My last taxi ride involved jumping red lights and speeding through residential streets at 60mph because, I assume, it was early morning and the driver had learned from experience that he could get away with driving like this.
The previous experience to this was a lecture about a certain religious ideology and how I should spend the next two weeks reading up about it.
Perhaps you live in a location that lends itself to such events...
So it’s not a hold-up, but definitely a form of robbery.
Robbed: Bishkek, Astrakhan. Former nicked my SLR at a gas stop, I should have been more attentive, but wasn’t expecting him to loot my luggage. Latter delivered me to his buddies who threatened violence unless I turfed over every penny I had. Joke was on them as they thought I was a loaded oil exec when I was actually just a broke backpacker.
I’ve also had the old shake-down fare in Ljubljana, Bucharest, and Riga, off the top of my head - but I don’t count that as robbery, just assholes.
I have no personal experience of Kyrgyzstan or Russia, but my hunch would be that the noughts were riddled with taxi drivers like you say, while that has slightly improved over time? I mean, perestroika is known for having those problems, wasn't it? Correct me if I am wrong please anybody, thank you.
Also, kudos on your travel.
Robberies, both 2012, on the same trip.
And yes, corruption still rules the roost in a lot of old eastern bloc countries, from the government to the cops to the babushka driving the bus - it will take a long while for the mindset that the USSR inculcated to dissipate - that is, that the only way to get ahead in life is to lie, cheat, and steal. 2012 was my first time east of the Urals, and these days I know far better how to deal with it than back then, when I was still wet behind the ears.
In 10 years when robotaxi companies are short on cash and trying to IPO they will absolutely start speeding. They'll lock the doors and give you a paid presentation about Scientology during your ride. "Accidentally" drive you to a competing store that paid for sponsored traffic, instead of the .
Openly commit crimes when everything's being recorded and subject to discovery? What could possibly go wrong?
>They'll lock the doors and give you a paid presentation about Scientology during your ride.
Sounds like a good excuse for whoever's trapped to break open the side windows because he "felt he was in danger because of claustrophobia" or whatever. More seriously though, I don't see anything wrong with mandatory ads as long as it's disclosed ahead of time.
> "Accidentally" drive you to a competing store that paid for sponsored traffic, instead of the .
By your own admission "Google search has been enshittified", yet when was the last time google "accidentally" sent you to the competitor's site on the search results page?
What would you say if in 5 years with Waymo, you want to go to Burger King but before taking you there, it suggests ‘hey, why don’t you go to this McDonald’s instead?’ At first you’d get a discount on your ride if you accept the suggestion, and then once everyone gets used to it, no more discounts and everyone ends up paying more than human taxis cost today.
Edit: Here is a link to Karpathy discussing the trade-offs: https://www.youtube.com/watch?v=cdiD-9MMpb0&t=5276s
You really expect a Tesla employee to speak out against Elon?
Especially when $10M+ TC is on the line?
To be fair I remember reading about GPT2 and thinking that LLMs would blow out for similar reasons.
If building a mass market product though the cost is a big deal.
I would assume LiDAR is much more expensive so it would be a big win to get the same performance out of cameras in the long run. I have always just assumed that was the bet.
In cities, high-speed rail and e-bikes make more sense than a honking traffic jam at 4am. lol =3
> One analyst asked about the reliability of Tesla’s cameras when confronting sun glare, fog, or dust. Musk claimed that the company’s vision system bypasses image processing and instead uses direct photon counting to account for “noise” like glare or dust.
I call bullshit. Photon counting requires specialized cameras that are simply not present on Teslas. Not to mention lab conditions (so you can direct photons at your sensor, versus you know, scattering into the atmosphere...) And that don't do anywhere near as well at regular image processing, for that reason.
But Musk thinks you're not smart enough to know this.
Surely photon detection wouldn't be cheaper than LIDAR, given it's impossible to mount to a car lol
He's just wrong about this.
All the information is there in a video feed, but the amount of work to get reliable perception from it is not small. With LIDAR and radar you get to the end goal with less uncertainty.
