In the short run, technological progress is much slower than anyone hopes, but in the long run it is much faster than anyone expects.
My favorite example is that in 1987, the scientific consensus was that it would take "at least 100 years" and likely much longer to sequence the entire human gnome.[a] But the vast majority of it was sequenced by 2000, only 13 years later. Moreover, just two decades later, anyone could get their genome checked for known markers for pocket change by companies like 23andMe, founded in 2006.
Having some expertise and interest in AI, I regularly watch presentations by all companies working on self-driving and also look at the videos posted by beta testers online. While it's fun to watch the failures, I'm more interested in judging whether the technology is continuing to improve.
My perceptions contradict this article: (1) The technology is progressing faster than is generally recognized, with vehicles getting progressively better at dealing with edge cases and handling failures gracefully. (2) Judging by the videos I've watched online, Tesla is significantly ahead of everyone else.
Prediction: Before the end of the decade, this article will seem... short-sighted.
I agree with everything you said, but chuckled at this particular part (which is very wrong):
> Tesla is significantly ahead of everyone else.
Everyone in the industry knows that Tesla is nowhere near the tip of the technology. What Tesla does is _fantastic marketing_. Their whole self-driving division is just a mechanism to sell more cars.
At a high level, this is why:
- The hard thing about self driving isn't the first 95%, it's the impossibly long tail of the last 5% with unique, chaotic and rare scenarios (think, a reflective citern tank with a reflection of the back of a truck transporting stop signs, or terrible weather illusions with fog).
- Doing well on the last 5% is where most of the energy from Waymo/Cruise goes (the two leaders by quite a margin).
- Tesla is camera only. Weather alone means you can't reach critical safety because of this. Cameras don't do fog well, precipitation well, or sunsets/bad lighting well (see many Tesla crashes on freeways bc of this)
- Tesla does well on the 95% and Elon is a marketing genius, with those 2 things it's easy to convince outsiders that "Tesla is significantly ahead of everyone else".
My prediction: before the end of the decade, cruise and waymo have commoditized fleets doing things that most people today would find unbelievable. Tesla is still talking a big game but ultimately won't have permits for you to be in a Tesla with your hands off of the wheel.
My favorite case so far of the '5%' that you mention happened on my Tesla irt:(object recognition) and I still laugh about it to this day.
I was driving down the road as normal, 4 lane divided highway that's a bit hilly. Suddenly my car starts having what I can only describe as a panic attack saying I'm running a stop sign and blaring alarms.
It was detecting a giant 40ft tall red circle sign a bit away as a stop sign...
As someone who also follows the progress online and watch the presentations by Cruise, Tesla, etc, I agree that Cruise is well ahead of Tesla.
It feels like Tesla’s main strategy is to add more data, more compute power, more simulation, and hope for “convergence”. Maybe that will work, but right now it feels like Cruise’s technology feels more mature and thought through.
I believe that Waymo and Cruise have more capable platforms that can more accurately measure the environment at those boundary conditions, where as Tesla's camera only approach more closely mimics human perception. But where do they stand in terms of datasets used for training their models?
It seems like Tesla has a huge advantage in terms of training data by leveraging a fleet of millions of vehicles.
Having ridden the self driving vans from Waymo in Arizona several times, it really does feel like stepping into the future. Although they only cover a specific geographical area, they have really refined the riding experience within it.
George Hotz is 'in the industry' and seems to disagree with you. I have heard others say the same. It seems people who work at Waymo/Cruise are totally convinced by those things.
Different approaches lead to different paths to solutions, I am not convinced that either will be successful and not convinces Waymo/Cruise is ahead.
Unlike those, Tesla actually makes money and uses the technology stack in more limited forms.
95% is a ridiculous exaggeration. How well can these cars do in the winter? You know, that season we have that can easily keep the ground covered with snow and/or ice for 30% of the year in many cities. I lived in Chicago and it was genuinely difficult to drive for 3-4 months of the year. How well can these cars do during the very rainy hurricane season in Florida?
Do people who work in this industry actually think they've solved 95% of driving scenarios because their software can manage driving in sunny California, Nevada, and Arizona?
> Everyone in the industry knows that Tesla is nowhere near the tip of the technology. What Tesla does is _fantastic marketing_.
Between one and two years ago, that was my perception too. But the rapid progress I've seen with Tesla FSD Beta over the past couple of years, and over the last year in particular, has forced me to change my mind. (Note: I'm talking only about Tesla's beta software. Tesla's production software is behind by dozens of versions and is without a doubt technologically inferior to Waymo and Cruise.)
Less than two years ago, I would have said FSD Beta could only deal with the "first 95%" too. Now, my perception is that FSD Beta routinely handles the first > 99% and fails only on < 1% of situations. Moreover, the failures have become more graceful -- e.g., the car will stop at intersections perceived as risky and ask the driver to confirm go-ahead by pressing the accelerator. If FSD Beta continues to improve, sooner or later it will cross the threshold at which it becomes safer than most human drivers.
Of course, IF I see new evidence that contradicts my perceptions, I'll change my mind again. There's no shame in changing our minds when the facts disagree with our views. FWIW, I'd love to see videos of Cruise and Waymo vehicles, filmed by tens of thousands of regular consumers driving autonomously on fully unrestricted roads, with zero editorial input from Cruise or Waymo.
> but in the long run it is much faster than anyone expects
I saw the first Moon landing.
At the time, everyone, the experts, the public, everyone, expected us to colonize the Solar System within a few decades. We expected that fusion power would be too cheap to meter and that burning fossil fuels would be a thing of the past. We expected human life expectancy would soon rise to over a century in developed countries.
Serious, respected scientists said all these things, and everyone took them for granted.
None of these things did in fact come to pass.
Humans have not ventured in person even as far as the Moon in fifty years.
Our first atomic "pile" was 80 years ago in a few months, and we still don't have a fusion reactor. The tokamak was the big fusion design 50 years ago, and it still is, and we are better at them, but we still are nowhere near actually producing real power.
Life expectancy increases stalled quite fast, and then life expectancies started to regress. Americans have lost about two years of their life, basically because of their group mistrust of medical science.
It is simply diminishing returns on the amazing discovery that is the scientific method - it is unavoidable.
> Americans have lost about two years of their life, basically because of their group mistrust of medical science.
I don't think that's the actual reason for the decline, it's more likely another effect of the real cause.
While I'm not American, it's the same story all over the world: our diet keeps getting worse, there is plastic everywhere which breaks down into micro plastics which end up in the water we drink etc.
We're preparing our food with carcinogenic utensils (everything "non stick") and all of our western societies went from a healthy worker/academic career choice into mostly just getting exploited by the established players.
There have been a lot of societal changes since the post WW2 times, and summing that up into "losing trust in medical science" is a very confusing take, especially if you consider all the medical professionals that spread outright lies to profit. (The person that started the antivax movement was a doctor as a notorious example)
And drug companies that sold bad medicines (knowingly!) As they judged the cost of liability to be lower then the cost of not selling the drug.
Just to be clear, I'm not saying that any of these things are causing this decline either. Our societies have just changed too much since to make any confident claims about their impact.
I'm genuinely curious why you think Tesla is ahead compared to Waymo and Cruise. Autopilot struggles in my car on some fairly boring roads, while Waymo and Cruise are both operating real taxi services in vehicles without drivers. I can understand the argument that Tesla has lots of data from real world driving. But Google also has fleets of cars mapping out every road in the world.
Please keep in mind that I'm talking about FSD Beta, not the current production software, which is dozens of versions behind.
Are you on Beta 10.69.2.3?
If I had to articulate my reasons:
* Judging by the videos available online, my perception is that many situations that were impossible for Tesla FSD Beta a year ago have become uneventful in recent weeks. Take a look at Chuck Cook's videos for example (I like the fact that he always highlights the failures).
* Judging again by the videos available online, my perception is that Tesla FSD Beta has encountered and had to deal with more crazy edge cases than any other system. A possible explanation for this is that for a long time Tesla FSD Beta hasn't been geofenced or restricted only to certain types of roads, like highways. You can test it anywhere in North America.
* Tesla FSD Beta currently has 160,000 individuals testing it without road restrictions. As far as I know, no other system has been exposed to similar open-ended large-scale testing.
* Occupancy networks look like a real breakthrough to me -- DNNs that predict whether each voxel in a 3D model is occupied by an object, using only video data as an input. I understood the high-level explanation of these DNNs on AI Day 2. I haven't seen anything like it from anyone else.
* Tesla's DOJO also looks like a breakthrough to me. I understood the high-level explanation of it on AI Day 2. IIRC, DOJO cabinets are 6x faster at training existing neural networks than Nvidia rigs, at 6x lower cost, so call it ~36x more efficient.
They’re operating Taxi services in San Francisco. A city that doesn’t experience any real-world weather, with an area of like 50 square miles where speeds generally never exceed 25MPH. They also have humans watching cameras that take over when the self driving breaks down.
