephisto. In a recentish reddit AMA, he says he still keeps a cube on his desk so he can practice a bit and not forget his algorithms.

Can you provide the link to that AMA?

Re: Andrej Karpathy talks about how Tesla's NNs are structured and trained [video]

#240
post #217

Earlier quoted context omitted.

Competition is good

Choice is good, alternative implementations are good. But I think competition is bad. It is wasteful and antisocial.

Competition is good and directly leads to the progress we’ve seen in the western world.
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