Tesla is building its own AI chips for self-driving cars
techcrunch.com
techcrunch.com
By going to a custom ASIC with inferencing. I have little doubt about Musk's claim that it'll offer a 10x improvement over the current stripped down PX Drive2 variant for deep learning tasks. If I'm to decrypt Musk's branded terminology and take him at his word, then they'll be going from about 8 TOPS to 80 TOPs with that they'll be able to get even deeper into the no man's land that's even more overkill for basic ADAS but still not nearly enough to handle full self driving safely in all conditions, though maybe they'll get L3 highway out of it.
Nvidia's upcoming Pegasus board will supposedly do 320 TOPS, and their competitor, Intel/Mobileye's EyeQ5 will likely have comparable specs. These motherboards are designed to replace Robotaxi trunks brimming with ~$150k in liquid cooled compute that draw 3-4000 watts.
I use the word "deluded" above realizing that I might be completely wrong. If they pull it off I'll eat crow and give them props. But I just don't see it happening.
Having said that, I find it hilarious that people on here have such immensely strong opinions about something that doesn't exist. They'll go at length about how LIDAR is a hard requirement, about how many FLOPs you need, etc etc etc. Fact: FSD doesn't exist and no one has it. Nobody knows which approach will work and if more than one approach works no one knows the timelines or economics of the different approaches and very certainly no one knows the market impact of these. But people start thinking they know the future and then become very emotionally vested in their make-believe stories.
It's actually quite simple: companies sell you stuff, buy it if you want it/need it. Today Waymo sells me nothing, Cruise/GM sells me nothing, Tesla sells me a great BEV with Level 2 driving assistance. I got the car, but not the level 2 because it doesn't fit my driving needs. If they start selling level 3-4-5, I'll buy it if the price is right and I need it.
It's certainly not nothing
But there's no need to pick on GM, they all have self-driving tech capable of killing you.
Assuming autonomy is make or break for Tesla in the long term: if Tesla fails at it your stock will still be worth something (versus a useless upgrade). If Tesla succeeds you'll make more than enough to cover a retrofit.
Tesla will fail. But they will fail at 5% of their goals, and achieve 95% of that goals. It will still be called 'Failure'.
To avoid that 'Failure' tag, other companies won't even try.
Do this process enough number of times. And it will appear like Tesla is failing all the time, and yet achieving almost 100% more than other companies.
The problem here is in defining success in terms of binary outcomes of success and failure.
I think “fraudulent” would be more accurate; it was factually false, it was at best entirely unfounded if not actually knowingly false, it was material to decisions of people to purchase the self-driving add-on.
The Autopilot page [1] still states the following:
"All Tesla vehicles produced in our factory, including Model 3, have the hardware needed for full self-driving capability at a safety level substantially greater than that of a human driver."
Personally, I’d refuse to implement self-driving vehicle without at least a ”backup LIDAR” to check vision system results. Otherwise you are forced to assume stuff like ”things at stand still are either above the road, beside it, or just shadows”, causing crashes when there is suddenly a stopped car in front of you. (If you didn’t do that assumption, you would be dodging shadows and other clutter..)
[source: I’ve been researching vision algorithms in a related field.]
Because that isn't how people judge distances, at least not distances beyond a few feet in front of their faces. Plenty of people with only one eye do very well. It is a difficult problem because we use a variety of techniques and 'hardware' when estimating distances and speeds. Car companies are trying to do with one tool (ie lidar) something we do with many.
I guess the same logic applies directly to self-driving cars as well..
At any but very close distance, don't humans mostly use a combination of lighting cues, a priori knowledge of actual size vs. apparent size, motion parallax, and other flat-image cues instead of stereoscopy?
But, yeah, LIDAR cuts through all that, too.
Anyway, if LIDAR becomes small and cheap, Tesla can just strap it on.
Whether this will succeed is another question.
Supposed to be fully autonomous since, what, 18 months ago? How about that SF->NY demo?
He also added that it would be sort of cheating, because that's a very defined route and he prefers it to be able to be much more dynamic. i.e. pick any two US cites and the car will drive you there.
http://www.autonews.com/article/20180208/MOBILITY/180209770/...