It’s a completely different problem space, like claiming someone built a train and therefore they can easily build self driving cars since they are both “driverless”.
Aren't Waymo and Cruise constrained to specially tailored cities? Autonomous driving that can be activated literally anywhere in the country is far more impressive, even if it has cases where it doesn't perform as well.
Compared to general self-driving, it's relatively simple to make self-driving taxis work in just a couple of cities (preferably ones with minimum adversarial weather, too) - you just test the code against that particular dataset until it performes reasonably well, manually fixing the edge cases along the way if need be. It's still a monumental, multi-billion dollar project, but I would be surprised if it wasn't achievable.
Not sure about the current state of the actual ML, but compared to other self driving companies Tesla has a treasure trove of data because they have so many vehicles on the road at all hours of every day. The edge cases are the parts that are hard to identify and solve so having all that drive time data to identify edge cases would seem to give them a big advantage.
> Moreover, just two decades later, anyone could get their genome sequenced for pocket change by companies like 23andMe, founded in 2006.
Sequencing your genome is still relatively expensive, Probably around $15k. 23andme does not sequence your genome. They look at specific regions looking for specific markers. This doesn't invalidate your point of how great progress has been, though.
A person overestimates what he/she can do in a day and underestimates what he/she can do in a year. I read that somewhere and I often think of that. Seems also applicable to a bigger scale.
> In the short run, technological progress is much slower than anyone hopes, but in the long run it is much faster than anyone expects.
But it doesn't happen in predictable ways. A particular area of technology hits a wall and plateaus all the time, for myriad reasons. In 1950, you might have thought that by 2022 we would have done a lot more with nuclear technology or supersonic airplanes than we have.
Yeah. There's a lot of variance in the timing of milestones, which leads to people drawing all sorts of overgeneralizations from the few examples they happen to know. The most popular example being "Moore's Law and FAANG market caps prove that everything in technology develops exponentially, which is a word that means super-fast"
My perceptions contradict this article: (1) The technology is progressing faster than is generally recognized, with vehicles getting progressively better at dealing with edge cases and handling failures gracefully. (2) Judging by the videos I've watched online, Tesla is significantly ahead of everyone else.
We must be watching different videos. And experiencing Teslas differently. I see Teslas constantly slamming on their breaks on freeways, swerving across lanes, and avoiding collisions by the narrowest margins only because their owners took control before certain death. And that's just driving around in L.A. traffic, the YouTube videos are even worse. Tesla's vaunted camera-based system still can't recognize white semis or other broad, flat obstacles that a human or radar-based system would recognize instantly.
Tesla was ahead of their competitors, several years ago. Now they're way behind, and dropping further behind with every "update" that addresses the problems that got media coverage with "solutions" that indicate brittle, manual programmer overrides rather than any sort of scalable AI-driven capability.
And it's irrelevant that Tesla has 160,000 drivers on the road "training" the system, since they selected the drivers who drive in the safest road conditions using a "safe driver" metric that has no relationship to safe driving. This means that Tesla's "AI" (to the extent it can be called that) is being overwhelmed with tons of useless data that overtrains it to drive easy roads and with almost no training for difficult conditions or edge cases. For point of comparison, most vehicles today with advanced cruise control can drive the same roads that FSD can safely drive...but they don't need advanced AI to do it.
It doesn't matter how far ahead you were at the beginning of the race, it matters how far ahead you are at the finish line.
Part of this is the fault of Tesla's marketing, but you are wildly off mark. The cars you are seeing are Autopilot, not FSD. Most of them are even the even older, radar-based autopilot.
Tesla Vision has no issues detecting white semis crossing your path. Vehicles with radar, on the other hand, struggle with discerning those from overhead bridges, so if one appears close to a bridge, you're SOL due to whitelisting.
Tesla Vision in FSD is a much, much more developed version which has been excellent about detecting its environment, especially now with the new occupancy network. Its decision making needs work but you will notice, when watching all those videos, that detection of vehicles - even occluded ones - is not a problem at all.
Your comment about useless data is also wrong. They are experts in their field and they know exactly what type of data they need. Both Tesla and Karpathy himself have shown on multiple presentations that they focus on training unique/difficult situations because more data from perfect conditions is not useful to them anymore. They have shown exactly how they do it, and even showed the great infrastructure they've built for autolabeling.
I think the quote you are referring to in the article [a] states "At the time, that [the 500 genes already sequenced] was thought to be about 1% of the total, and given the pace of discovery, it was believed that complete sequencing of the human genome would take at least 100 years"
The 'given pace of discovery' I would read as an extrapolation using (then) available technologies, not as a scientific consensus about what would be possible using new technologies in the near future.
I remember the gnome thing. But that is nothing I have any knowledge about. So this area was for me like unknowledged people learn about things.
Automation is actually my area. Self-droving cars are in development for more than 50 years. In the 1970s it was more analogous technology that needed a lot of space. Fact is today technology is not that much better than that. Except, at the days it was highly supervised by professionals.
Many hardware devices we have in our cars today profited from those developments. Because radar and things are now cheaply avalaible. But self-driving is still not a thing. All the improvements in AI even didn't realy helped.
What many people seems to forget AI is about to detect (learned) patterns and to make decisions based on those patterns. But AI is completely unpreditcable on patterns it can't recognise. AI will classify those into a know pattern. But this is random and as such the judgedment is random. That is the big difference to a human, which can still cope with an unknown situation.
That is the reason why many people from the industry say: real self-driving cars will take very much longer than the average publics thinks and was told by Elon, Uber and others.
> Prediction: Before the end of the decade, this article will seem... short-sighted.
Can we say that all the self-driving optimists that were completely wrong about the last decade were shortsighted?
Also, no idea what progress in biotech has anything to do with self driving. Your argument is that because some tech advanced all other possible tech will advance, seems like a logical fallacy.
It is another 80/20 rule with project management / prediction / projection. The last 20% is going to take as much time as the first 80%. And I would argue we are not even reaching 80% yet.
Not to mention most of the edge case training it had in US may have little resemblance to say UK or AUS. And that is ignoring culture difference in places like India or China. I am not an expert in DNA, but I don’t understand how DNA sequencing isn’t a finite problem. Comparing to AV, in a real world with so many edge cases, you are practically looking at an infinite problem.
In the long run, the technological progress is also in fields people didn't predict, or entirely new fields.
A lot of ideas just never pan out, despite considerable investment. Or, they get it to work, but proves to be of niche use. Progress happens elsewhere.
We have had self-driving trains and self-flying planes for decades. Despite this, train operators and airplane pilots aren't going away, and, if anything, are becoming even more important professions.
That's because they're in the "providing security" business, not in the "operating machinery" business. We pay pilots to take responsibility so passengers feel safe.
It's exactly the same for cars and trucks. The self-driving car racket is a scam, because they're not solving the actual problem society wants solved.
To be clear 23andMe does not do sequencing. They check for certain markers.
However full sequencing IS available and runs around $1000.
If I recall right the first human genome sequenced cost about $1 trillion. But we now have much better algorithms, mostly due to algorithms from works such as Knuth's AOCP.
This article is kind of a wrapper for the linked bloomberg article, which is more interesting IMO.
Driving of cars is a massive cooperative game with high stakes, and autonomous cars essentially need AGI in order to play to a degree that is safer than a human with other humans. Fully autonomous cars would be sick, but IMO you'd need massive infrastructure changes (realistically restricted to cities/urbanized areas) if you want autonomous cars to work with anything less than AGI. Until companies start pursuing that, they are actually unknowingly using all that money to push for AGI and obviously coming up short because they don't even understand what they are trying to do.
Why should we change city infrastructure to work with less than AGI driven cars? It sounds instead like yes, AV companies are over selling what they can do and they should be restricted to things like highways and the like where the easier, less complex driving environment already exists.
Cities are by far the most complex relative to every other driving environment, in fact there is a good argument that, in cities, cars should be much more restricted because of health effects and traffic deaths, and the less complex areas (highways) are already the bulk of the drudgery in driving, but are much easier to automate, so why not do it for there?
Not all driving outside city areas happens on the highways.
My brother lives in the countryside (somewhere in the EU), and, as a driver, he shares the paved road just outside his house with the village’s cows (including his two cows). I don’t see any non-AGI system being able to negotiate that, as at times is difficult even for me, a reasonably AGI system, to make sense of it all when I encounter a herd of loose cows on the road.
One good reason is to help the AIs make better decisions through greater certainty. For example, if a road sign has machine readable data there is a greater certainty that an AI will interpret it correctly. This could affect safety and ease of implementation.
This bullshit shows up in every HN thread on autonomous vehicles. Really only on HN. It's wrong, uninformed, and won't seem to go away.
The challenges self-driving cars have nothing to do with infrastructure and everything to do with the other moving objects on the road. It's not that the vehicles can't detect the other things on the road. It's that they can't anticipate reliably what they're going to do.
