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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]

#161

Post by acollins1331 »

jacquesm wrote: post #105

The competition in this space is great but I can't help but wonder what would happen if instead all these companies pooled their resources and went after the goal collectively. There is so much duplication going on and the paths do not seem to me - as an outsider - to be all that divergent, which is usually a pre-condition for having a lot of independent efforts one of which will succeed. It's as if everybody wants t…

I think the diversity you see in cameras and lidar placement and existence is worth it enough to have different paths forward. Tesla seems insistent that it can be done sans lidar. It's definitely worth it to see which approach works best.

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

#162

Post by mkolodny »

jacquesm wrote: post #105

The competition in this space is great but I can't help but wonder what would happen if instead all these companies pooled their resources and went after the goal collectively. There is so much duplication going on and the paths do not seem to me - as an outsider - to be all that divergent, which is usually a pre-condition for having a lot of independent efforts one of which will succeed. It's as if everybody wants t…

Fortunately, some companies do share a significant amount of what their cars have learned so far. Uber publishes a ton of papers about their self-driving research [0][1]. Waymo released an open autonomous driving dataset, and publishes papers as well [2][3].

Of course, papers and data aren't code. But I think a lot more is being shared than people realize.

[0] https://eng.uber.com/author/raquel-urtasun/ [1] https://eng.uber.com/research/?_sft_category=research-self-d... [2] https://waymo.com/open [3] https://arxiv.org/pdf/1812.03079.pdf

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

#163

Post by mrep »

codeulike wrote: post #136
jacquesm wrote: post #116

Earlier quoted context omitted.

A car is a hardware device as well, and an electric car does not have the kind of power budget that allows you to throw oodles of standard pieces at it without paying a severe penalty in range.

Compared to the energy needed to move the car, everything else is pretty irrelevant. Power hungry features like Heating/AC only makes a few % difference to range.

From autonomy day hacker news comment: "Pegasus consumes about 500Watts, compared to under 100 Watts for Tesla's FSD computer. Elon in particular emphasized the performance per watt (as it's always possible to cram more chips to increase performance if you ignore cost and power consumption). The comparison made in the video: 500Watts for an hour consumes about 2-3 miles of range. In a city in slow traffic, going 12mph, that's a significant range reduction. So you might have a 10% improvement in range for the Tesla ASIC in low speed conditions" [0].

Also, Tesla is/was planning on running these chips even while not in autonomous mode in order to spot new scenarios which it can then record what the car sees and what the human does in order to collect a lot more unique road scenarios to train their models with.

[0] Credit Robotbeat: https://news.ycombinator.com/item?id=19729743

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

#164

Post by barney54 »

codeulike wrote: post #136
jacquesm wrote: post #116

Earlier quoted context omitted.

A car is a hardware device as well, and an electric car does not have the kind of power budget that allows you to throw oodles of standard pieces at it without paying a severe penalty in range.

Compared to the energy needed to move the car, everything else is pretty irrelevant. Power hungry features like Heating/AC only makes a few % difference to range.

The compute for autonomous driving could be 10% of the total energy budget of the car. https://cleantechnica.com/2017/10/13/autonomous-cars-shorter...

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

#165

Post by freediver »

mdorazio wrote: post #29
freediver wrote: post #26

Earlier quoted context omitted.

You are right this approach is scary, and it is astonishingly innacurate (im a tesla owner). However the reason are not eight cameras. You should be able to drive fine with just one camera (thought experiment: could you drive a car 1000 miles from you, just by seeing what the driver of that car would see, no extra cameras, sensors or lidars?).

One stereoscopic ultra-HDR 4K camera would be fine... if it was backed by a strong AI. To even suggest that ML is anywhere wen remotely close to this level is the height of hubris.

Not even stereoscopic, you could still drive with one eye shut.

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

#166

Post by SloopJon »

Udik wrote: post #101
chronic739i wrote: post #90

Earlier quoted context omitted.

> At Tesla's scale and priorities, they'd probably be less keen on using external cloud providers. By hardware-hours, Tesla is hardly one of the top companies training deep networks. Planet Labs (satellite imaging), Netflix, Pornhub, to name a few. What's the info they'd leak to the Google consultants? How much data or TPUs they're using? This is practically public information.

What does Netflix do with NNs?

Their own page says, "our recommendation algorithms ... learning characteristics that make content successful ... optimize the production of original movies and TV shows ... optimize video and audio encoding, adaptive bitrate selection, and our in-house Content Delivery Network ... and advertising".

https://research.netflix.com/research-area/machine-learning

Here's another article on the subject:

https://becominghuman.ai/how-netflix-uses-ai-and-machine-lea...

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

#167

Post by Geee »

adamnemecek wrote: post #7

The trick for level 5 is learning the mapping between the lidar point cloud and the video stream. It’s the best of both worlds.

2D to 3D transform is simple trigonometry (using stereo / motion) and should be possible to learn without lidar. I think this is already a solved problem. One option though is to add lidars in random Teslas (e.g. 1/1000) to help with the labeling / learning.

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

#168

Post by _cs2017_ »

visarga wrote: post #77
spectramax wrote: post #5

Earlier quoted context omitted.

Without meaning offense to Sam, I thought he was an investor / YC head. What credentials does he have to be at OpenAI?

Teaching the CS231n course at Stanford, for one.

Andrej Karpathy taught cs231n. The question is about Sam Altman who didn't teach anything related to NL. He's on the board of OpenAI because he was one of its founding investors.

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

#169

Post by nsilvestri »

protomikron wrote: post #130

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.

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

#170

Post by Pazzaz »

new_realist wrote: post #20
mkagenius wrote: post #2

Oh he's no longer with OpenAI? Sam Altman must be worried about this..

Elon Musk poached him, and for that was kicked off the OpenAI board.

I've never heard that that was the reason for Elon leaving the OpenAI board. The official announcement said "As Tesla continues to become more focused on AI, this will eliminate a potential future conflict for Elon." Do you have a source?
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