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Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

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Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#71

Post by xfs »

colorincorrect wrote: post #7

Perhaps this paper provides an explanation? https://arxiv.org/pdf/1608.08225.pdf "The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality and polynomial log-probability, and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as d…

This is really good. Deep learning right now is giving off a kind of illusion of domain-independent general intelligence that can solve any problem, so it would be really helpful to have some theoretical characterization of the specific problem domains it's good at and ones it's not good at.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#72

Post by seanmcdirmid »

dkarapetyan wrote: post #69
seanmcdirmid wrote: post #67

Earlier quoted context omitted.

You keep assuming a self driving car strongly resembles a traditional industrial robotics at all. The actual actuating part of a self-driving car is fairly easy (just turn the wheel, apply the breaks), it is the control system and sensing that is key to success, the former being software, and the latter (LIDAR) that Google is heavily invested in while Toyota is very late to that game. Right now, if I was a betting ma…

I think you're downplaying the actual engineering required. Robotics is more than just actuators. Robotics folks have been doing sensor fusion, control, and learning for much longer than software ML folks have been doing those same things. I'm saying a software company is not gonna crack the market. Software companies neither have the expertise nor the engineering discipline. Boston Robotics and Google have been famo…

http://www.toyota-global.com/innovation/partner_robot/histor...

Industrial robots, and humanoid robots for entertainment purposes. Whereas Google has many of the founders of ML, not to mention its experts. Is Toyota, who already seriously undervalue software, willing to pay top dollar for those people when their average SDE makes You also seriously misunderstanding the amount of engineering discipline needed to build modern software systems.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#73

Post by vhold »

60654 wrote: post #3

> Driverless long-haul trucks are apparently just a few years away, and the main worry now is not so much the safety of these trucks but the specter of unemployment facing millions of people currently employed as truck drivers. No, no they're not. We have some lane tracking in good weather etc., but we are still decades (or more) away from full level-5 autonomy that would make drivers behind the wheel unnecessary. Bu…

We don't have to get to level 5 to start automating trucking. In fact level 4 would likely be better than many drivers on that road now. Level 5 is an ideal we probably won't reach for decades, self driving cars better or equal to the best most attentive drivers in all situations.

I think the level system is also a bit over simplified. Level 4 highway is very different from level 4 city, and level 4 highway would be enough to automate a lot of trucking.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#74

Post by dkarapetyan »

seanmcdirmid wrote: post #72
dkarapetyan wrote: post #69

Earlier quoted context omitted.

I think you're downplaying the actual engineering required. Robotics is more than just actuators. Robotics folks have been doing sensor fusion, control, and learning for much longer than software ML folks have been doing those same things. I'm saying a software company is not gonna crack the market. Software companies neither have the expertise nor the engineering discipline. Boston Robotics and Google have been famo…

http://www.toyota-global.com/innovation/partner_robot/histor... Industrial robots, and humanoid robots for entertainment purposes. Whereas Google has many of the founders of ML, not to mention its experts. Is Toyota, who already seriously undervalue software, willing to pay top dollar for those people when their average SDE makes You also seriously misunderstanding the amount of engineering discipline needed to build…

Misunderstanding? Given that my day job is to literally build tools and infrastructure for "modern software systems" I don't think I'm misunderstanding things at all.

But whatever I can say on "modern software" has already been said much better by Alan Kay so I'm going to defer to him for the rest of this conversation.

I'll buy Toyota stock. You buy Google stock. We'll compare notes in a decade and see who managed to make self-driving cars commercially viable.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#75

Post by jacquesm »

Retric wrote: post #68
jacquesm wrote: post #49

Earlier quoted context omitted.

The problems in automated driving are not in controlled and normal circumstances but in all the edge cases and exceptions.

People also do really poorly in edge cases. Don't forget self driving cars are going to quickly have billions of road miles worth of data. There are plenty of videos of autonomous cars is highly chaotic road conditions, but that's all old hat. Weather is an issue, but weather is also forecastable. Even if you only get rid of truckers in areas stay above freezing that's still a massive change.

> People also do really poorly in edge cases.

That's true but if they are simply different edge cases it might already be problematic.

And yes, the data will be there. But even if deep learning + lots of data is 'indistinguishable from magic' that does nothing to dispel the feeling of loss of control and every accident where regular drivers would think 'that would never happen to me' (see the Tesla one with the truck that got rammed) is going to make this a much harder battle.

So the question is: will the initial deployment of self driving vehicles be sufficiently impressive that any such errors will be forgiven?