You can probably eventually ("never" is a long time after all) get a computer to understand the world as well as a human purely through camera based sensors, but it's a much more difficult task than taking an approach that uses tools computers are already good at. Similarly, I suspect it would be an uphill battle to have a human drive using raw LIDAR input.
Private moon landers (the Japanese being most recent one) keep crashing because they rely on a single high-quality altimeter and expect it to work perfectly, all the time. If they had a complementary low-quality backup altimeter that operated independently, they would have had a less failure prone distance estimation system.
I've been thinking maybe vision-only was a reasonable decision, back when lidar was expensive and the software was hand-coded. Now it doesn't, because lidar is cheaper and the software is and end-to-end neural net, and additional sensors are just more inputs to the network which will learn to use them. But Tesla is locked in because of the promises they made to early FSD buyers.
Well considering Musk record for adherence to reality, question is if you can really believe that or if Musk thinks that this is happening or it is not happening at all and Musk is just making it up.
Plus various Tesla engineers have said the same thing, Tesla does have a very large AI training cluster, and FSD quality made a big jump when they claimed to deploy end-to-end.
When the software was hand-coded, having Lidar and a high-res map was vital.
But if you have a good enough AI, sensors that replicate the human senses are all what is needed.
The real question is: when will we get good enough AI that can be applied to all cars?
Back in the day, Elon's specific objection to lidar was that it was too hard to code the sensor fusion part. With end-to-end there's no coding to deal with.
And it's not like they've replicated the visual cortex. It's the same neural net technology everybody else is using. It can deal with any sort of sensors just fine.
What is your example or evidence for this?
Plus our cameras aren't as good as human eyes.
Also, if you've ever done any ML you would note that more data isn't always better. Plus there's the piece about which thing to believe when you get conflicting data. It's a lot more to it than what random Hacker News people are saying in this thread.
The cofounder of Waymo taught one of the first Udacity courses on this subject. He went through a small Python project that processed lidar point clouds for self-driving. The data is noisy, you get conflicting information from different points, and the code aggregates all that into the most likely 3D model of the world.
Additional sensor inputs are just more of the same, and neural nets are pretty good at this sort of thing. They'd even learn which sensors are more reliable in different scenarios.
As for "more data isn't always better," I've mostly seen that applied to training, not inference in real-time control systems. Even for training, it turned out people had been fooled by a local maximum, and once past that, more data really was better.
If you actually look back at the E2E tweet, musk only says that the NN replaced 300k lines of "control code". Control code usually doesn't encompass the entire AV software stack, but neither should it take 300k LOC. As far as I'm aware no one is 100% sure what they mean by E2E and if it's actually the standard meaning or something else that's been widely misinterpreted.
I don't care even if you said you had 70% of the experience their team has, what you say can't sound reasonable or caring for actually improving safety in numbers.
This is really no different from good code practices with a complex system. Most HN readers should be familiar with rot in codebases quite comparable to "adding a few extra features to make it better" which just became a maintenance burden and take away from core features.
If you knew the slightest of how Musk has actually stayed exactly the same since the beginnings of Tesla, you'd know his hard specs are always technology-based. People that know more have said that his capability of speccing systems relatively right deep in to the future is possibly his single greatest leadership feat.
>Once Musk is near-certain about one technology pathway over another, he’s not afraid to put massive amounts of resources into that path, while still staying flexible enough in the case that a new emerging technology disrupts that particular path. Because he’s willing to make enormous (and seemingly risky) bets on these pathways, he’s able to outpace his competitors. https://www.quora.com/Is-Elon-Musk-all-that-hes-cracked-up-t...
For an example of that in self-driving, Huawei uses vision, lidar, radar, and ultrasound. Out of Spec let it drive them around for an hour in busy traffic in a city in China. It looked about as good as FSD, without having had several million cars providing training data for years.
Clearly Tesla engineers will do the same
With that out of the way, it's much easier to write a hardware safety case than one for software. It's easier to write a software safety case for a traditional software architecture than one based on ML. It's much easier to write a safety case for focused models than an E2E system. None of these should be controversial statements.
