Nvidia and Audi aim to bring a self-driving AI car to market by 2020
techcrunch.com
techcrunch.com
Nvidia demonstrated their platform with many sensors like 10 cameras, ultra-sonic sensors and at least one LIDAR.
Where as Tesla is using a sensor mix with radar, ultra-sonic sensors and like 7 cameras - the LIDAR is probably still too expensive even for a 70+k car (the big one you know from Google cars cost $70k, the smallest one cost at least 7k).
It will be interesting to learn about Audi's sensor mix, and what LIDAR product they choose.
With regards to the missed shipments - I agree, that is a red flag. And when a company gives a timeline, and then that passes and they change their answer to "soon" it's concerning. Of course, it's CES right now - maybe someone will make an announcement in a few days. Last year Quanergy hyped up their sensor, but when they were pressed it turns out their demo sensor was actually not solid state.
A seven layer seal? That could surely only be broken by the darkest of magics.
1: https://techcrunch.com/2016/11/15/new-lidar-package-makes-it...
Enhanced Autopilot available mid December 2016... oh wait.
One claim versus another, both optimistic. I'm not going to believe Audi or Tesla until I see it.
The automated system can control the vehicle in all but a few environments such as severe weather. The driver must enable the automated system only when it is safe to do so. When enabled, driver attention is not required.
How bad will the roads be when we have thousands of people all very unsure of their driving skills driving in conditions that are dangerous to begin with? Seems quite the interesting issue.
People will still be wildly overconfident of their driving abilities.
The advent of cruise control has not made people incapable of using the accelerator. You occasionally meet 16-year-olds who enable it on any 35, 45 or 55 mph road when they reach the speed limit, because holding constant speed is a skill. But it's not difficult to refrain from using it for a while to brush up on the skill.
But I have done far more miles on highways in good conditions where an L4 system would be helpful. The tech is still useful even if it's not a complete solution.
People need to stop saying this. It doesn't matter if Level 4 specifies this, and someone claims a certain car successfully passes the test. No car in AI infancy is going to be 100%. Drivers must absolutely be prepared to take over in under half a second at all times. That's not likely to change for 30-50 years.
I found it quite socially irresponsible for him to mention during the keynote that it would be fine for his 80+ year old mother to continue driving when she becomes no longer capable of managing the vehicle on her own. It might be perfectly fine in a few decades. Not by 2020 though.
There should be some healthy dose of inspiring "propaganda", but this got out of hand - everybody claims "they have it".
I am so sick of this endless stream of lies, that I'm not even going to read the article. The next thing I am going to read on the topic would read something like "X has a viable self-driving car - it's hitting the market later this year.".
That won't happen till General AI. How would you do something as simple as drive the car onto a lift to change the oil without pedals and a steering wheel?
Or there is a concert and you need to drive onto the grass to park?
People talk as if there's some infinite number of unique edge cases when driving a dang car, but there simply isn't that much you actually do with a vehicle. Real deal no-drivers-license-required automated vehicles are coming pretty soon; maybe not in 5 years, but definitely within 20.
We are in a completely different situation today.
In other words, now you need a hi-res, current map to show. Where does the data for that come from? (Oh wait, don't tell me: Magic!)
TL;DR: "remote control" != "self-driving"
Come to think about it - even 'just park somewhere' is remarkably complicated. I don't quite understand why self-driving cars should need external high quality maps and can't make do with onboard cameras as human drivers do.
But then the parking requires either a high-quality database of valid and off-limits parking locations, or the ability of the car to read the signs on parking lots and generally be able to distinguish private from public parking.
The only thing I can think that would sell me on the idea is a really low price point, e.g. sub-$10k to get a new, driverless, road-only vehicle. But I would only want that in addition to a "real" car.
There will be an app for that?
Hey, James Bond was driving from the backseat with a Nokia 9000 (?) a few movies ago.
You know that there are 10s of thousands of self driving cars on the road RIGHT NOW, that you as a consumer can buy tomorrow.
Tesla sells thems, and they work.
That is absolutely self driving.
If half of your driving time is done by the car, that is still a massive benefit to consumers.