Google's autonomous efforts far precedes Tesla, yet they have not out in the market yet. I would say on the contrary, they made significant strides in a short amount of time even after a complete overhaul Mobileye (AP1) to an in-house solution (AP2/2.5) https://vimeo.com/192179727
Waymo has been operating a real actual passenger service for over a year now. They drive tens of thousands of miles a day. They're "not out in the market yet" only in the sense that they're not charging money for it yet, which they seem to plan on doing soon.
https://www.bloomberg.com/news/features/2018-07-31/inside-th...
I can only assume then that you have some kind of insider info to support your claim?
Ergo, logically, if Tesla has not performed such a test, it is strong evidence that Autopilot is not currently capable of passing it.
Here's the direct link to my notes from the event: https://www.reddit.com/r/teslamotors/comments/7iczp7/elon_mu...
[1]: https://www.theregister.co.uk/2017/12/08/elon_musk_finally_a...
[0] https://www.zachaysan.com/cars and apologies for the length; it was a very stressful time in my life.
[1] http://support.blackberry.com/kb/articleDetail?articleNumber...
When you have 100,000 self-driving cars on the road, and someone figures out how to hack them, and drive them simultaneously into crowds, that will be a BIG BIG problem.
And this problem isn't just about self-driving cars. It is a fundamental issue with all algorithms. It's a lot harder to hack humans to do bad things (although it can be done with sustained messaging and propaganda). But algorithms can be compromised and exploited.
I'd say, it's probably easier to hack individual humans, but - like everything in computing - hacking algorithms scales well, while human factors generally don't.
First, they're not physically inspectable by every Tom, Dick, and Harry. This means it's easier to obscure interfaces and other potential areas of attack. Not state-actor proof, but probably ISIS-proof. Second, they only get software updates while parked at an airport, so it makes MITM attacks harder (though not impossible). Third, they're in the air where death isn't a sudden movement away. Pilots can override the system and fly it manually. Fourth, they're heavily monitored with errant flightpaths reported to militaries around the world (to stop another 9/11).
We have none of these safeguards for self-driving cars and there are going to be hundreds of millions of them.
https://www.computing.co.uk/ctg/news/3020901/dhs-team-manage...
How autonomous systems will make anything different? Why do you believe people aren't currently hacking cars?
I don't intend to be critical at all, I believe this is a very interesting point too that should be discussed. (I hope to check the links on my interval).
> How autonomous systems will make anything different? Why do you believe people aren't currently hacking cars?
It might make always on data connections near-mandatory (to get maps data, etc). At least in my car, the insecure embedded computers are air-gapped from the internet.
Part of the reason is that, in current ecosystem, companies will find ways in which self-driving requires being constantly on-line and connected to vendor's server. Off-line processing is not in fashion these days.
If we use a more narrow definition of weapon, e.g. a tool optimized for the primary purpose of injuring or killing people then it certainly is not a weapon.
It's not useless; the damage that can be caused varies by degree between things. Then you have to look at two cases - suitability for object to be used as a weapon, and the damage it can cause on accident. Unlike knives or hammers, both those factors are very high for cars.
The fact how dangerous cars is is very much underappreciated by people in general, as evidenced by the number of morons on the road. We already lose hundreds of people daily in the US alone because of this; now we're trying to add another class of drivers into the mix - algorithms written by greedy optimizers caring primarily for short-term profit and being first to market. This should give us some pause.
I'm not saying this technology is not possible or not wonderful, but I think the current ecology of self-driving efforts is unhealthy. We have a for-profit race by companies, many of which can't be trusted with getting software right, and most (all?) of them pursuing self-driving capabilities by means of half-understood brute-force black boxes the neural networks are.
> and the damage it can cause on accident
You are conflating (un)safety of a tool used in the way it is intended (kitchen knife = cutting a steak) with accidents (cutting a finger) and with malicious use (stabbing people). Those three categories are not the same for object-that-may-act-as-weapon and object-designed-as-weapon.
Conflating them collapses the number useful things we can communicate.
So are you concerned about Tesla intentionally building killing instruments? Or potential for accidents? Or the potential for intentional misuse?
> The fact how dangerous cars is is very much underappreciated by people in general, as evidenced by the number of morons on the road.
