That's on me; I set the bar too low. When I asked "How", I was hoping for someone to literally write up an alternative and better way of phrasing this, which would still work as part of a corporate press release.
For example - allow ticket resale only through the official platform and cap it at the original sale price.
Another approach - check IDs at the door and only let the original ticket purchaser through.
The real problem is that scalping is insanely profitable for Ticketmaster & co. They take a cut of the original sale and every subsequent transfer, most of them at highly inflated prices, from both buyer and seller. Why would they give that up?
> allow ticket resale only through the official platform and cap it at the original sale price.
That obviously doesn’t work because money can still change hands outside the official platform, unless you mean resale to a random buyer selected by the platform, in which case the resale is not terribly different from a refund and restock for any event where scalping is a problem.
You simply can’t stop scalping if you allow resale. Heck, people even attempt to scalp things where there’s no official resale mechanism (e.g. I change my id at this second, you immediately change yours).
A lot of festivals in Europe use ticketswap, your ticket gets raffled off to a list of people interested in buying it. It's the only way to sell your ticket. It's a the original price plus a small fee
Self driving cars are not going to be accepted if they have only marginally better success rates than humans. Just look at the news. Every minor self driving incident is endlessly magnified by the media while millions of human-caused accidents are just a part of life. That's just how our brains work. All major decisions are made primarily based on emotion, not analytics.
In the case of driving and flying a significant part is the passenger's agency. There are many common sense things you can do to reduce your own chance of crashing your car. Drive defensively, don't speed, don't drive drunk. There is very little a passenger in an AV or on an airplane can do to prevent things from going wrong. And it turns out we really don't like having no agency over our own travels and that's why we have such high safety requirements for airlines — but not general aviation — and now AVs.
Human accidents don't get treated as "just a part of life", serious human driving errors are often considered so egregious that the person making the error picks up a driving ban or even a custodial sentence.
So it's actually entirely rational that the bar for companies to be able to ship software that makes those fatal errors without consequence other than an insurance payout should be higher (especially since when fatal error rates can only be estimated accurately over the order of millions of miles, driverless systems are more prone to systematic error or regression bugs than the equivalent sized set of human drivers, and the cost and appeal of autonomy probably means more experienced drivers get replaced first and more journeys get taken)
There are over 6 million auto accidents in the US per year. How many of them make the news? I'm willing to bet that most people don't even know about pedestrian deaths that occur a few blocks away from where they live, at intersections they walk through every day. Meanwhile the same people will read about how a self driving car got into a fender bender on the other side of the country and confidently proclaim "this technology isn't safe, I'm never going to use it".
Sure, autonomous vehicles are new, experimental technology so they're inherently more newsworthy, and news reports aren't a substitute for data - though in this case it's a good illustration that AI can make errors humans would be less likely to even if it is objectively better than the average driver at parking and not speeding.
This not in any way refute my argument that would also be irrational to set the safety bar for autonomous vehicles as "marginally better than humans" , given that AI failure modes are distributed completely differently from human ones, a sufficiently serious edge case bug triggered only once every hundred million miles might make the autonomous system more likely to kill you than humans[1], and for that and other reasons its almost impossible to quantify whether a particular firmware update actually is safer than the average driver (takes around >10 billion miles to approach statistical significance if you're worried about fatalities rather than only weakly-correlated scrape rates, and then you've got to wonder whether the driving conditions are well matched). Especially if we're using that statistical argument not just to license the vehicles for road use but to absolve autonomous system developers of potential criminal liability for actions taken by their software, a luxury humans that wipe out pedestrians with similar driving aberrations wouldn't get.
[1]the US had 1.38 fatalities per 100 million vehicle miles in 2023, skewed significantly upwards by DUI and other egregious driving behaviour. Less than half that in other countries with different road conditions and also more in-depth driver education. Humans have a lot of car accidents, but they also drive a lot of miles.
Same reason there's less gridlock when people obey traffic lights and other rules of the road and don't brake randomly. If every car on the road drove itself then there would never be traffic.
Driving through an obviously flooded street thinking "I'll easily make it" and getting stuck in the middle? Yeah, these cars have achieved human level intelligence.