You're arguing that Tesla is deliberately jumping to the hardest safety case in order to avoid the simpler safety case. I know many people who have worked on the relevant teams at Tesla. I don't think they're ignorant of the difficulty of Tesla's choices or making decisions based on what's easier to validate.
Sensor fusion is not the difficult part. You only have to look at the fact that virtually every other company in the industry (even the ones using E2E and not using LIDAR like Wayve) does it. Either we have to accept that everyone else is stupid, or there are other factors involved in Tesla's decision-making.
For what it's worth, I'm not sure if I could write a safety case about Tesla's system that meets my own personal standards. It's pretty clear from their actions and the various regulatory/legal inquiries that they don't have what I consider an effective safety process regardless.
- What are the main challenges in building software that relies solely on camera input?
- Which specific modules or tasks still require LiDAR to function reliably?
You can tune the camera obstacle detection to be hyper-sensitive, which results in phantom braking, causing Passengers to feel that the car is "unreliable" while it actually is safer. Humans are better at braking the appropriate amount when they see something strange, dynamically tuning their sensitivity in a new situation.
You can lax the sensitivity, which will reduce false alarms, but will actually cause more crashes, deaths, and injuries. You don't want your customers to feel unsafe, so from a business perspective you will inevitably reduce the sensitivity.
Probably the main challenge is that it took nature about a billion years to get to human level visual perception and understanding of environment and nobody really knows how to duplicate it.
Using tools like LIDAR can fill some gaps.
But more specifically to this case, human eyes are attached to brains with (generally) vastly better image recognition and reasoning abilities than any camera based self-driving car. Because of this, humans are better able to recognize visual input even in degraded or unusual conditions compared with a computer.
Despite all this, a lot of modern cars are adding lidar to make things easier for me.
There's a small, sharp, high resolution color-enabled area in each eye - but the bulk of your vision field is monochrome, and mostly sensitive to motion.
You don't notice that, because your image data is stacked and post-processed to shit to make it presentable. Your brain has been doing computational photography before it was cool - 90% of what you see at any moment in time is effectively AI-generated.
We possess a spatial intelligence (e.g. how your brain has an approximation of: It feels like I walked three blocks) that will never exist in this "photons-in" "controls-out" fantasy.
And they may well end up with something "that works". But... many buts.
This "world model" is what you get to peek into through the car's screen. By now, it even has basic "object permanence". Nowhere near as good as a human yet. But AI is getting better, and an average driver isn't.
- What living creature is using wheels to move around?
- What kind of birds come strapped with a jet engine?
Sometimes non-natural solutions are easier and often better than attempt to replicate nature for every cost. Imagine your logic applied on a plane - birds flap their wings, thus this 737 should spread it wings and flap away like a goose. Now take military goose and flap fast enough to get supersonic...
A human driving a car.
I agree with you that sometimes non-natural situations are easier and can absolutely be better, but the point of bringing up humans is generally to show that it demonstrably is possible to do at least as well as humans, with humans as an existence proof.
But that it might well take too long and cost too much to get there, and that it might well in the end be cheaper and better to use additional types of sensors is a good point.
(Also, I don't know what star-system you grew up on by my lidar sensors are next to the tubular sheaths on my cephalothorax, right where Xoc'tlz'ik (the Creator) intended them to go.)
Take any self-driving car crash where the self-driving car was found at fault. Dump the blackbox, extract the raw sensor data. What will you see?
You'll see that the car had all the sensory data it needed to make the right call, many times over. And it didn't make the right call. That's not a "sensors" problem. The sensors are good enough. The main bottleneck for self-driving is, and always was, in AI.
Which is why you get things like that Cruise car dragging a pedestrian despite being equipped with 360 cameras and a total of 5 overlapping LIDARs. It had the sensors. What it didn't have was object permanence.
A big part of a self-driving car's "safety edge" is that it isn't going to go 80 in a 40, doesn't fall asleep at the wheel, and isn't capable of DUI.
Self-driving cars still struggle in some situations most human drivers wouldn't find challenging - AI issues - but they don't make the worst, the most unforced and avoidable "human factor" mistakes.
So you could define it as a sensor issue