(However you may be correct that we need AGI or something close to it to do autonomous vehicles robustly)
Object detection is an issue, but it doesn't only apply to "moving" objects. The current state of the art is having trouble differentiating between a "solid" stationary object like a tire or chunk of metal that must be navigated around, and something like an empty plastic bag that can be safely ignored. Regarding infrastructure, AVs also seem to have a lot of trouble with faded lane lines on roads.
Unless… the infrastructure change they propose is removing the “other things on the road”. Sounds brilliant. You could call that system a “rail” road as the cars would be on rails in a manner of speaking
This is where I see the problem being with autonomous vehicles, too. They can only react to other vehicles, they can't predict them.
Humans are good at predicting.
You don't consciously know you've seen the guy a couple of cars in front checking his mirror and his shoulder but you're hanging back because you just know he's going to pull out any second. The guy that's wavering a bit in the middle lane is about to dash across to the far lane of the sliproad that's coming up, clipping the zebra stripes a bit, because he's concentrating on the sat nav not the road, but you just know - out of all the other drivers in your space at the moment - that red Ford is the one that's going to do something boneheaded.
Autonomous Vehicles won't be able to do that, probably not ever.
I recently drove a rented Tesla 3. On a highway in Norway it failed to detect speed limit signs in like 25% of cases. And if the speed limit sign was a temporary one due to road repairs it failed with those like 50% of times.
And this is with stationary objects designed to be seen and easily grasped by humans.
This reminds me of something the Rocket League devs said. Something to the effect that bots wouldn't be effective at the game because it's too challenging.
This is -not- my line of work, so I have no idea if that's true. But if it is, I don't see how we could have perfectly safe self driving vehicles.
Teslas, despite the ludicrous promises of "full self-driving in 6 months, just you wait", completely choke if there is so much as a traffic cone or a road-work barrier for it go around.
Cars are bad. Electric cars are a bit better than ICE cars, and self-driving cars might one day be better than human-driven cars. But they're still terrible: they're still big, inefficient, polluting, loud, dangerous, and take up precious space. Cars are completely unsuited to cities, and cities which are designed around cars are terrible.
This to say: the future is having cars removed from cities entirely. Focusing on self-driving technology, or on electric cars as if they're going to "save the planet", is entirely the wrong direction imo.
Personally, I think while probably too futuristic large drones that are autonomous and deliver people would be safer. Its arguably easier to avoid objects when you can dodge in 360 degrees of direction.
It's also easier to avoid pedestrians as most can't fly.
It also could save on gas and energy as you can go directly as a bird flies to your destination instead of taking 20 minutes it takes 3.
Of course this would be a huge infrastructure ordeal as well probably and require a damn good system as you don't want vehicles landing on houses all over the place, but it'd be amazing for people with long commutes.
You could probably have airbuses that pick up like 30 people say in a small town and fly them to the city to be delivered individually by smaller vehicles locally. What took 45 minutes, now maybe takes 15.
These would be better if maybe electric with gas as an emergency backup system, and then just have good batteries and solar power fuel most trips.
I always imagined the future of self driving cars wouldn't lie in cities (except maybe some main ringroads or arterial roalds) but in highways. Just tell the computer to go to highway X exit Y, and from there the driver can drive the last few miles.
Basically geofencing known 'sane' locations, which cuts down on the boring bits of driving significantly.
And then in those safe geofenced locations we could put down metal guide rails so that navigation is easier. And then use metal wheels with flanges to reduce rolling resistance. And then hook many cars together into one long vehicle. And ...
Are there even any racing/sim games with solved driving AI? All the ones I can think of cheat, replay hardcoded paths, and/or are terrible. And thats with perfect information about the world and other cars.
Those games ai systems get substantially less budget and time than the automakers. There's also been a lot of talks about players not really having fun with ai that is too good, the players will always assume the ai is cheating whenever it out maneuvers them if you make it too well done
Practically all the AI-assisted automation systems I'm familiar with, whether its identifying objects in an image or parsing documents, have the same problem. They work for 90-95% of problems but get trumped up by edge cases and a human has to intervene.
That's not a problem when you're transcribing a video, but becomes a matter of life and death when you're driving a car.
> autonomous cars essentially need AGI in order to play to a degree that is safer than a human with other humans.
This is really an astonishingly large claim without any evidence.
I question if you understand what AGI actually is? It's not "AI that can solve game theory" or "AI that can play cooperative games" - conventional video game AI's do this all the time in a myriad of permutations.
Statements about X specialized task requiring AGI are questionable now, just like they were questionable when they were said about Chess, Go and Video Games.
Show me a chess engine that can deal with a chess peice that has been mangled so you can only tell what it is with context clues. Also a plastic bag occasionally obscures one of the other pieces. There's also construction on one side of the board every few turns that it has to route around using lanes that arnt normally legal. It has to do this while a drunk human is also moving the pieces, sometimes in ways that arnt legal, and if it takes too long it can hit a wall and die
That’s really not true. Americans love good public transportation where it’s available and works well. Most US cities have lots of buses, and many have light rail. But in a big country with a spread-out population, public transportation is tough.
American corporations perhaps aren’t as interested in public transportation, because there is no money to be made. And that is who is largely funding this self-driving vehicle research.
Self driving tech could and will be applied to public transportation too. With self-driving mini-buses we can serve more destination much more efficiently, fine-tuning commute supply to better fit the exact demand.
Yes, as a hyper individualistic American fuck public transport. I don’t want to travel with other people, I don’t want to live in dense cities and I don’t want to go to like the 10 places with public transport. I love national parks, camping, hiking, road-tripping etc.
This is all private money. If these companies didn't spend the $100B developing autonomous vehicles, it wouldn't suddenly be available for building public transit.
Beyond that, $100B doesn't go as far toward building transit as you might expect. For example, San Diego recently spent $2.3 billion on a light rail extension that's projected to have 34,700 daily trips by 2030. If you assume most of these are round-trips, it's serving fewer than 18,000 people. Spending $100B at this rate would serve around 750,000 people (0.2% of the U.S. population).
The most cost-effective form of public transit in most places is busses because they can reuse existing road infrastructure, and in the U.S., labor accounts for around 70% of the cost of operating busses. As a result, autonomous driving technology should be helpful in scaling public transit systems as well.
Do you mean the La Jolla trolley line? It seems like a nice idea (I’ve lived next to several trolley stops) but the virtually nonexistent ticket validation tends to make the trolley a magnet for negativity, and keeps me in my car.
The budget for just the MTA in NYC is 18 billion / year and that’s really only for the ongoing costs. A 100 billion investment in public transportation over the entire US over 10 years once wouldn’t have done shit.
A fleet of self driving cars are realistically the only scalable public transportation because the costs increase with the number of people not the area to be served. Do people just forget how unbelievably spread out everything but the densest cities are? 24/7 bus service to within 0.5 miles of every house in my city is already impossible even if you allowed them to be on the every 4 hour. To replace cars people would realistically need them on the hour and want on the 20 minutes. Bet you my shirt at that point it would just be cheaper for the city to just run a free taxi service.
This pretty much. Building public infrastructure is insanely expensive and there are capacity limits that can't easily be solved. We need to add public transport to the mix that 1) doesn't require heavy additional investment in infrastructure and 2) solves convenience concerns that make buses unappealing and 3) can reliably replace privately owned vehicles.
You cannot just add "public transport" to most of our existing cities to replace peoples' cars. The reason is that everything is too spread out, mostly due to the large parking lots everywhere. That may be sub-optimal in a certain sense, but it's the reality and you can't just snap your fingers and change it. Any bus-like thing would need far too many stops to be practical to be able to get people to a reasonable walking distance of where they want to go. That's why in basically every city where they've been added (distinct from being created due to organic demand), pretty much nobody who can afford any alternatives rides them, no matter how much money is dumped into them.
To do public transport right, you'd have to basically demolish the entire city and re-build everything from scratch to be friendly to pedestrians. Which is pretty much a non-starter, and even if you wanted to try would cost many orders of magnitude more than all of the self-driving car projects.
> You cannot just add "public transport" to most of our existing cities to replace peoples' cars. The reason is that everything is too spread out, mostly due to the large parking lots everywhere. That may be sub-optimal in a certain sense, but it's the reality and you can't just snap your fingers and change it.
Its not about 'snapping your finger'. Neither Netherlands or Switzerland built their systems from 1 day to the other.
You need to make decision to change and then consistently and incrementally work on it. Put it in your standards and invest ever $ you have for new roads to that instead.
You need to change your tax policy so that horrible inefficient land uses like parking lots cost a lot more. You need to enable mixed use development so these parking lots can be built on.
> To do public transport right, you'd have to basically demolish the entire city and re-build everything from scratch to be friendly to pedestrians.
I'm sorry that is complete and utter nonsense. Like seriously, completely insane.
If you look into some urbanist and city planning literature you will see that lots of places where there used to be total car shitshows, are now beautiful. Often you would never have guessed that just 10-20 years earlier it was horrible road and a parking lot.