Cars also provide immense utility. If all they did were providing the thrill of speeding then they would probably be banned as too dangerous. One of the tradeoffs is the overhead of enabling people to drive. We could drive down the number of morons by requiring astronaut training for vehicle operators but again, that tradeoff seems too harsh and it's more efficient to occasionally let people die in traffic accidents than letting them die because nobody qualified as ambulance driver.
> We have a for-profit race by companies, many of which can't be trusted with getting software right
In the short term this may cause more deaths than necessary. But on the other hand it might be the quickest way to find a winner and then hold the rest to the same standard. As long as the experimental fleets are small they are just a blip in the statistics. Right now they should be equated to the yearly batch of first-year drivers who have an inherently higher risk profile due to lack of experience. We still accept them on our roads in the expectation that they improve.
What is important is to make sure that they are as good as or better than humans once they roll out in large fleets.
The latter two.
> We could drive down the number of morons by requiring astronaut training for vehicle operators but again, that tradeoff seems too harsh and it's more efficient to occasionally let people die in traffic accidents than letting them die because nobody qualified as ambulance driver.
I don't think this is the real reason. You don't need astronaut-level training for vehicle operators, just more than the ridiculously low standard of today, and more importantly, much stronger and harsher enforcement of traffic laws. I doubt that this will reduce the number of qualified ambulance drivers.
I suspect the real reason we tolerate so many morons on the road is path dependence. When cars first appeared, they were rare, slow and safe. In the couple of decades it took to get to the present density and speed of cars, it became a social status symbol, and something politically impossible to rein in.
> What is important is to make sure that they are as good as or better than humans once they roll out in large fleets.
I'm afraid that with self-driving tech based on neural networks, with no ability to inspect and verify what's going on, we'll eventually have to eat the risk and roll them out in large numbers before we know they're as good as humans.
I do not agree that neural networks are a "black box" with "no ability to inspect and verify". Even putting aside the many methods to understand what a neural network is doing without running it, at core, neural networks are well tested instruments. That's how they learn-- by testing themselves.
Obviously it's possible for a neural network to have odd behavior in circumstances not accounted for but that was always going to be possible at the level of complexity we're talking about here.
We're talking about cutting edge technology here -- and I agree with your general sentiment. I just don't agree with pinning the blame on "... based on neural networks". The same factors would apply to any codebase of this complexity.
Name three :).
> neural networks are well tested instruments. That's how they learn-- by testing themselves.
Last I checked, neural networks are well-tested in a sense that if you throw a big database and a shit ton of compute at them, they'll learn to accurately work within that database. Step out of it, and all bets are off. We're better at this than we were 30 years ago - good enough to apply this technology to consumer-level products in which mistakes don't really matter. I'd be wary of applying even current neural networks to safety-critical tasks.
> Obviously it's possible for a neural network to have odd behavior in circumstances not accounted for but that was always going to be possible at the level of complexity we're talking about here.
The problem is that with NNs, the odd behavior is usually totally unexpected, and you can't really inspect the network beforehand to discover the possible ranges of error-generating inputs. Everything works fine but every now and then you get a patterned sofa classified as a zebra, or a car + little noise classified as a toaster. And then there's no obvious relation between multiple misclassifications, because the reasoning structure of the neural network is implicitly encoded in its weights.
> The same factors would apply to any codebase of this complexity.
I think there's a fundamental qualitative difference here. A codebase can be complex, but ultimately it has a structure, and usually (in case of ML) represents a well-understood mathematical structure. Neural networks have simple code, and the whole complexity is hidden in opaque matrices of numbers, where even single changes usually have global effects.
I'm not trying to dismiss NNs in general; I just don't trust them in applications where health and safety is at stake.
I'm sure that making the primary means of long (greater than walking) distance transportation for the majority of the population more expensive and higher stakes is going to work out great in the long term. I can see the parallels with healthcare. Creating yet another part of life where a single screw up that is capable of ruining the financial well being of someone living slightly better than paycheck to paycheck is not going to do positive things in the long run.
