251–260 of 441 posts
Fully vested and widely respected. Great position to be in.
> widely respected Except he failed to achieve what he claimed he could.
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Can you blame us? The messaging from the company is both inconsistent and insane. I just call the built-in autosteer wizard "the lane keeper" now because that bypasses the terminology confusion. BTW it works reasonably well on the highway in good conditions. It's perfect as a way of taking a short break and stretching all 4 of my limbs during a drive, but that's the only reason I use it.
They should just rename Autopilot to some generic thing (but not Autopilot haha) like every other companies adaptive cruise control. Then use Autopilot for FSD.
The autopilot part of Tesla has never made much sense. Is Tesla the electric car company, or is it the luxury car company? Either way, why does the power train (EV or ICE) come into play at all? Not only that, but Tesla has played the Innovator's Dilemma game from the position of the upstart financially, but targeted the segment of the market that incumbents will defend to the death (luxury cars). Tesla could have go…
The incumbents will defend that market segment to their death indeed. Tesla is pulling a 30% gross margin on their EV's, while legacy auto looses money on their EV's. I don't see legacy auto being capable of refactoring their cars, manufacturing processes and business models fast enough to survive. The fall can cascade quickly, as the incumbents are stuffed with debt. When sales of ICE fall due to the growing EV segm…
That doesn't make any sense. "Legacy auto" knows how to make cars. To their final assembly factories there's not a huge difference between a BEV, PHEV, or an ICE drive train. So long as they feed in components they get cars out. They definitely know how to get components made to feed into their factories.
That's a place where they have an advantage over Tesla. They can make BEVs that break even or lose money because they have a whole line of ICE cars making a profit. Tesla only has their up market BEVs to make their money.
Tesla doesn't have a moat around BEVs. Now that "legacy auto" is making them Tesla is just another BEV manufacturer. As their market share erodes they're going to have a harder time maintaining their price premiums. They also don't have the deep bench of fleet sales that "legacy auto" has. The places they're trying to diversify (Power Wall, solar, etc) aren't markets that support the premium prices they currently enjoy with their cars.
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The cars are real but they are... Mediocre. The first year of ownership is an amazing honeymoon period, assuming you got one with decent build quality. But the parts are cheap, and they break quickly. I've had one for 5+ years and it's gradually become something I prefer driving less and less.
they’re electric cars. And i’m pretty sure they’re the biggest electric car brand with the most chargers, which they lease to other automakers at a profit. My family has a Tesla and they seem to be very happy with it. I’ve driven in it and it seems really nice, maybe not exceptional but not bad. I do have to say though, the Lidar and self-driving is glitchy and kind of gimmicky. But the fact they’re electric makes th…
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Yeah, but Google's vision + lidar tech also doesn't seem any better at solving it either. They have been working on this problem the longest and they aren't even confident enough to produce a product with it. Google is probably the leader in AI and AI research. They are also the leader in data and mapping. They have billions of cash to play with. Yet it seem like they haven't gotten any closer at solving this problem…
Waymo and Cruise routinely have driverless cars on city streets. In California, all collisions, however minor, have to be reported, and DMV posts them on their web site.[1] Most are very minor. Here's a more serious one from last month: " A Cruise autonomous vehicle ("Cruise AV") operating in driverless autonomous mode, was traveling eastbound on Geary Boulevard toward the intersection with Spruce Street. As it appro…
For all the woe and gloom in the news reporting, Google (and Cruise)'s rollouts have been more or less what I expected: no enormous accidents that were clearly caused by a computer, but instead, a small number of small accidents usually due to the human driver of another vehicle doing something wrong. That seems to lead towards greater acceptance of self-driving cars and confidence that they are roughly as good as an attentive newbie.
The next big situation, I think, will be some really large-scale pileup with massive damages and deaths, and a press cycle where the self-driving car gets blamed. But the self-driving car collected a forensic quality audit log, which of course will aid the police in determining which human caused the accident.
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In what sense is the Model 3 seeming dated? It's still one of the best electric cars on important measures like range and efficiency. It also has access to hands down the best charging network and is well loved by its owners, despite the well-documented problems.
Bjørn Nyland's review of the 2022 Model 3 Performance [1] confirms your view that the Model 3 is absolutely not dated. He tests scores of EVs and still considers the Model 3 the best balance of comfort, features, technology, performance, etc. If anybody here is seriously concerned that the Model 3 may be "dated," I suggest watching the video below. [1] https://www.youtube.com/watch?v=QdMwGJBFUd4
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I disagree with you. On the contrary, I think it can only be solved by throwing more data, but much more data that we have available now, to get first a smart AI that has common sense about the world (a world model). Then we can start trying to drive in it.
The counterexample to your point is that brand new 16-year-old human drivers seem to do a pretty good job of driving with very little training data. Yes, they do kill people and they're much worse drivers than 40-year-olds who have much more training data, but the fact that 16YOs kill as few people as they do with so little training data means some other mechanism than raw data quantity is important.
They’ve been absorbing years of data classifying traffic and things like that.
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Yeah, but Google's vision + lidar tech also doesn't seem any better at solving it either. They have been working on this problem the longest and they aren't even confident enough to produce a product with it. Google is probably the leader in AI and AI research. They are also the leader in data and mapping. They have billions of cash to play with. Yet it seem like they haven't gotten any closer at solving this problem…
Because self-driving has a bunch of tricky edge cases and most of them will kill people. Problems with hundreds of important edge cases cannot be solved by simply throwing more training data at the problem; that's how you solve AI problems in a "dumb" manner, and it works for lots of problems (like recognizing dogs in images) -- but not for self-driving. To solve the self-driving problem we need "smart" A.I., which m…
I maintain the position that if self-driving cars handle the most common situations as well as average human beings, there won't be a strong drive to make cars that drive significantly better than humans. Cruise and Waymo are collecting a limited version of that dataset right now. It's unclear how many deaths because some details are being kept under wraps.
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Scaling parameter count requires a similar increase in the amount of (accurate, labeled) data. This can be mitigated by “bootstrapping” techniques that make labeling new data easier, but is still likely the bottleneck for training such a model effectively (assuming they can probably spin up a supercomputer to scale their models otherwise).
You are correct. A Masked Language Model architecture would work very well here. Train it on labeled data and then bidirectionally infer missed spots. That's fun. We should join their AI team.
I’m not sure what architecture they use, but they do indeed already have a pre trained “auto-labeler” that their annotators use. My understanding is that due to hallucinations from the model and the risks involved with driving, they still need to be vetted manually before being added to the dataset.