Again, small and incremental steps. Here are some really basic steps you can take:
- Remove parking requirements
- Slow speed of cars
- Don't allow turn right on red
- Make the lanes thinner
- Make the sidewalk broader, maybe add some trees
- Take one of the existing lanes and add painted bike lanes, later add protection for those lanes
- Rezone for mixed use (specially existing commercial zones)
- Change property tax policy to discourage sub-optimal land use
I could literally keep going on and on. Non of this, requires you to demolish anything.
Specifically for the US, there is whole movement about incrementally improving your city, see Strong Towns (https://www.strongtowns.org/). They have lots of podcasts and books. Specially: 'Strong Towns: A Bottom-Up Revolution to Rebuild American Prosperity'.
They also point out in detail with real data how these changes make your city safer and economically much better (They have some seriously amazing visualization of city finances that shows how such chances can improve cities).
And this is not some hippy organization, these are coming from a somewhat conservative small towns perspective.
Honestly your attitude of 'we are stuck with this' is horrible. I can understand frustration and bleak outlook, about the situation. But put your hope into incremental low cost change, not some techno futurism and you will be less disappointed.
I think the goal is to solve public transport by having a fleet of autonomous vehicles that pick you up where you are and drop you off where you need to go and would roam freely in between - basically driverless, electric ueber.
As cars need charging, they would congregate at some charging plot outside the busy areas.
Personally, I rather like this vision as it combines the best of public transport and car traffic. Especially, if the existing, personally owned cars that just stand around 99% of the time vanish over time.
> As cars need charging, they would congregate at some charging plot outside the busy areas.
Great so you have constantly cars driving form the city center to outside of the city, that for sure will cause no traffic at all.
Will be fun when people proposes new elevated highways out of the city so the self driving car can go outside of town to the coal power plant to charge.
The only thing worse then having vehicles driving around with 1-preson, is vehicles with 0-people. Its literally the most inefficient use of space ever.
It makes traffic worse, not better and it makes the city worse, not better.
How about this, a city optimized for walking and biking, where different parts of the city are connected threw buses, trams, subways or regional trains.
> Especially, if the existing, personally owned cars that just stand around 99% of the time vanish over time.
Turns out that cities where people can, walk, bike and take trains they don't own cars. Shocking.
Well, why not then create bigger cars, or smaller buses? Between 5 people in a car or a 100 people in a bus, an optimal solution should be able to converge slowly, to 20 people in a vehicle. It would be different for every city, or region or country.
Most cars spend most of their time, being stationary in a parking lot or a garage, 93 percent of the time to be more specific, or 23 hours/day. Buses spend a lot less time being stationary. The trade off here is the speed of driving. Cars offer an unlimited amount of speed, while buses do not. An optimal economic solution should exist in which the economic actors, i.e. people will figure it out after a lot of trial n error.
A crucial factor in self driving cars no one mentioned, is the data the machine uses to drive should be incorruptible. A blockchain which supports billions of tps, offer a solution to that.
Additionally road variables change over time, and data should change as well. Economic actors, not just people should feed the machines with updated data every day. That means that a marketplace of information is required, in which the most efficient economic actors with the best accuracy and the best reputation are rewarded, and the worst economic actors, who's data aggregation cause a lot of crashes, fall off the market.
A marketplace of information, doesn't exist for the time being, so there is no chance for self driving cars to be safe and effective.
I'm about as big of a fan of mass transit as you can get, but this is completely overblown.
That's $1Bn for the hundred largest cities in the world.
Assuming even 40% of that went to the US, that's $1Bn for the top 40 cities. That leaves out 22 entire states [0], and like 75% of the population...
Look to NYC, LA, and SF for what $1Bn gets you... It's about 1 mile of subway [1]. And it takes close to 15 years to build.
We wouldn't all be riding around on space elevators with materially better lives if this money was invested in subways or trains.
If you spent $40Bn on busses - you'd have to spend another $250Bn to pay people to ride them...
Self-driving cars will eventually change cities. I think there's evidence it's already starting to happen.
I don't think this money would've been better spent on trains, and definitely not busses.
What else are you thinking of?
I'd be interested in a better cost breakdown of bike lanes and how much it would cost to get a significant percentage of people in cities biking & scootering around - but I'm skeptical, and also, it's not mass transit!
NYC installed 29.5 miles of protected bike lanes last year [2]. I can't find the cost, but next year they're asking for $3.1Bn to build 500 miles of protected bike lanes, among many other things [3]. I know it costs less than $1M to pave a two-lane road one mile [4] - so a protected bike lane should be well under $1M - but then everything costs way more in the city...
If protected bike lanes cost substantially less than $5M per mile in the city (like $0.5M) - $40Bn could get you pretty far!
That's 80k miles of protected bike lanes! That's about 4x the amount of total bike lanes we have now.
Bike commute rates in NYC are decent (by US standards). I'd love to see a study on how much bike commute rates increased after these new lanes were completed.
Copenhagen has only 240 miles of bike lanes and 600 miles of paths for 70 square miles and 750k people [4]. That's enough to get 62% of people commuting by bike [5]!
For the top 40 cities, you'd be looking at like 80k miles of protected bike lanes for 4000 square miles and 81M people. That's better than Copenhagen!
That could potentially get you close to 62% of people biking instead of driving - just depends on if that many people live within 5 miles of work / school / going out. 5 miles being the average commute distance in Copenhagen [6].
62% of cars off the road in the top 40 US cities would DEFINITELY change my life for the better - but I'd be surprised if we could even get 15%. Still, it's something you could do in a couple of years - and for $40Bn - would definitely be worth it. But it's decidedly not mass transit.
I don't think you're refuting the parent post so much as lamenting the difficulty of doing anything "infrastructure" in today's America.
There are lots of places all over the world where $1B would make a big difference in many, many people's lives -- and where people eagerly ride the busses they actually have, which are often not that nice.
Do we need things to work in the USA for them to be worth doing?
It's a non-trivial amount of our society's wealth, doesn't really matter who owns it in regards to talking about it's use. Just like we can talk about how spending a billion hours on reality TV is probably not an amazing use of time. It's not our time, do what you want, but you can still talk about it.
Having been the victim of public transport for the first 30 years of my life: nope. You just can’t polish a turd. (Maybe other countries can but here in Germany it’s a lost cause and you can’t make individual mobility expensive enough for me to ever take public transport again).
As a counter-anecdote, I’ve been using public transport exclusively for 35 years (since first grade) in Helsinki, London and New York, and never felt like I’m a “victim” of anything.
I only ever take a car for trips outside the city.
$100B is not that much when you compare it to the value self-driving cars will create.
There are over 1 billion cars on the road which need humans to drive them. How much are they used? I don't know. Let's say one ride per day. When cars turn into a service business, the "driver" will be software. What will be paid to the driver? Let's say $1 per ride.
That is 1B * $1 * 365 = $365B per year. Give that a p/e ratio of 10 and the value is $3650B.
So we could spent 30 times more and still break even.
I would like to nit-pick and say you're estimating some novel metric. The value add would be somewhat higher than that because the alternative is a human driver, so the value-add of the software is approximately ($/h value of driver x hours) that it frees up to do something else. Plus all the evidence I see suggests that whatever the current state, sooner rather than later computers are going to be superhuman at driving (like they are at most other discrete tasks) and save a lot of lives. Plus reduced wear & tear on vehicles.
$365B in value is probably a serious undercall. The only reason to complain here is if only $100B has been put in to the venture so far.
I think computers will become superhuman at driving under the right conditions.
However, there will inevitably be conditions that require the use of general intelligence (rather than driving heuristics), and in those situations all you can do is pray the computer acts rationally despite not having GI.
I think self driving cars have already passed the test of "number of crashes" or "number of fatalities" per mile driven. But I don't think that's enough to sway the public, if every once in a billion miles a self driving car slowly drives off a cliff for no apparent reason.
>>Plus all the evidence I see suggests that whatever the current state, sooner rather than later computers are going to be superhuman at driving (like they are at most other discrete tasks) and save a lot of lives.
What is that evidence, exactly? I agree that we might eventually get there, but the scale seems to be 50-100 years at this point. We are as arrogant as the researchers in the 60s who famously announced that absolutely perfect image recognition is only 1-2 years away - except the problem is several orders of magnitude harder.
I'm not sure I get your valuation model.
"we're" not investing in self driving it's the car companies, because (presumably) they believe that self driving will make their cars more sellable compared with other brands.
If after another $50B or $100B spent some companies start to pull back on funding because they think diverting funds to other areas will give a better return (better batteries, cheaper manufacturing etc), it's likely others will too.
It's not only off topic, it's also wildly unrealistic (IMO). 1$/day may seem little, but that's for middle-class US standards, and it gets proportionally worse when you realize many people make more than one trip per day. What incentive do you have for people to pay that amount?
So? Okay, we're not there yet with autonomous vehicles, and have spend tons of money on it. But the tech we have now didn't exist a decade ago. Give it another decade.