End to end autonomity, Is even better, of course, but I think that everyone is selling short the in between 95% self driving.
Even TINY features, like autobraking in emergencies, has the potential to save tens of thousands of lives every year, if it got fully deployed in all cars.
And for jobs, highway driving is 90% of what truck drivers do. If you have highway self driving (which we DO, right now!), then there goes most truck driver jobs.
But fully autonomous driving is still transformative in entirely different ways that makes it hard to treat the evolutionary steps as all that exciting even though many of them probably should be.
Could you ELI5 what the massive benefit is? The system requires you to be alert and focused on the road the entire time.
We know for a fact e.g. from the aviation industry that having a human sitting alert but doing nothing increases human response time and decreases the correctness rate of their actions, which is one reason why airplanes don't regularly land on autopilot, even though the tech has been there for a long time. In fact, auto-land on airliners is only used in very low visibility, low wind conditions, or for training to ensure pilots use it at least semi-annually.
The huge benefit of autopilot on airplanes and ships is that for normal operation out on the ocean or up in the sky, it's sufficiently safe that the pilot/captain can focus his attention elsewhere for long periods of time.
The truck drivers can just get up out of their seat and do something else. There goes 90% of truck jobs, as now you only need a driver for the first and last parts of a trip.
Other benefits: saved lives. The more highway driver cars we have, the less that humans will be driving, and the less chance that a human will make a mistake.
The level 2 and 3 stuff on a Tesla, is already a better driver than most humans.
Ehm, how do you come to this conclusion? Statistically, Autopilot Teslas are no safer than the NHTSA average, at least so far.
If I say "black swans are extremely rare in this part of the world", and you spot one the very next day, the Bayesian in you would assign a low probability to my statement being true, even though that's based on a sample size of one.
Anything fits within "autonomous except when not autonomous" - even the crudest cruise control.
If those were true our phones and notebooks would have power until eternity already.
In Homer's time the Greeks viewed the unconscious as an external (divine) force, the most famous example being Telemachus's sneeze. In a weird way this feels more intellectually honest than the thing we do.
> we often manage to convince ourselves
But more complex than figuring out if another person is lying about a very nuanced and opaque decision they made?
[1] http://spectrum.ieee.org/transportation/self-driving/after-m...
There is the presumption that all you gotta do is gather enough training data and shazam!, you got a self driving car, but that's bollocks. There is a tremendous amount of elbow grease that foes into developing and validating an AV to six sigma reliability. Six sigma might not even be good enough for something as safety-critical as a driverless car.
Nvidia stock (NASDAQ:NVDA) is x4 in the last year and probably will continue escalating..
I can't go into much detail as it's not my knowledge obviously, but i can say that GPUs are viable/required for some of the coins out there. Based on his word, at least.
http://www.zf.com/corporate/en_de/press/list/release/release...
But they have more-or-less been doing that the whole time. Its just now they have more sensors and deep learning so they are going to be autonomous a higher percentage of the time.
So I think as soon as they start rolling it out more and more Tesla owner will have more common 100% autonomous trips with some exceptions for weird traffic or weather.
I think this is risky in some ways but overall its more ethical than delaying because the only way to train/engineer for the exceptional situations is to get a lot of vehicles running the system and training on data. Waiting a few years means people die from human error and you're unlikely to see massive improvements to the system that would make up for that.
One thing people will realize eventually is that we create a lot of driving situations that are structurally unsafe. For example, it is accepted to speed past pedestrians or bicyclists a few feet away on the sidewalk or bike lane. No level of AI advancement can prevent some random horrific accidents in that case. Could be as simple as a pedestrian crossingthe street a little early. People are not going to tolerate AIs going 5 mph anytime a pedestrian is nearby but thats the only way you could prevent fatalities in some situations. That is part of the 'low confidence situations' the nvidia guy mentioned. So actually we need laws to protect autonomous tech companies in those situations or that will delay situational deployment and lead to more human error deaths.
We know this is coming, and this technology will improve the lives of so many people in the long run. Maybe it's from Nvidia and Audi, maybe Tesla, Uber, Google, that dude who launched and failed and ran away to China, who knows?