But who determines which is which? The letter of the law really, really, really sucks when it comes to traffic law. I haven't collected data but I'd wager that pretty much nobody follows the letter of the law for an entire drive from A to B
>For example, treating speed limits as suggestions instead of hard constraints
That's more human than reckless. Outside of places with Orwellian enforcement (I'm looking at you Europe with all your cameras) they really are. The vast majority of people go at a speed they feel comfortable in the conditions. This is why (conditions permitting) traffic flows at 80 even when the sign might say 55. People only follow the speed limit when they feel it's a comfortable speed. This is why the general recommendation is to set speed limits for the 90th percentile speed. If you don't do this on highways you get people doing the (inappropriately low) speed limit in the wrong lane. Passing on the right, tailgating and all the other things caused by traffic friction which is more stuff for drivers to keep tabs on and that decreases safety for all. There have been studies on this (Google "traffic friction" and filter out everything that has to do with literal friction). If anything speed limits on multi-lane roads should be raised to reflect the speeds people actually drive. I hope we'll see more dynamic speed limits in the future since they'll help a lot.
>overtaking in places where it's not allowed.
If every 100th instance of an illegal pass (usually on the shoulder when waiting for someone who's stopped to take a left turn or on the right on the highway) in my state resulted in a ticket it would probably be about a year before half the state's drivers hit the three strikes cutoff and had their licensees revoked.
The picture I'm trying to paint here is that aggressive enforcement of existing laws would probably be bad for the population at large because people break traffic laws in inconsequential ways all the time and that stronger enforcement of them would just screw people over (unless of course you enforce them so well that important people get screwed which would result in the laws changing) and discretion isn't an answer because that just results in profiling.
America banned alcohol by an amendment, pretty much the hardest political barrier we have
It's just an excuse IMO
Any object that can be remotely programmed to drive itself at high velocity into a target, yes.
A person can explain "why" they did something when an accident happens. I've spent a lot of consulting hours helping unwind the "why" of ML/AI models, and we bill regardless of whether we can actually find that answer.
Putting aside by personal bias that I think Tesla is functionally incapable of doing anything well, lets pretend they deploy FSD - I think the fact that in that 1 accident to every 100+ human accidents you can't find "why" will override the fact that there are xx% less accidents.
Just from my experience consulting in healthcare and working with actuaries during the Obamacare debates and modeling out the impact of that -- people just honestly aren't open to statistical arguments when it comes to human life or emotional issues.
(Not saying they're wrong, me and my wife completely disagree about so many issues where it's emotions vs statistics, I think she has a very valid point on many of those - what I'm trying to say is this is going to be a philosophical debate more than a statistics debate so Tesla should gear up accordingly).
Also, while I agree that you can "program" a human, you can't program them to jump up and down and touch their toes (so to speak)
the truth is, people have a lot of experience with other people. we can usually predict behavior of other drivers on the road, and can even predict pathological behavior or know how to handle it. that is a major point. when machines and software fail, they can fail in big, unpredictable ways. humans fail in fairly predictable ways. none of us have this experience with neural networks or machines.
i barely trust my computer to work on a daily basis, much less a vehicle.
and lastly, when a human is driving, fault is usually clearly and easily assignable. how fault is assigned in the case of machines driving is not clear at all.
of course safety is important. and i agree that verifying driving ability is well behind where it should be. elderly and inexperienced and inattentive and just plain bad drivers are major problems on the roads.
and fault certainly does matter. aside from legal responsibility, we are emotional beings. there is a difference between getting hit by a drunk driver versus a true accident. that affects people in real ways. as a motorcycle driver, i will be terrified of these automated vehicles. i am already terrified of human drivers, but i can often predict their idiocracy.
no one wants to be killed by a machine, especially one built by wide-eyed engineers.
If they succeed, it means slightly improved efficiency and cutting NVidia's profit margin out of their cost structure. If they fail, it would cost billions, and obscure chip bugs could put lives at risk. That risk/reward doesn't seem worth it unless you really know what you're doing and are confident you will succeed.
Of course, it fuels the hype machine in the short term.
Not quite... The chip was designed by Jim Keller, who was a key player in designing the highly successful AMD Zen architecture and Apple's A4 and A5 SoCs (pretty much a legend).
Jim is known in the industry as having an unquenchable thirst for really hard problems. Solves them, leave the company to recharge and goes on to the next company/project.