Another decade for prototype self-driving cars to cover another state part of the US? $100BN in funding these gadgets and contraptions that don't work. Not even Tesla FSD can drive itself reliably at night without supervision, because it is not Level 5.
Until LiDAR becomes more cheaper and these cars CAN drive themselves safely at night without supervision in any state at scale and as advertised like a robotaxi, then you're looking into multiple decades of these research prototypes being 'useful'.
So far, that $100BN is a VC scam until proven otherwise.
>Another decade of researching a cancer cure to cover another different type of cancer? $1400BN in funding these cancer treatments that don't work. Not even radiotherapy can fully cure cancer...
Thats how I read it, sorry. We made tremendous leaps in the past decade with improving automated driving, is it fully automatic? No. Does it mean we should somehow stop funding it? No.
I repeat my comment on LiDAR that I gave a few days ago. The gist is that LiDAR is cheap and you will be able to buy a LiDAR with sufficient resolution for in the next 1-2 years because it will be integrated in normal passenger cars for L2/L3 assistants. These cars are coming out now or in the next year.
LiDAR is finally getting cheap. OEMs (like VW) are very price sensitive. It is estimated the sensors from Valeo cost about 500 dollars. The fact that you see more and more normal passenger cars with higher resolution LiDARs means that LiDARs are getting cheaper.
The Audi A8 used Valeo's (with Ibeo) first generation low resolution LiDAR Scala 1 from the automotive supplier Valeo. Mercedes new models will be using Valeo's second (or third) generation LiDAR. All these are used for L2/L3 assistants. Valeo is a traditional large automotive supplier.
Luminar, a public company from the US, cooperates with Volvo. Some models will come with a LiDAR in the base configuration. These are "new LiDARs" with high resolution.
Innoviz, a 'startup' from Isreal, will supplies LiDARs to VW. Its angular resolution is (in its focus area) about 0.1 (or 0.2) degrees, which is sufficient for higher levels of autonomy and surpasses/equals the resolution of the expensive Velodyne sensors of the past. They will probably be in the same price range. Due to the limited FOV due to the technology, you will need multiply LiDARs.
Many new models from Chinese car brands will also ve equipped with a LiDAR. Most of them with Chinese LiDAR manufacturers like RoboSense or Hesai. Some are equipped by European manufactures like Ibeo/ZF. For example, there is the automotive sensor AT128 by Hesai. It targets normals vehicles (see price range above) and claims a similar performance (except for FOV, so you need multiple) like the Velodyne Ultra Puck (~$50000).
So costs of LiDARs are a not the very expensive obstacle they were in the past. The only problem could be that the new LiDAR manufactures cannot scale up series production. For example, Ibeo just filed for insolvency because they could not close another round after aggressively increasing spending in the past years.
> So far, that $100BN is a VC scam until proven otherwise.
A lot of the self driving tech has already made its way into safety systems in cars. Things like automatic breaking seem generally useful. There's a question about if it's worth $100B for research into partially autonomous safety solutions, but I don't think it's useful to attribute zero value to self driving research until we get full self driving (certainly some value is being realized already).
This is an important piece I have not seen addressed in the US. In places where there is snow and ice on the road a good part of the year self driving cars will need help from sensors and guide objects in some form. Perhaps sensors injected into the road? Humans barely manage in my area because people have a cognitive awareness and memory of the terrain. Road lines are often absent. Sidewalks are obscured. Even simple things like parking at the grocery store is relative parking and people just make a best-guess as to where a spot is.
I am also curious if any testing has been done in snow blizzards and squalls. Squalls can occur without warning and visibility drops to nothing.
That’s way too expensive on the order of trillions of dollars. Not worth it and not worth the extra regulation that will come with requiring new roads to support them.
This. I recall most predictions saying 2024 would be the tipping point, but even then I doubt regulations at the local and national level will change that fast.
These companies are swinging for the fences and instead striking out. A base hit would suffice for most people.
I just want a reliable car that can steer in the highway so I can eat a burger or answer a text message. GM’s SuperCruise is nearly there but has too many safety restrictions. It doesn’t need to work in inclement weather or construction zones. Being able to answer emails or watch a YouTube clip on a long road trip gives me back precious time that is worth paying a few thousand for.
Maybe there isn’t a train available. Maybe it takes much longer time taking the train. Maybe you don’t like sitting close to other people for several hours each week.
I can think of many reasons why train wouldn’t be an option.
AV is basically level 4 or 5 in the SAE classification ( https://en.wikipedia.org/wiki/Self-driving_car ). And those vehicles are either not existing yet (i.e. just marketing ploys), or far from it (requiring remote drivers, for example), or just research prototypes that have not been really tried at scale.
>or just research prototypes that have not been really tried at scale.
Furthermore, all of those research prototypes are geofenced, since they rely on extremely detailed mapping and lots of training data from humans driving the same routes. There is not a single AV in the world close to capable of driving on any road, at any time, in any condition, that a skilled human could drive on without ever having seen it before.
In my mind, that means we are indeed nowhere close to a true AV.
Readit News
My favorite example is that in 1987, the scientific consensus was that it would take "at least 100 years" and likely much longer to sequence the entire human gnome.[a] But the vast majority of it was sequenced by 2000, only 13 years later. Moreover, just two decades later, anyone could get their genome checked for known markers for pocket change by companies like 23andMe, founded in 2006.
Having some expertise and interest in AI, I regularly watch presentations by all companies working on self-driving and also look at the videos posted by beta testers online. While it's fun to watch the failures, I'm more interested in judging whether the technology is continuing to improve.
My perceptions contradict this article: (1) The technology is progressing faster than is generally recognized, with vehicles getting progressively better at dealing with edge cases and handling failures gracefully. (2) Judging by the videos I've watched online, Tesla is significantly ahead of everyone else.
Prediction: Before the end of the decade, this article will seem... short-sighted.
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[a] https://www.nature.com/scitable/topicpage/sequencing-human-g...
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EDIT: I edited my statement about 23andMe based on sausagefeet's comment below.
I agree with everything you said, but chuckled at this particular part (which is very wrong): > Tesla is significantly ahead of everyone else.
Everyone in the industry knows that Tesla is nowhere near the tip of the technology. What Tesla does is _fantastic marketing_. Their whole self-driving division is just a mechanism to sell more cars.
At a high level, this is why:
- The hard thing about self driving isn't the first 95%, it's the impossibly long tail of the last 5% with unique, chaotic and rare scenarios (think, a reflective citern tank with a reflection of the back of a truck transporting stop signs, or terrible weather illusions with fog).
- Doing well on the last 5% is where most of the energy from Waymo/Cruise goes (the two leaders by quite a margin).
- Tesla is camera only. Weather alone means you can't reach critical safety because of this. Cameras don't do fog well, precipitation well, or sunsets/bad lighting well (see many Tesla crashes on freeways bc of this)
- Tesla does well on the 95% and Elon is a marketing genius, with those 2 things it's easy to convince outsiders that "Tesla is significantly ahead of everyone else".
My prediction: before the end of the decade, cruise and waymo have commoditized fleets doing things that most people today would find unbelievable. Tesla is still talking a big game but ultimately won't have permits for you to be in a Tesla with your hands off of the wheel.
edit: formatting and typo
I was driving down the road as normal, 4 lane divided highway that's a bit hilly. Suddenly my car starts having what I can only describe as a panic attack saying I'm running a stop sign and blaring alarms.
It was detecting a giant 40ft tall red circle sign a bit away as a stop sign...
People tend to forget just how hard the edge cases in vision are!
It feels like Tesla’s main strategy is to add more data, more compute power, more simulation, and hope for “convergence”. Maybe that will work, but right now it feels like Cruise’s technology feels more mature and thought through.
It seems like Tesla has a huge advantage in terms of training data by leveraging a fleet of millions of vehicles.
Which is more valuable, experience or technology?
Different approaches lead to different paths to solutions, I am not convinced that either will be successful and not convinces Waymo/Cruise is ahead.
Unlike those, Tesla actually makes money and uses the technology stack in more limited forms.
Do people who work in this industry actually think they've solved 95% of driving scenarios because their software can manage driving in sunny California, Nevada, and Arizona?
Between one and two years ago, that was my perception too. But the rapid progress I've seen with Tesla FSD Beta over the past couple of years, and over the last year in particular, has forced me to change my mind. (Note: I'm talking only about Tesla's beta software. Tesla's production software is behind by dozens of versions and is without a doubt technologically inferior to Waymo and Cruise.)
Less than two years ago, I would have said FSD Beta could only deal with the "first 95%" too. Now, my perception is that FSD Beta routinely handles the first > 99% and fails only on < 1% of situations. Moreover, the failures have become more graceful -- e.g., the car will stop at intersections perceived as risky and ask the driver to confirm go-ahead by pressing the accelerator. If FSD Beta continues to improve, sooner or later it will cross the threshold at which it becomes safer than most human drivers.
