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Comment by CasillasQT | Hacker News Reader

Did you see the presentation from Karpathy? Tesla goes for a general vision only end-to-end deep AI model that could in theory get rolled out everywhere on earth with enough training and a good approach for fast edge-case solving, which they showed how this can be accomplished. All the other players try to solve this with lidar and cars that cost around 500k to build and they have pretty much 0 data except for the maps they generate themselves. This approach will never solve L5. Tesla may need another 10 years, but they are so far out of reach of the other players that you cant even call them competition at this point.

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Waymo have all the StreetView data - practically every road in the country, complete with lidar and high-precision GPS. All labelled in detail by recaptcha 'volunteers'

And lidar wouldn't be expensive if manufactured in automotive volumes. Certainly less, per vehicle, than Musk charges people for "full self driving" at the moment.

California allows autonomous vehicles to be tested on the road, so long as every disengagement is reported (along with total miles driven etc). Waymo is testing, reporting mileage and disengagements. So are Toyota, Nvidia, Mercedes, BMW, Cruise, Lyft and Apple.

Guess who's too shy to have driven a single autonomous mile in California, where faults have to be reported? That's right, Tesla!

Tesla might be able to make vision-only driving work. But Musk has been promising deadlines then failing to achieve them for years. They've put all their chips on 'no lidar' and they've had a bunch of problems that lidar could trivially solve - such as detecting a fire truck or concrete barrier right in front of the vehicle. So it's far from obvious to me that they've got a winning approach.

> with enough training and a good approach for fast edge-case solving

Apparently they have neither as they have missed their deadline three years ago and continue to miss it every year since.

> All the other players try to solve this with lidar and cars that cost around 500k to build

Citation needed?

> Tesla may need another 10 years

What has you this pessimistic? Tesla promises full self driving by the end of the year every year. Are you saying a random commenter on the internet knows more about the state of their AI then they do?

So catching up to Comma.ai then (which is also Level 2), except that there is no driver monitoring that makes sure that the driver has their eyes on the road. That can't be good, but I guess once again Comma.ai and similar systems were right again.

> This approach will never solve L5.

Again, Tesla advertised FSD as Level 5 and ready for completion with the robotaxis for 2020. Sounds like it was falsely advertised right?

Except they’re nowhere near using an end-to-end model - their perception and planning stack still looks the same as every other self driving company, but with incredibly degraded performance since they’re using nothing but cameras and flakey maps. Tesla reverse engineers have uncovered the internals of the FSD beta - you can check it for yourself.

“they have pretty much 0 data except for the maps they generate themselves”

What do you mean by this? You realize the bottleneck for training data generation is always human labeling, not raw amount of data, right?

> All the other players try to solve this with lidar and cars that cost around 500k to build

Comma has been using that approach from the start with a cheap smartphone-like device.

> Tesla goes for a general vision only end-to-end deep AI model that could in theory get rolled out everywhere on earth with enough training and a good approach for fast edge-case solving,

No amount of "training" can fix the problem of "AI" not being AI

These people have very poor idea what they are talking about when they say the phrase "artificial intelligence." It's a clear misuse

Your comment reminds me of the AI effect[0]. AI doesn't mean AGI.

[0] https://en.wikipedia.org/wiki/AI_effect

Folks understand L1 and L5. The levels between are such a blur and mix-match of things that I don't think anything is even accomplished having these levels. I agree with you, from what I have seen Tesla is far ahead and rapidly progressing using the right approach of training NNs while having a human behind the wheel.

The levels are even faulty. They assume that geofencing is required and so geofencing is hard coded into the levels.

> ...the right approach of training NNs while having a human behind the wheel.

Can someone please help me across a conceptual bridge here?

Is there some work I'm not familiar with that shows humans use the biologically-equivalent NNs used by Tesla to accomplish L5-grade driving? I'm not talking about doing it quickly, I'm at this point interested in Tesla or anyone else for that matter demonstrating doing it at all, at any speed. It can be at an agonizingly-slow 0.25 km/hour and that would be fine.

I'm having trouble bridging between L5-the-destination and NNs-are-definitely-the-way-to-get-there. This sounds an awful lot like saying NNs-are-the-Moravec's-Paradox-solution, and I'm not sure I've read conclusively how that can be true. I can accept it as a hypothesis, but other than actually trying it out like Tesla is doing, I haven't read why it is such a strong conjecture.

It sounds from articles like [1] and [2] Tesla is only just now starting to really get into applying NNs more broadly to the problem space, and the prior years were mostly focusing on more conventional machine vision techniques and getting clean data for NNs to ingest. But I've yet to read a convincing explanation for how ML will functionally solve even the subset of Moravec's Paradox needed to accomplish L5. I grant that it will solve a facsimile of the paradox, but I feel it is arguable if it will be a reasonable facsimile. That sounds an awful lot like, "we'll brute force throw enough training data at it to reach 'reasonable facsimile' level", and I'm cautious when I hear of brute forcing as a strategy for arriving at R&D results.

[1] https://insideevs.com/news/466239/tesla-migrating-to-neural-...

[2] https://electrek.co/2021/02/08/tesla-looks-hire-data-labeler...

So happy to meet someone else who gets it on hacker news.

What I get is Tesla selling a pipe-dream "next year" that may take a decade or two to solve. FSD is a novelty at best right now (cruise control is handy), and a con at worst.

I didn’t comment on the sales strategy, just the the technical approach. The technical approach is correct - the marketing is dodgy. But then again this is how the game of hyper capitalism is played.

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