This seems like the biography of Elon Musk's companies. He gets a crazy hard problem that people say he and his companies can not do, then he tries to implement it, often falling well behind and well short of the goalposts due to lack of foresight and overambitious scheduling. There were naysayers the whole way, including just as loud as there are now if not louder, and yet his companies' achievements and networth continue to climb.
Spaceflight was solved decades before Elon was born. Reusable rockets weren't a hard problem--they were simply a problem the incumbents were unwilling to address because it would have massively cut into their revenues and profits.
Electric cars actually predated ICE cars. The issue with EVs was always the charging infrastructure, which Tesla solved...by simply throwing a lot of money at it. (Not hard, just resource intensive.) Their batteries are built by Panasonic.
Boring Co literally is just a used tunneling machine. They have literally not done anything with it except test it out below the SpaceX parking lot (and the innovation in boring would come not from the tunneling but with the post-tunneling construction of the tunnel walls, stations, dirt removal, and ultimate extraction of the boring machine).
Even at Paypal, their biggest innovation was developed by Musk's biggest pre-Paypal competitor (Thiel's company) before they merged to form PayPal.
But of course you know much better how simple all these things are. For you to disagree with that is just emberecing yourself.
The same applies in a lesser degree to your other comments. Do you seriously belive that the origonal EV in 1900 was the end of development and all Tesla did was 'throw money at it'? That is the hight of stupidity and you again wont find a single expert on the subject agreeing with you.
Also the battery chemstry is co-licenced by Tesla and Panasonic with the design of the cells and even part of the chemistry being inhouse at Tesla. Panasonic can not sell this technology.
So when you are spreading baseless FUD, at least get your information correct.
You seem to have lost all rationality in context to of this question.
Reusable rockets weren't a hard problem
If you have your own ASIC it's a differentiator that's hard for other players to compete with. They'll take this to potential investors (or stockholders the "market") and say this is a big expensive thing we have that nobody else has. It's expensive and time-consuming for them to replicate and we also own a lot of IP that they would need.
You and I might know it's bullshit. That's not true of the investment market in general.
Only thing I can imagine would be allowances for wider and deeper neural nets.
https://www.youtube.com/watch?v=-KxjVlaLBmk
The higher the speed of the system, the less it has to predict in the future and better can adapt to unforeseen situations. It's basically using the world as a model, for free.
Yeah I think this is it. My best guess, it's 200fps vs 2000fps for the same workload. As Tesla's data model gets larger and its computations get more complex, that 200fps will start to sag.
2000fps will provide a lot more wiggle room to do higher level computation on each frame.
36 / 200 frames = 18cm travel distance between each calculation.
So you either can calculate much more each 18cm or calculate shorter distances.
I think a shorter distance is not very useful but calculating more might be.
I think n = 8
...or more cameras, with better frame rates over a lot of cameras. My guess is that the fps rate is over all the cameras, so 10 cameras means going from 20fps per camera to 200fps per camera.
As a simple example in a different domain, years back I found that the best way to improve Tesseract's OCR accuracy was to ensure that I didn't feed it images at more than 150dpi because it would sometimes misrecognize dust, paper texture, etc. as characters.
I'll believe it when they successfully install it to the previous gen car. More often than not it turns out that some other hardware is incompatible with software improvements or has bugs and full system is not functional anyway without full upgrade.
Will this mistake never go away?
and talking about this now seems like another desperate attempt at generating hype.
But from the sounds of it this chip goes beyond that. It sounds like some kind of RAM/GPU hybrid with matrix-specific instructions specifically targeting NN inference for Tesla's specific style of NNs, which as a first guess I take to mean "lots and lots of layers."
In the call they mentioned increasing their investment in the chip team and technology, which is a sign that they're confident it's going to pay off.
By limiting the context of use, specialized hardware can perform better than CPU/GPU. I think his thinking relates strongly to ASIC crypto mining hardware https://en.bitcoin.it/wiki/ASIC .
"bare metal" is just a shorthand for the level of access to hardware that does not require a kernel/OS.