Of course, IF I see new evidence that contradicts my perceptions, I'll change my mind again. There's no shame in changing our minds when the facts disagree with our views. FWIW, I'd love to see videos of Cruise and Waymo vehicles, filmed by tens of thousands of regular consumers driving autonomously on fully unrestricted roads, with zero editorial input from Cruise or Waymo.
That asset will probably be worth far more than the self driving system.
I saw the first Moon landing.
At the time, everyone, the experts, the public, everyone, expected us to colonize the Solar System within a few decades. We expected that fusion power would be too cheap to meter and that burning fossil fuels would be a thing of the past. We expected human life expectancy would soon rise to over a century in developed countries.
Serious, respected scientists said all these things, and everyone took them for granted.
None of these things did in fact come to pass.
Humans have not ventured in person even as far as the Moon in fifty years.
Our first atomic "pile" was 80 years ago in a few months, and we still don't have a fusion reactor. The tokamak was the big fusion design 50 years ago, and it still is, and we are better at them, but we still are nowhere near actually producing real power.
Life expectancy increases stalled quite fast, and then life expectancies started to regress. Americans have lost about two years of their life, basically because of their group mistrust of medical science.
It is simply diminishing returns on the amazing discovery that is the scientific method - it is unavoidable.
I don't think that's the actual reason for the decline, it's more likely another effect of the real cause. While I'm not American, it's the same story all over the world: our diet keeps getting worse, there is plastic everywhere which breaks down into micro plastics which end up in the water we drink etc. We're preparing our food with carcinogenic utensils (everything "non stick") and all of our western societies went from a healthy worker/academic career choice into mostly just getting exploited by the established players.
There have been a lot of societal changes since the post WW2 times, and summing that up into "losing trust in medical science" is a very confusing take, especially if you consider all the medical professionals that spread outright lies to profit. (The person that started the antivax movement was a doctor as a notorious example)
And drug companies that sold bad medicines (knowingly!) As they judged the cost of liability to be lower then the cost of not selling the drug.
Just to be clear, I'm not saying that any of these things are causing this decline either. Our societies have just changed too much since to make any confident claims about their impact.
Are you on Beta 10.69.2.3?
If I had to articulate my reasons:
* Judging by the videos available online, my perception is that many situations that were impossible for Tesla FSD Beta a year ago have become uneventful in recent weeks. Take a look at Chuck Cook's videos for example (I like the fact that he always highlights the failures).
* Judging again by the videos available online, my perception is that Tesla FSD Beta has encountered and had to deal with more crazy edge cases than any other system. A possible explanation for this is that for a long time Tesla FSD Beta hasn't been geofenced or restricted only to certain types of roads, like highways. You can test it anywhere in North America.
* Tesla FSD Beta currently has 160,000 individuals testing it without road restrictions. As far as I know, no other system has been exposed to similar open-ended large-scale testing.
* Occupancy networks look like a real breakthrough to me -- DNNs that predict whether each voxel in a 3D model is occupied by an object, using only video data as an input. I understood the high-level explanation of these DNNs on AI Day 2. I haven't seen anything like it from anyone else.
* Tesla's DOJO also looks like a breakthrough to me. I understood the high-level explanation of it on AI Day 2. IIRC, DOJO cabinets are 6x faster at training existing neural networks than Nvidia rigs, at 6x lower cost, so call it ~36x more efficient.
It’s a completely different problem space, like claiming someone built a train and therefore they can easily build self driving cars since they are both “driverless”.
Sequencing your genome is still relatively expensive, Probably around $15k. 23andme does not sequence your genome. They look at specific regions looking for specific markers. This doesn't invalidate your point of how great progress has been, though.
But it doesn't happen in predictable ways. A particular area of technology hits a wall and plateaus all the time, for myriad reasons. In 1950, you might have thought that by 2022 we would have done a lot more with nuclear technology or supersonic airplanes than we have.
We must be watching different videos. And experiencing Teslas differently. I see Teslas constantly slamming on their breaks on freeways, swerving across lanes, and avoiding collisions by the narrowest margins only because their owners took control before certain death. And that's just driving around in L.A. traffic, the YouTube videos are even worse. Tesla's vaunted camera-based system still can't recognize white semis or other broad, flat obstacles that a human or radar-based system would recognize instantly.
Tesla was ahead of their competitors, several years ago. Now they're way behind, and dropping further behind with every "update" that addresses the problems that got media coverage with "solutions" that indicate brittle, manual programmer overrides rather than any sort of scalable AI-driven capability.
And it's irrelevant that Tesla has 160,000 drivers on the road "training" the system, since they selected the drivers who drive in the safest road conditions using a "safe driver" metric that has no relationship to safe driving. This means that Tesla's "AI" (to the extent it can be called that) is being overwhelmed with tons of useless data that overtrains it to drive easy roads and with almost no training for difficult conditions or edge cases. For point of comparison, most vehicles today with advanced cruise control can drive the same roads that FSD can safely drive...but they don't need advanced AI to do it.
It doesn't matter how far ahead you were at the beginning of the race, it matters how far ahead you are at the finish line.
Part of this is the fault of Tesla's marketing, but you are wildly off mark. The cars you are seeing are Autopilot, not FSD. Most of them are even the even older, radar-based autopilot.
Tesla Vision has no issues detecting white semis crossing your path. Vehicles with radar, on the other hand, struggle with discerning those from overhead bridges, so if one appears close to a bridge, you're SOL due to whitelisting.
Tesla Vision in FSD is a much, much more developed version which has been excellent about detecting its environment, especially now with the new occupancy network. Its decision making needs work but you will notice, when watching all those videos, that detection of vehicles - even occluded ones - is not a problem at all.
Your comment about useless data is also wrong. They are experts in their field and they know exactly what type of data they need. Both Tesla and Karpathy himself have shown on multiple presentations that they focus on training unique/difficult situations because more data from perfect conditions is not useful to them anymore. They have shown exactly how they do it, and even showed the great infrastructure they've built for autolabeling.
Your claim about cruise control from competitors being equal to FSD is laughable. They don't even match Autopilot: https://www.youtube.com/watch?v=xK3NcHSH49Q&list=PLVa4b_Vn4g...
The 'given pace of discovery' I would read as an extrapolation using (then) available technologies, not as a scientific consensus about what would be possible using new technologies in the near future.
Automation is actually my area. Self-droving cars are in development for more than 50 years. In the 1970s it was more analogous technology that needed a lot of space. Fact is today technology is not that much better than that. Except, at the days it was highly supervised by professionals.
Many hardware devices we have in our cars today profited from those developments. Because radar and things are now cheaply avalaible. But self-driving is still not a thing. All the improvements in AI even didn't realy helped.
What many people seems to forget AI is about to detect (learned) patterns and to make decisions based on those patterns. But AI is completely unpreditcable on patterns it can't recognise. AI will classify those into a know pattern. But this is random and as such the judgedment is random. That is the big difference to a human, which can still cope with an unknown situation.
That is the reason why many people from the industry say: real self-driving cars will take very much longer than the average publics thinks and was told by Elon, Uber and others.
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Can we say that all the self-driving optimists that were completely wrong about the last decade were shortsighted?
Also, no idea what progress in biotech has anything to do with self driving. Your argument is that because some tech advanced all other possible tech will advance, seems like a logical fallacy.
AGI is a generalization and super set of self driving cars and has a far wider impact and set of people researching it.
The two problems seem equivalently hard in that fully autonomous vehicles must be five nines (?) reliably safe. That's a ridiculously hard problem.
Not to mention most of the edge case training it had in US may have little resemblance to say UK or AUS. And that is ignoring culture difference in places like India or China. I am not an expert in DNA, but I don’t understand how DNA sequencing isn’t a finite problem. Comparing to AV, in a real world with so many edge cases, you are practically looking at an infinite problem.
How can you possibly make this claim when the leader of the company you say is "ahead of everyone" said they'd have FSD years ago?
Also, FYI, watching Tesla fanatics promote FSD in edited videos isn't a good judge of the technology.
A lot of ideas just never pan out, despite considerable investment. Or, they get it to work, but proves to be of niche use. Progress happens elsewhere.
We have had self-driving trains and self-flying planes for decades. Despite this, train operators and airplane pilots aren't going away, and, if anything, are becoming even more important professions.
That's because they're in the "providing security" business, not in the "operating machinery" business. We pay pilots to take responsibility so passengers feel safe.
It's exactly the same for cars and trucks. The self-driving car racket is a scam, because they're not solving the actual problem society wants solved.
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However full sequencing IS available and runs around $1000.
If I recall right the first human genome sequenced cost about $1 trillion. But we now have much better algorithms, mostly due to algorithms from works such as Knuth's AOCP.
Dead Comment
Driving of cars is a massive cooperative game with high stakes, and autonomous cars essentially need AGI in order to play to a degree that is safer than a human with other humans. Fully autonomous cars would be sick, but IMO you'd need massive infrastructure changes (realistically restricted to cities/urbanized areas) if you want autonomous cars to work with anything less than AGI. Until companies start pursuing that, they are actually unknowingly using all that money to push for AGI and obviously coming up short because they don't even understand what they are trying to do.