That being said... it's actually somewhat uncommon for humans to drive into flooded streets. To the degree that people think it's notable enough to take videos and post them to social media. I don't have the data, but would be interested to see how many times per passenger mile travelled human-directed and remotely-operated vehicles like Weymos drove into flooded streets.
I can appreciate the cameras and lidar on the Weymos don't give their remote operators a lot of good data about the depth of water on the road-way. As you point out, humans in cars often don't get this right. I think the humans that don't drive into deep water are the ones who a) give any amount of water on the roadway a big NOPE and b) people familiar with the local environment and use multiple visual clues to judge the true depth of the flooding.
It shows up on social media when it’s a rare event for that area. It’s uncommon but “happens all the time” here in California in the deserts every heavy rain either because locals forget how deep the flood control washes are, or because tourists just drive into them thinking its a straight road, despite all the signs and warnings posted around them.
As far as I can tell from these articles, driving into a flood has happened twice to Waymos, once in Texas and once in Atlanta? It does seem like it's pretty uncommon.
Ask the car, in the sense you can, why it drove into the water.
Then ask the human.
I'm not sure you'd walk away the idea that they have equivalent intelligence. The human at least knew the water was there and took a risk, the car, presumably, had no idea what was in front of it and drove into it anyways.
A decent welder should be able to turn out a trailer hitch <=> outboard motor bracket in under 15 minutes. It's not like you'll need much more than a modest fishing outboard to get through flooded spots.
jeep snorkels are for air intakes for engines. electric cars don't have air intakes. they have air cooling for batteries... I suppose you could snorkel those.
You'd need to ensure every electrical connection is in a waterproof location which I'm pretty sure is not a thing for any standard car manufacturing. Cabins are also rarely watertight.
AFAIK your best bet is a diesel with a snorkel, and hope things have dried off before you need to restart the engine.
That's...the joke. The humor is in the absurdity of recommending an addon to the car that utterly would not work and would look ridiculous. It's layered on the fact that Jeep snorkels look sort of ridiculous even on the vehicles they were designed for.
This is why I personally feel like Tesla's approach is more likely to "win". The fundamental blocker to self-driving cars is not sensing / sensor fusion, it is intelligence. And the Tesla approach seems much more likely to achieve functional intelligence than Waymo's.
While I agree with basically all of this, and find the FSD on my Tesla to be quite useful, a question pops into my mind.
Why can't Waymo ALSO develop the same smarts and just also solve the sensor fusion issue such that they can use the right set of sensors in the right environmental conditions, and then leapfrog Tesla's capabilities?
Because they don't have a fleet of millions of people labeling the data for them and paying for the privilege of doing so. Waymo has about 3700 vehicles. Tesla has millions. Waymo only operates in known environments and collects a very limited range of data. Tesla collects data everywhere that people drive their cars.
I thought about this and I think it boils to how the model is trained.
Tesla trains it models from actual drivers purely based on (input) Vision and (output) actuators - Brake, Steering, Accelerators.
Human output is based on what they and the camera sees. So, it's a 1:1 match.
If Waymo were to do that, it'll muddle the training set. The Lidar input may override camera input.
I always struggled when Musk mentioned Lidar will make it ambiguous. It didn't make any sense to me why having a secondary failback sensor messes things. But, if you put it in the training data context, it absolutely makes sense.
This is an interesting viewpoint, but isn't it also solveable?
Just because the human in the scenario only took vision as input, why does that matter to the training data and the model? The actions are the same.
To put it another way, what about all the cultural context the human had, or the sounds, smells, past experiences at the same intersection, etc? Even Tesla can't record this, but I'm not sure that matters.
E.g If the driver brakes because they saw a pothole, and Lidar captures someone biking 200m away on their own path, it may mistakenly put more weight on brake causation to the 200m away object (because large moving object) vs the pothole.
I'm exaggerating, but I hope you get the point. It isn't even conflicting sensor signals about the pothole, but conflicting information about the causation. With vision only there is no conflict for the training data. This was my Aha moment. Multiple Sensors are absolutely important for fallback and extra safety, but screws up training that are based on Human Drivers
I think Elon himself doesn't understand this and hence can't articulate it, while just repeating whatever his ML engineer has said.