It's not a unique name in any shape or form. There's a smartphone "manufacturer" from Serbia that uses Tesla as its brand name: https://tesla.info/en/
In particular the semiconductor manufacturer Tesla Roznov and later TESLA SEZAM is today a subsidiary of ON Semiconductor (anecdotally they manufacture the power transistors used in Tesla Motors drive units, but I have no way to verify that claim).
What are the real-world benefits going from 200 to 2000 frames a second? 200 seems quite fast.
I don't make a habit of crashing cars but I did have one glancing head on crash, combined speeds around 125 m.p.h. and it was only centimetres that meant I did not wipe out that family and trash the car I was in.
As a consequence I would say that this 10x frame rate really is quite game changing, particularly for on-coming vehicles.
Early autonomous test vehicles used LIDARS because camera and compute tech are not at the same level they are today. It's a legacy system and more of a shortcut. In essence, Object recognition > LIDAR judging distances.
Yeah it's true that a vision based system is much harder to solve. However if Tesla cracks it, it will pay dividends.
As has been discussed in some depth here before you commented, computer vision hooked up to some ML doesn’t come close to human eyes controlled by a human brain trained for many years.
Early autonomous test vehicles used LIDARS because camera and compute tech are not at the same level they are today. It's a legacy system and more of a shortcut. In essence, Object recognition > LIDAR judging distances.
Waymo is the cutting edge if you believe what people here say on thread after thread about them, and the raw stats. I’m sure they’ll be interested to hear your view of their “legacy” tech.
Yeah it's true that a vision based system is much harder to solve. However if Tesla cracks it, it will pay dividends.
Finally, reality. Yes, if they do something they’re yet to do, and their cars stop ramming stationary objects, then maybe something else will happen. No one is contesting that, there are just doubts given the state of Autopilot vs. leaders in the field, not to mention Tesla’s dodgy PR in general, and in the face of fatal incidents in particular.
Even if they "crack" it, they will just be spending much more money to get the same benefits as a $100 mm wave system.
In the future, vision systems will only be seen as a gimmick of this era.
Radars beat both ladars and vision systems at phase, speed, and distance estimation.
In other words, you get everything that vision systems would offer except color, without having to do any processing to calculate depth, so you can go straight to the object-recognition and navigation processing.
LIDAR > vision systems.
Maybe driving on the roads we have now with computer vision alone needs strong AI, maybe simpler standardized roads and vehicles are needed, who knows. I think that strong statements about this should be backed by evidence, not gut feeling. And I'd like to see that evidence coming from the company that promises their customers their cars have all the hardware they need.
Same age for walking around town unsupervised: the Japanese do and don't seem to lose many.
My original point was focused on the technical feasibility of driving with limited sensor input, which is physically possible.
Whether or not it takes a week, a month, or a million experience-years is a computational and algorithm problem.
All of which (based on previous technological progression) appear to be tractable.
E.g. the disaster that is the first major snowfall, every year, or automatic transmissions, antilock brakes, and traction control systems becoming standard
The human can only reliably be trusted to keep the car between the lines, stop appropriately, and occasionally make turns. Which is something much simpler to compete with!
https://hips.seas.harvard.edu/blog/2013/01/30/what-is-the-co...
Our intuition about the future is linear. But the reality of information technology is exponential, and that makes a profound difference. If I take 30 steps linearly, I get to 30. If I take 30 steps exponentially, I get to a billion. - Ray Kurzweil
People once said the same thing about fire lances.
The current model makes assumptions that sometimes turn out to be false. So yea it's not going to get to perfection, but level five only needs better than human not perfection.
* Granted I am likely a bad driver, but I don't think I could do this well.
See you can try to move the goal post, but as soon as they would be saving lives by taking tired or drunk drivers off the road that's very useful.
I am curious on what numbers are you basing this on, the Tesla PR numbers are not valid statistics.
I trust insurance companies to have good data, they think it improves safety when you compare rates.
This cars require the driver to pay 100% attention, by law and by Tesla agreements so isn't obvious that driver+drive assist+expensive car has better average numbers then driver + average (maybe old) car ?
I would like to see the numbers of how many times a driver had to intervene to save the situation
In terms of paying attention I really doubt most people are doing this very well. I am going to be stuck in the car either way so by default I am going to be watching the road, but as long as my relative stress level drops that's a huge win.