Cities are by far the most complex relative to every other driving environment, in fact there is a good argument that, in cities, cars should be much more restricted because of health effects and traffic deaths, and the less complex areas (highways) are already the bulk of the drudgery in driving, but are much easier to automate, so why not do it for there?
My brother lives in the countryside (somewhere in the EU), and, as a driver, he shares the paved road just outside his house with the village’s cows (including his two cows). I don’t see any non-AGI system being able to negotiate that, as at times is difficult even for me, a reasonably AGI system, to make sense of it all when I encounter a herd of loose cows on the road.
The challenges self-driving cars have nothing to do with infrastructure and everything to do with the other moving objects on the road. It's not that the vehicles can't detect the other things on the road. It's that they can't anticipate reliably what they're going to do.
(However you may be correct that we need AGI or something close to it to do autonomous vehicles robustly)
Humans are good at predicting.
You don't consciously know you've seen the guy a couple of cars in front checking his mirror and his shoulder but you're hanging back because you just know he's going to pull out any second. The guy that's wavering a bit in the middle lane is about to dash across to the far lane of the sliproad that's coming up, clipping the zebra stripes a bit, because he's concentrating on the sat nav not the road, but you just know - out of all the other drivers in your space at the moment - that red Ford is the one that's going to do something boneheaded.
Autonomous Vehicles won't be able to do that, probably not ever.
And this is with stationary objects designed to be seen and easily grasped by humans.
This is -not- my line of work, so I have no idea if that's true. But if it is, I don't see how we could have perfectly safe self driving vehicles.
This to say: the future is having cars removed from cities entirely. Focusing on self-driving technology, or on electric cars as if they're going to "save the planet", is entirely the wrong direction imo.
Everyone is committed to being green as long as their lifestyle isn’t inconvenienced and they have the funds to buy the green equivalent technology.
It's also easier to avoid pedestrians as most can't fly.
It also could save on gas and energy as you can go directly as a bird flies to your destination instead of taking 20 minutes it takes 3.
Of course this would be a huge infrastructure ordeal as well probably and require a damn good system as you don't want vehicles landing on houses all over the place, but it'd be amazing for people with long commutes.
You could probably have airbuses that pick up like 30 people say in a small town and fly them to the city to be delivered individually by smaller vehicles locally. What took 45 minutes, now maybe takes 15.
These would be better if maybe electric with gas as an emergency backup system, and then just have good batteries and solar power fuel most trips.
Sounds somewhat authoritarian to impose your idea of the 'future' to inconvenience a large number of people.
That's not a problem when you're transcribing a video, but becomes a matter of life and death when you're driving a car.
This is really an astonishingly large claim without any evidence.
I question if you understand what AGI actually is? It's not "AI that can solve game theory" or "AI that can play cooperative games" - conventional video game AI's do this all the time in a myriad of permutations.
For others agreeing autonomous driving needs "AGI", first read what it is: https://en.wikipedia.org/wiki/Artificial_general_intelligenc...
Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn *any intellectual task that a human being can*.
It's a very difficult engineering problem but we don't need AGI to solve it.
Most of this research is funded in the US and the hyper individualistic Americans as a group don't believe in public transport.
American corporations perhaps aren’t as interested in public transportation, because there is no money to be made. And that is who is largely funding this self-driving vehicle research.
Beyond that, $100B doesn't go as far toward building transit as you might expect. For example, San Diego recently spent $2.3 billion on a light rail extension that's projected to have 34,700 daily trips by 2030. If you assume most of these are round-trips, it's serving fewer than 18,000 people. Spending $100B at this rate would serve around 750,000 people (0.2% of the U.S. population).
The most cost-effective form of public transit in most places is busses because they can reuse existing road infrastructure, and in the U.S., labor accounts for around 70% of the cost of operating busses. As a result, autonomous driving technology should be helpful in scaling public transit systems as well.
A fleet of self driving cars are realistically the only scalable public transportation because the costs increase with the number of people not the area to be served. Do people just forget how unbelievably spread out everything but the densest cities are? 24/7 bus service to within 0.5 miles of every house in my city is already impossible even if you allowed them to be on the every 4 hour. To replace cars people would realistically need them on the hour and want on the 20 minutes. Bet you my shirt at that point it would just be cheaper for the city to just run a free taxi service.
To do public transport right, you'd have to basically demolish the entire city and re-build everything from scratch to be friendly to pedestrians. Which is pretty much a non-starter, and even if you wanted to try would cost many orders of magnitude more than all of the self-driving car projects.
Its not about 'snapping your finger'. Neither Netherlands or Switzerland built their systems from 1 day to the other.
You need to make decision to change and then consistently and incrementally work on it. Put it in your standards and invest ever $ you have for new roads to that instead.
You need to change your tax policy so that horrible inefficient land uses like parking lots cost a lot more. You need to enable mixed use development so these parking lots can be built on.
> To do public transport right, you'd have to basically demolish the entire city and re-build everything from scratch to be friendly to pedestrians.
I'm sorry that is complete and utter nonsense. Like seriously, completely insane.
If you look into some urbanist and city planning literature you will see that lots of places where there used to be total car shitshows, are now beautiful. Often you would never have guessed that just 10-20 years earlier it was horrible road and a parking lot.
Again, small and incremental steps. Here are some really basic steps you can take:
- Remove parking requirements
- Slow speed of cars
- Don't allow turn right on red
- Make the lanes thinner
- Make the sidewalk broader, maybe add some trees
- Take one of the existing lanes and add painted bike lanes, later add protection for those lanes
- Rezone for mixed use (specially existing commercial zones)
- Change property tax policy to discourage sub-optimal land use
I could literally keep going on and on. Non of this, requires you to demolish anything.
Specifically for the US, there is whole movement about incrementally improving your city, see Strong Towns (https://www.strongtowns.org/). They have lots of podcasts and books. Specially: 'Strong Towns: A Bottom-Up Revolution to Rebuild American Prosperity'.
They also point out in detail with real data how these changes make your city safer and economically much better (They have some seriously amazing visualization of city finances that shows how such chances can improve cities).
And this is not some hippy organization, these are coming from a somewhat conservative small towns perspective.
Honestly your attitude of 'we are stuck with this' is horrible. I can understand frustration and bleak outlook, about the situation. But put your hope into incremental low cost change, not some techno futurism and you will be less disappointed.
As cars need charging, they would congregate at some charging plot outside the busy areas.
Personally, I rather like this vision as it combines the best of public transport and car traffic. Especially, if the existing, personally owned cars that just stand around 99% of the time vanish over time.
If you're still stuck with peak road usage nearly equivalent to that today, your goal isn't to solve prominent issues today.
Great so you have constantly cars driving form the city center to outside of the city, that for sure will cause no traffic at all.
Will be fun when people proposes new elevated highways out of the city so the self driving car can go outside of town to the coal power plant to charge.
The only thing worse then having vehicles driving around with 1-preson, is vehicles with 0-people. Its literally the most inefficient use of space ever.
It makes traffic worse, not better and it makes the city worse, not better.
How about this, a city optimized for walking and biking, where different parts of the city are connected threw buses, trams, subways or regional trains.
> Especially, if the existing, personally owned cars that just stand around 99% of the time vanish over time.
Turns out that cities where people can, walk, bike and take trains they don't own cars. Shocking.
Most cars spend most of their time, being stationary in a parking lot or a garage, 93 percent of the time to be more specific, or 23 hours/day. Buses spend a lot less time being stationary. The trade off here is the speed of driving. Cars offer an unlimited amount of speed, while buses do not. An optimal economic solution should exist in which the economic actors, i.e. people will figure it out after a lot of trial n error.
A crucial factor in self driving cars no one mentioned, is the data the machine uses to drive should be incorruptible. A blockchain which supports billions of tps, offer a solution to that.
Additionally road variables change over time, and data should change as well. Economic actors, not just people should feed the machines with updated data every day. That means that a marketplace of information is required, in which the most efficient economic actors with the best accuracy and the best reputation are rewarded, and the worst economic actors, who's data aggregation cause a lot of crashes, fall off the market.
A marketplace of information, doesn't exist for the time being, so there is no chance for self driving cars to be safe and effective.
That's $1Bn for the hundred largest cities in the world.
Assuming even 40% of that went to the US, that's $1Bn for the top 40 cities. That leaves out 22 entire states [0], and like 75% of the population...
Look to NYC, LA, and SF for what $1Bn gets you... It's about 1 mile of subway [1]. And it takes close to 15 years to build.
We wouldn't all be riding around on space elevators with materially better lives if this money was invested in subways or trains.
If you spent $40Bn on busses - you'd have to spend another $250Bn to pay people to ride them...
Self-driving cars will eventually change cities. I think there's evidence it's already starting to happen.