I'm excited to think about what opportunities will start to open up once humans don't need to spend 2+ hrs a day with their hands on the wheel :)
"Audi and NVIDIA developed an Audi Q7 piloted driving concept vehicle, which uses neural networks and end-to-end deep learning. Demo track at CES 2017 in Las Vegas."
The computer delivers more than 40 times the processing power of the previous system, running an Tesla-developed neural net for vision, sonar, and radar processing
The quality and performance of the system is mostly determined by the data, not the raw computing power, so it's worth comparing them as they can be very different.
Not only that, there are _inherent_ pathologies associated with using deep nets in the first place.
For instance, the network might only be used to decide among a series of actions, and those actions can still have limits (such as “car cannot travel faster than X” or “Y cannot change more than 3 times per minute”, or whatever). There is still an abundance of attention put into safety, as usual for the auto industry. It isn’t just a brain hooked up to an engine that is allowed to run rampant.
One would expect the output of the neural nets to be encoded in those.
1. "Keep the car in a straight line" => The road is closed or there is a detour, or the lane markers aren't visible (or incorrectly marked like on the Palo Alto stretch of the 101 where there are two sets of lane markers; one new (the "actual" markers) and another old but barely faded and still very visible.
2. "Turn right, 10 degree" => With what steering radius? What if the road is bumpy or sloped and the driver has to compensate with the wheel?
etc.
These corner cases are everyday occurrences and lend a necessity for something far more sophisticated than a mere ad hoc model to represent and control human-level driving behaviors with a finite set of rudimentary actions.
Actuator inputs like steering and pedals are almost always represented using continuous values rather than discrete sets for these very reasons.
An alien race landed on earth and demands to play a game of Go. We only get to play one game with them. If they win, our planet is destroyed.
Who would you trust to play for the human race if this scenario happened tomorrow? Lee Sedol or AlphaGo? Remember that we do not completely understand how AlphaGo reasons, it is still a black box to us.
Also, one shot for the survival of humanity is very different from billions of iterations of driving situations every day for the foreseeable future. A complete understanding of the system is much more valuable when you have the opportunity to iterate.
I'm not necessarily arguing for one approach; just saying that the analogy doesn't really apply to the case at hand.
To be honest, I would have felt the same way two years ago. Seeing all these recent advances in the field has slowly changed my mentality.
Last time I checked we can talk to Lee Seedol and ask him to explain things. We can ask him questions. We can have an intelligent conversation with him.
Example: Would you fly on a plane designed ultimately by a human vs an impenetrable black box?
Also there is a spectrum. Let us not pretend otherwise.
1. One end: No explanations.
2. Middle: Seometimes false explanataions and sometimes true explanations.
3. Other end: Always true explanations.
Are we really saying the middle is completely useless?
We have no way to determine whether an explanation is true, false, or simply a post-hoc rationalization. We like to believe that we can, but we're just fooling ourselves.
If we have no way of determining whether something is true or false, then I can say the same thing about your own statement quoted above. I can just say it is false and go on with my life.
I sincerely hope you realize the obvious self contradiction :D
Logic 101. :)
Logic 201. :-)
> I never claimed that we have no way of determining whether something is true or false
Everything can be cast as a declarative statement.
Matches(givenStatement, actualIntention).
N.B: When I was younger I had no idea that others did those and thought they were fucking with me when they were describing this.
That means we have to start with physics.
If adaptability is the metric, machines still didn't beat humans.
AlphaGo was trained on some set of movements (as were the other AIs that helped train it). It is not a pure game theory construction, so there is no reason to expect it to be as good against an alien as it is against a person.
BR: I think there is! And I think there's an exciting opportunity here to understand how to combine robust control and reinforcement learning. Being able to build systems from data alone simplifies the engineering process, and has had several recent promising results. Guaranteeing that these systems won't behave catastrophically will enable us to actually deploy machine learning systems in a variety of applications with major impacts on our lives. It might enable safe autonomous vehicles that can navigate complex terrains. Or could assist us in diagnostics and treatments in health care. There are a lot of exciting possibilities, and that's why I'm excited about how to find a bridge between these two viewpoints.
/s
You're welcome ;)