PS: You can make your own estimates. If people pay attention sufficient to avid accidents 99% of the time the car might be in 100x more accidents without drivers assuming those drivers where perfect. Drop that to IMO more likely 75% and it's closer to 4x at the absolute best case.
Same with Tesla, this is why I would like to see the number the drivers had to intervene and prevent crashes, without this number the only conclusion is about driver asist and not autopilot/self driving
Further, the difference between OK and an accident is normally fractions of a second. So, my suspicion is humans are really bad at this role and only catch the most obvious cases. AKA crap I am an a corn field, not hmm it's not slowing down enough I need to apply slightly more breaking power or I am going to hit that guy simply because you don't have time to react with the second situation.
Having said that, humans may be preventing a lot of issues well before they turn into accidents. The 'wanted to make a wrong turn down a one way street' is something a person can deal with easily, though it's not necessarily going to cause an accident.
Anyway I am waiting for some government institution or maybe the lawsuit to get more data then we can use that to make informed conclusions.
IMO, the largest risk is an over the air update killing a large number of people.
You can't invent numbers and statistics.
Remember the Tesla that crashed in a side barrier and killed the driver, people could reproduce the incident in the same spot , you can see it on youtube, so without a driver and with say 1000 Teslas driving trough that section in the problematic interval you would get at least 1000x more deaths.
Only if you include the people that reproduced the incident mentioned above you will get the Tesla stats much below human drivers.
Computers fail in different ways than humans do. But, that does not mean you can't reason about failure modes. If you ask someone to pay attention to something for an hour without doing anything most people are just really bad at this. Based on that I am rather shocked how well Tesla's systems work as I was expecting vastly more problems.
Could I have flipped to far in the other direction probably. But, I base things on my experience and the data I have available not just unchanging gut feelings.
If you pay for self-drive you will get an update.
Elon has always said it should be enough and if not you get an upgrade.
But some nuonce is lost in all his statments ofter it got threw the hype and couter hype cycle.
The benefit of high margins and sophisticated engineering is you can get away with a lot of unknowns and promises.
Its only Elon Musk haters that somehow want to proclaim every mistake of him as some sort of fall from grace where finally his auro of diseption will be lifted from his victims.
I outright snorted at this. Time to market is not why you build a custom chip instead of software. Is this article written by a complete bonehead?
Tesla has it's own hardware platform. Anyone with an ounce of sense will know that was always going to happen because of power consumption, cost and performance. The Nvidia Drive platform is designed to be a quick way to get to market with FuSa. The requirements for fully autonomous modes are estimated by some to be over 10x higher than the top CPU/GPU offerings.
They also published the quote about iPhone leading in performance and go-to-market because of their custom SoC efforts, and they mentioned Tesla's SoC program manager Pete Bannon, but they didn't even bother to connect the two ideas: Pete Bannon was the guy who led Apple's SoC efforts to develop the first 64 Bit ARM chip for mobile.
They also didn't mention what Elon said about now being able to run all the cameras at full resolution and full framerate.
This article could have been so much better.
IOW, yeah, it’s a crap article, but not for this reason.
... so that they can create performance-intensive features that nobody else in the market yet has, because the off-the-shelf hardware won't allow it. Due to power consumption, cost and performance issues. And this custom hardware is being developed to solve them.
You're basically saying the same thing in different words. I mean, what else do you do with better power consumption, cost and performance other than new, better-working features? You think Tesla will sell cars to the mass market with impressive computer specs?
Apple's own custom hardware has enabled it to experiment with performance-intensive features unlike other smartphone manufacturers, Tesla is going for the same strategy.
Will existing Tesla owners be able to swap the computer? Or is the swap happening only inside the factory, to the next generation of the Teslas they're building?
This may be one way in which Tesla's approach differs from Apple's.
This is not what Musk said on the conference call. Surely some bonehead reporter is writing this. Musk said the latter, precisely to go from current 200 fps speed to something like 2000 fps (I might have the numbers wrong, but it's a 10x improvement) and that they have been doing this in stealth for the last 2-3 years. They expect the cost of the new chips to be the same as that of the current nVidia lineup. That got me thinking though. What are they currently using - the Titans ?