That is vastly preferable to slamming into the back of an emergency vehicle because the cameras are dazzled by the strobes, or slamming into tractor trailers because the cameras were blinded by sunlight. Or slamming on the brakes because the car thinks a shadow in the road is a physical object...
> such that they can use the right set of sensors in the right environmental conditions
Because this part is really hard, and that's why Tesla abandoned the fusion approach. You cannot possibly foresee all the conditions in which LIDAR or any active sensor will malfunction/return wrong data/return data that's only slightly off for that ONE specific time. And even if it doesn't, you need to trust it to not return noise. And when it does return noise, how do you classify it as noise?
Cameras are passive sensors - they get whatever light comes in and turn it into an image. Camera is capturing shapes that make sense to the neural nets: it's working. See all black/white/red/cannot see any shapes? Camera is not working, exclude it from the currently used set of sensors or weigh it less when applying decisions, because it's returning no signal (and yes, neural nets have their own set of problems).
EDIT: cameras also provide more continuous context: if 1 pixel is off, is clearly bright red in a mostly-green scene where no poles can be identified, the neural net will average it out and discard it as noise. If 1 pixel says "object" in LIDAR, do you trust it to be correct? Perhaps the ray just hit a bird or a fly, but you only see a point, it's a lossy summary of the information you need.
They could in theory. If they put at least as much emphasis on the AI side as Tesla does. Or if someone else cracked vehicle AI wide open and left it open for them to copy, and then they did exactly that, and found a way to bolt on their extra sensors in a useful fashion while at it.
As is, Waymo's playing it smarter than Cruise did, but they're not all in on AI yet. So I don't expect them to "leapfrog Tesla" in that dimension - and it's the key dimension to self-driving.
I don't know. Cost might have been part of it but I also recall hearing that he thought since humans can drive with two eyes and no LIDAR then the car should be able to do the same thing.
I got downvoted for saying this last time the topic came up but constraints focus a project. It’s best to start work with as few variables as possible, and only add new ones when absolutely necessary.
I'm working on a similar problem in computer vision and we're quickly approaching the point where our pure vision work is better than our Lidar supported track because we've had to deal with the constraints instead of having a crutch to lean on.
I agree, but these are also the exact constraints that lead to an early leader getting overtaken by a longer term, yet better set of plans. Not saying that's the case here, but given how much success Waymo has had so far, over really everything Tesla has produced, says quite a bit about the likelihood of the approach, even if it's not yet there.
Sensor fusion isn't free. Lidar requires more power consumption and more onboard compute. Cycles that could be spent on "intelligence" are instead being spent on sensing.
I like both approaches. The fact that both exist is a clear win for the rest of us as consumers.
Tesla's approach seems like a bet that A) AI will reach human-level driving intelligence before lidar becomes cost-efficient, in which case their current sensors will be sufficient to achieve at least human-level performance; and B) ~human-level performance will be sufficient to achieve large-scale consumer and regulatory acceptance. Waymo seems to be taking the other side of that bet.
If Tesla is right, their solution should scale faster, and they can worry about adding superhuman sensory capabilities later. If Waymo is right, all the Cybercabs that Tesla is pumping out right now are destined for the scrapyard, or at best will spin their wheels in beta testing for years while Waymo speeds ahead.
Tesla is putting its money on the bull case for self-driving as a whole. If Tesla wins that bet, it means we all get access to a useful version of the tech years earlier. If Waymo wins, that's great too, but it means that for better or worse lidar will be a bottleneck to scaling the tech.
The whole thing is basically a rehash of Intel vs TSMC on EUV in the 2010s.
The hardest decision a company, especially in tech, can make is to disrupt an immensely successul business of their own before their competitors can. Apple killed their biggest cash cow, the iPod, to push a smartphone. Netflix killed its entire business of DVD rentals in favor of streaming. Microsoft stopped selling software in boxes and pivoted to SaaS. Similar to all of these the business of typing words in a search box and getting 10 blue links was dead the moment ChatGPT got popular.