I'm on the fence on this one, I have no idea where it will go but I'm kind of happy that this is happening now. I got to enjoy the 'drive yourself' period of driving in some of the best cars that were ever made and at the same time I'll probably be able to enjoy increased mobility due to some form of assisted driving much longer than what I would accept for myself as an autonomous driver.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#76

Post by seanmcdirmid »

dkarapetyan wrote: post #74
seanmcdirmid wrote: post #72

Earlier quoted context omitted.

http://www.toyota-global.com/innovation/partner_robot/histor... Industrial robots, and humanoid robots for entertainment purposes. Whereas Google has many of the founders of ML, not to mention its experts. Is Toyota, who already seriously undervalue software, willing to pay top dollar for those people when their average SDE makes You also seriously misunderstanding the amount of engineering discipline needed to build…

Misunderstanding? Given that my day job is to literally build tools and infrastructure for "modern software systems" I don't think I'm misunderstanding things at all. But whatever I can say on "modern software" has already been said much better by Alan Kay so I'm going to defer to him for the rest of this conversation. I'll buy Toyota stock. You buy Google stock. We'll compare notes in a decade and see who managed to…

I guess you'll find Toyota stock to be incredibly undervalued with a P/E of just 9.5 vs. GOOG's 30.26. The market isn't treating Toyota like a tech company. The market has already spoken, they've placed their bet on Google, which means that boat has already sailed. If you believe TM will make a tremendous comeback, then you could make bank, but that is a risky bet vs. a safe one (given today's information).

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#77

Post by seanmcdirmid »

Ma8ee wrote: post #35
xfs wrote: post #25

Earlier quoted context omitted.

> none of us in the automobile or IT industries are close to achieving true Level 5 autonomy - Gill Pratt, Toyota Research Institute http://spectrum.ieee.org/cars-that-think/transportation/self... > It will be 25 years before self-driving cars take off in America - Bill Gurley, Uber investor http://www.cnbc.com/2017/04/06/bill-gurley-uber-investor-sel...

Gurley says 25 years until majority of trips with self driving cars in the US, and that's because of legal hurdles, not technological ones. I don't have time to watch the video with Pratt.

Why are we limiting ourselves just to the states? Markets like China with severe traffic and parking problems and authoritarian governments to push through changes are more likely to adopt self driving cars than developed markets with plenty of roads and parking.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#78

Post by anabis »

jacquesm wrote: post #62
mturmon wrote: post #61

Earlier quoted context omitted.

No objection. But portions of commercial routes can be arranged to get around many of these edge cases. I was trying to address the question raised by the comment above: is this a problem of principles or of engineering, by saying that for a big (proportion TBD) segment of routes, it's engineering. Put onboard a bunch of sensors and computer power.

Yes, but that is a hack of sorts, and if the volumes are low a very expensive one so likely this will not happen until there is a sizable fleet of vehicles that can take advantage of it. You'd expect those things to happen in lock-step. The apples-to-apples comparison of automated driving to normal driving is that we have an existing road system and we want to use that for automated driving and normal driving and aut…

>'first world only' affair, and probably only a very small subset of that first world.

As in, heavily trafficked inner city routes like buses. I think it will make a lot of impact fast.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#79

Post by bryanrasmussen »

seanmcdirmid wrote: post #67
dkarapetyan wrote: post #66

Earlier quoted context omitted.

So if you were a betting man what would you go with. A bunch of software folks that don't really understand robotics. Or a bunch of robotics folks that learn some ML. I think the ML is the easy part. The real engineering in making these things truly production ready is the hard part. Only few places in the world have that kind of production expertise. Toyota being one such place.

You keep assuming a self driving car strongly resembles a traditional industrial robotics at all. The actual actuating part of a self-driving car is fairly easy (just turn the wheel, apply the breaks), it is the control system and sensing that is key to success, the former being software, and the latter (LIDAR) that Google is heavily invested in while Toyota is very late to that game. Right now, if I was a betting ma…

I'm betting it also has to accelerate and decelerate at appropriate times.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#80

Post by 77pt77 »

seanmcdirmid wrote: post #77
Ma8ee wrote: post #35

Earlier quoted context omitted.

Gurley says 25 years until majority of trips with self driving cars in the US, and that's because of legal hurdles, not technological ones. I don't have time to watch the video with Pratt.

Why are we limiting ourselves just to the states? Markets like China with severe traffic and parking problems and authoritarian governments to push through changes are more likely to adopt self driving cars than developed markets with plenty of roads and parking.

Good luck having a machine driving in the absolutely chaotic Chinese traffic.

I've seen full size buses driving at night on low visibility with lights off.

Bikes just doing illegal 90 degree sharp turns against oncoming traffic.

Pedestrians crossing 5 lane roads frogger style.

You name it...

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