I don't think this money would've been better spent on trains, and definitely not busses.
What else are you thinking of?
I'd be interested in a better cost breakdown of bike lanes and how much it would cost to get a significant percentage of people in cities biking & scootering around - but I'm skeptical, and also, it's not mass transit!
NYC installed 29.5 miles of protected bike lanes last year [2]. I can't find the cost, but next year they're asking for $3.1Bn to build 500 miles of protected bike lanes, among many other things [3]. I know it costs less than $1M to pave a two-lane road one mile [4] - so a protected bike lane should be well under $1M - but then everything costs way more in the city...
If protected bike lanes cost substantially less than $5M per mile in the city (like $0.5M) - $40Bn could get you pretty far!
That's 80k miles of protected bike lanes! That's about 4x the amount of total bike lanes we have now.
Bike commute rates in NYC are decent (by US standards). I'd love to see a study on how much bike commute rates increased after these new lanes were completed.
Copenhagen has only 240 miles of bike lanes and 600 miles of paths for 70 square miles and 750k people [4]. That's enough to get 62% of people commuting by bike [5]!
For the top 40 cities, you'd be looking at like 80k miles of protected bike lanes for 4000 square miles and 81M people. That's better than Copenhagen!
That could potentially get you close to 62% of people biking instead of driving - just depends on if that many people live within 5 miles of work / school / going out. 5 miles being the average commute distance in Copenhagen [6].
62% of cars off the road in the top 40 US cities would DEFINITELY change my life for the better - but I'd be surprised if we could even get 15%. Still, it's something you could do in a couple of years - and for $40Bn - would definitely be worth it. But it's decidedly not mass transit.
[0] https://www.google.com/amp/s/vividmaps.com/map-of-largest-me...
[1] https://www.google.com/amp/s/www.nytimes.com/2017/12/28/nyre...
[2] https://www1.nyc.gov/html/dot/html/bicyclists/cyclingintheci...
[3] https://www.6sqft.com/council-wants-additional-3-1b-to-build...
[4] https://homeguide.com/costs/asphalt-driveway-cost#:~:text=Co....
[5] https://www.latimes.com/world-nation/story/2019-08-07/copenh...
[6] https://www.latimes.com/world-nation/story/2019-08-07/copenh....)
There are lots of places all over the world where $1B would make a big difference in many, many people's lives -- and where people eagerly ride the busses they actually have, which are often not that nice.
Do we need things to work in the USA for them to be worth doing?
Instead of wasting research money on computer networking, you could spend it on stamps and envelopes.
Hindsight is a wonderful thing. But you don't know what's going to come out of that research before hand.
We have our priorities all twisted.
I only ever take a car for trips outside the city.
There are over 1 billion cars on the road which need humans to drive them. How much are they used? I don't know. Let's say one ride per day. When cars turn into a service business, the "driver" will be software. What will be paid to the driver? Let's say $1 per ride.
That is 1B * $1 * 365 = $365B per year. Give that a p/e ratio of 10 and the value is $3650B.
So we could spent 30 times more and still break even.
$365B in value is probably a serious undercall. The only reason to complain here is if only $100B has been put in to the venture so far.
However, there will inevitably be conditions that require the use of general intelligence (rather than driving heuristics), and in those situations all you can do is pray the computer acts rationally despite not having GI.
I think self driving cars have already passed the test of "number of crashes" or "number of fatalities" per mile driven. But I don't think that's enough to sway the public, if every once in a billion miles a self driving car slowly drives off a cliff for no apparent reason.
What is that evidence, exactly? I agree that we might eventually get there, but the scale seems to be 50-100 years at this point. We are as arrogant as the researchers in the 60s who famously announced that absolutely perfect image recognition is only 1-2 years away - except the problem is several orders of magnitude harder.
just think of the huge economic value that faster-than-light travel will unlock!!
you're assuming your conclusion
If after another $50B or $100B spent some companies start to pull back on funding because they think diverting funds to other areas will give a better return (better batteries, cheaper manufacturing etc), it's likely others will too.
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Until LiDAR becomes more cheaper and these cars CAN drive themselves safely at night without supervision in any state at scale and as advertised like a robotaxi, then you're looking into multiple decades of these research prototypes being 'useful'.
So far, that $100BN is a VC scam until proven otherwise.
Thats how I read it, sorry. We made tremendous leaps in the past decade with improving automated driving, is it fully automatic? No. Does it mean we should somehow stop funding it? No.
I repeat my comment on LiDAR that I gave a few days ago. The gist is that LiDAR is cheap and you will be able to buy a LiDAR with sufficient resolution for in the next 1-2 years because it will be integrated in normal passenger cars for L2/L3 assistants. These cars are coming out now or in the next year.
LiDAR is finally getting cheap. OEMs (like VW) are very price sensitive. It is estimated the sensors from Valeo cost about 500 dollars. The fact that you see more and more normal passenger cars with higher resolution LiDARs means that LiDARs are getting cheaper.
The Audi A8 used Valeo's (with Ibeo) first generation low resolution LiDAR Scala 1 from the automotive supplier Valeo. Mercedes new models will be using Valeo's second (or third) generation LiDAR. All these are used for L2/L3 assistants. Valeo is a traditional large automotive supplier.
Luminar, a public company from the US, cooperates with Volvo. Some models will come with a LiDAR in the base configuration. These are "new LiDARs" with high resolution.
Innoviz, a 'startup' from Isreal, will supplies LiDARs to VW. Its angular resolution is (in its focus area) about 0.1 (or 0.2) degrees, which is sufficient for higher levels of autonomy and surpasses/equals the resolution of the expensive Velodyne sensors of the past. They will probably be in the same price range. Due to the limited FOV due to the technology, you will need multiply LiDARs.
Many new models from Chinese car brands will also ve equipped with a LiDAR. Most of them with Chinese LiDAR manufacturers like RoboSense or Hesai. Some are equipped by European manufactures like Ibeo/ZF. For example, there is the automotive sensor AT128 by Hesai. It targets normals vehicles (see price range above) and claims a similar performance (except for FOV, so you need multiple) like the Velodyne Ultra Puck (~$50000).
So costs of LiDARs are a not the very expensive obstacle they were in the past. The only problem could be that the new LiDAR manufactures cannot scale up series production. For example, Ibeo just filed for insolvency because they could not close another round after aggressively increasing spending in the past years.
When a human drives a car, their only sensors are eyes, ears, and maybe vibration. Somehow we manage to muddle through it.
Why do L4/L5 cars need anything extra sensors-wise?
A lot of the self driving tech has already made its way into safety systems in cars. Things like automatic breaking seem generally useful. There's a question about if it's worth $100B for research into partially autonomous safety solutions, but I don't think it's useful to attribute zero value to self driving research until we get full self driving (certainly some value is being realized already).
And perhaps we can design city centers to be car free.
This is an important piece I have not seen addressed in the US. In places where there is snow and ice on the road a good part of the year self driving cars will need help from sensors and guide objects in some form. Perhaps sensors injected into the road? Humans barely manage in my area because people have a cognitive awareness and memory of the terrain. Road lines are often absent. Sidewalks are obscured. Even simple things like parking at the grocery store is relative parking and people just make a best-guess as to where a spot is.
I am also curious if any testing has been done in snow blizzards and squalls. Squalls can occur without warning and visibility drops to nothing.
I just want a reliable car that can steer in the highway so I can eat a burger or answer a text message. GM’s SuperCruise is nearly there but has too many safety restrictions. It doesn’t need to work in inclement weather or construction zones. Being able to answer emails or watch a YouTube clip on a long road trip gives me back precious time that is worth paying a few thousand for.
It’s not perfect but I love that it makes it clear what it can and can’t do. It follows lanes on a highway and monitors you’re paying attention.
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If you don't like driving, don't drive.
I can think of many reasons why train wouldn’t be an option.
Correct me if im wrong, but Honda has done this?
https://www.caranddriver.com/news/a35729591/honda-legend-lev...
https://global.honda/newsroom/news/2021/4210304eng-legend.ht...
Toyota:
https://www.bloomberg.com/news/newsletters/2021-08-02/toyota...
And Cruise:
https://www.motorauthority.com/news/1132494_cruise-opens-up-...
I don't really see how this all qualifies as "not close to AV", when really, it looks quite viable (with some already driving autonomously...?)
Furthermore, all of those research prototypes are geofenced, since they rely on extremely detailed mapping and lots of training data from humans driving the same routes. There is not a single AV in the world close to capable of driving on any road, at any time, in any condition, that a skilled human could drive on without ever having seen it before.
In my mind, that means we are indeed nowhere close to a true AV.
Obviously we don't know to what extent they have remote drivers but you wouldn't be able to run such a service if every ride required them.
[1] https://www.youtube.com/channel/UCP1rvCYiruh4SDHyPqcxlJw
Also Tesla. You can see real advancements in these videos of Tesla FSD Beta [0].
[0]: https://www.youtube.com/c/AIDRIVR/videos
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