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Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

electrek.co

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Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#121

Post by [deleted] »

dogma1138 wrote: post #72
mattnewton wrote: post #59

Earlier quoted context omitted.

I don't understand your argument, so maybe this is off base, but if you are saying people in industry aren't replacing their supercomputers with commodity gpu's, you're wrong; both apple and google have massive purchase orders for commodity nvidia gpus because they aren't just cheaper, they are better at this application. And I imagine other companies are as well. Edit: "replace" is probably not the right word, this…

They aren't buying consumer GPU's they aren't buying the NVIDIA dedicated servers, but they aren't running Geforce chips either. If nothing else is that because you cannot virtualize Geforce line GPU's, there is no CUDA Direct or NVLINK support etc. If you are telling me that Google is buying Geforce GPU's and flashing the bios with a custom bios ripped off a Quadro card so they can do PCIe passthrough in a hyperviso…

[deleted]

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#122

Post by varelse »

lightcatcher wrote: post #105
mattnewton wrote: post #59

Earlier quoted context omitted.

I don't understand your argument, so maybe this is off base, but if you are saying people in industry aren't replacing their supercomputers with commodity gpu's, you're wrong; both apple and google have massive purchase orders for commodity nvidia gpus because they aren't just cheaper, they are better at this application. And I imagine other companies are as well. Edit: "replace" is probably not the right word, this…

> both apple and google have massive purchase orders for commodity nvidia gpus source?

Rather than attempt to out these downlow Ge Force deep learners, why don't you ask yourself why you can only buy Titan X Pascal from NVIDIA itself.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#124

Post by vonmoltke »

ChuckMcM wrote: post #63
mattnewton wrote: post #58

Earlier quoted context omitted.

Why not shoot for better than human performance and "cheat" any way possible along the way? To paraphrase a quote I can't remember by who, do we care if a submarine "swims"? Besides, with even with lidar, the problem is hard enough.

I agree, being better than human is a sales point that I expect to see in brochures. One of the ways I would expect that plays out is self driving transport cars for high value targets like world leaders and drug lords. "This car will respond faster, and more accurately, to get you to safety before a human driver even knew there was a problem." That said, John stated that without LIDAR you couldn't adequately meet th…

> One way to evaluate how far the image processing pipeline has come is to look at research on how well it can classify images. And in that space, in the research, it is doing better than humans [1]. As I've said elsewhere I think LIDAR was a crutch that worked well to cover for weaknesses in classifying images, but I recognize that the crutch may no longer be needed (certainly Tesla and Nvidia are trying to make that case).

I have worked on systems for automatic target detection and classification. Systems have a long way to go before they reach human-level accuracy in classification. Even on "artificial" images like radar return maps humans are still slightly better, and many systems pass the underlying processed data to the human operator for final review. Tracking and classification in a dynamic environment is hard. It's even harder when trying to rely on passive sensors to do it.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#125

Post by TaylorAlexander »

onion2k wrote: post #87
TaylorAlexander wrote: post #54

Earlier quoted context omitted.

Humans don't use accurate depth maps to drive. Machine learning is great at complex algorithms if done well.

Humans don't use accurate depth maps to drive. Humans are very good at estimating distance from a combination parallax visual cues and experience. We don't need to have seen a specific model of car before to judge how far away it is with a high level of accuracy.

Machine learning techniques are also being used to evaluate depth using parallax and other cues.

And humans aren't good at estimating distance. They're good at estimating relative distance which is different. Some people claim we need LIDAR because cameras can't give us accurate depth information but humans don't use accurate depth information.

Are you saying these systems need to have seen a specific model of car in the past before they can determine its distance? Certainly I've seen systems that do not require that information.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#126

Post by TaylorAlexander »

kchoudhu wrote: post #60
TaylorAlexander wrote: post #54

Earlier quoted context omitted.

Humans don't use accurate depth maps to drive. Machine learning is great at complex algorithms if done well.

Do the newer systems assume network connectivity to a backend processing facility? Personally, I view each additional layer (network, someone else's data center, machine learning) to be something that can fail and put people in danger. Assurance via local brute force is much more reassuring for me.

No. The current Tesla Autopilot v2 system has 8 cameras and must be able to react in milliseconds. They do the computation onboard with an Nvidia Drive PX2 computer.

https://www.tesla.com/presskit/autopilot#autopilot

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#127

Post by slv77 »

sushirain wrote: post #92
ilaksh wrote: post #41

Earlier quoted context omitted.

What fundamental problems remain to be solved for self driving cars?

Vehicle detection: detect any vehicle, even from the side, even if its shape is rare, even at dark, etc. This could be solved with data. Control: when to yield, without watching the face of the other driver, etc. Edge cases: obeying a police officer, yielding to an ambulance, cooperating with other cars.

And the non-technical side.

Liability: who is liable when the car kills a pedestrian?

Driver engagement: How do you safely transition from automatic control to manual control.

Maintenance: Will the manufacturer be obligated to provide software updates for the life of the vehicle? Even if that vehicle is 20 years old? Even if newer software is 10x less likely to kill a pedestrian?

Cost: Are the costs one-time or will there be a maintenance fee?

Licensing: Does the manufacture have the right to disable functionality after the purchase? Do they have a right to your data? Can they sell that to your insurer?

Regulation: Who certifies systems? How do they test them? When is a system "good enough?"

All of these things sound trivial compared to the technical challenges but it's these kinds of non-technical challenges that killed the small-airplane market in the US and are still unresolved 65 years later.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#128

Post by pakl »

visarga wrote: post #74
ChuckMcM wrote: post #55

Earlier quoted context omitted.

I don't disagree, both humans and ML are bad at classifying things they haven't seen before[1]. However that reasoning doesn't disqualify either vision only auto driving systems or machine learning. Both statements are true: "Computer driven cars may crash, even fatally, when they encounter a situation that they do not recognize." and "People driving cars may crash, even fatally, when they encounter a situation that…

There is a difference though - humans understand the surrounding state, computer vision is not quite there. It can recognize things, and in NVIDIA's case directly generates steering commands without going through the intermediate step of building a model. Humans build models of the world, and such models allow us to predict the future to a little extent, and explain the reasons behind a situation. Humans can intuit t…

You've hit on a key insight. Predicting the future (even by a bit) turns out to be a very powerful learning signal for building models of the world.

It won't work on a traditional feedforward neural network but if you have feedback everywhere it appears to work.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#129

Post by pakl »

Hydraulix989 wrote: post #95
dave_sullivan wrote: post #94

Earlier quoted context omitted.

> The key difference is learning in animals occurs by breaking things down in terms of modular concepts, so even when things are not recognized new things can be labeled as a composition of smaller nearby concepts. Machines cannot yet do this well at all and certainly not as flexibly. Actually, that's pretty much what deep learning is doing. For instance: https://papers.nips.cc/paper/5027-zero-shot-learning-through..…

Well, not with traditional feedforward networks (LeNet, etc.). You can't run the classifier and find tires, then wheels, and then a car; but you do get composition of features.

Agreed -- If you get away from traditional feedforward networks by adding recurrence throughout, then at least there is some chance of learning scale-free features and compositionality.
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