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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

#61

Post by mturmon »

jacquesm wrote: post #49
mturmon wrote: post #44

Earlier quoted context omitted.

One of the lessons of these first steps toward autonomous driving is: if you have a big array of sensors, and plenty of on board computing, then many driving problems, especially in semi controlled circumstances, like open highways, are within our grasp now. The sensors and computing are expensive now - especially lidar. But that's an engineering problem, as you suggest.

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

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.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#62

Post by jacquesm »

mturmon wrote: post #61
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.

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 automated driving should meet or exceed normal driving if it is to be successful.

The ultimate adapted commercial route for transport is called a railway, any kind of re-arrangement of the road system in order to adapt it for better use for automated driving is going to be a 'first world only' affair, and probably only a very small subset of that first world.

So for the foreseeable future automated driving systems will have to cope with all of the eventualities, even if for some stretch of their workload they may find things a little easier because of special adaptations (which of course will have to be equally accommodating to human drivers, or at a minimum not hinder them).

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#63

Post by dkarapetyan »

Animats wrote: post #59
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...

Pratt has to say that because Toyota is so far behind. He was hired to build up a self-driving group within Toyota.

How is Toyota behind? Don't they have an entire robotics division? Google, Uber, and all the other guys had to cobble together something that looks like a robotics division but Toyota has all of that already figured out.

I think if anyone pulls this off it is Toyota.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#64

Post by seanmcdirmid »

dkarapetyan wrote: post #63
Animats wrote: post #59

Earlier quoted context omitted.

Pratt has to say that because Toyota is so far behind. He was hired to build up a self-driving group within Toyota.

How is Toyota behind? Don't they have an entire robotics division? Google, Uber, and all the other guys had to cobble together something that looks like a robotics division but Toyota has all of that already figured out. I think if anyone pulls this off it is Toyota.

Robotics has little to do with self-driving cars at this point, which is heavily ML dependent while the former hasn't been so far (yet, that's changing).

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#65

Post by hakcermani »

PeterisP wrote: post #38
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…

Noone is entirely sure exactly when we'll reach level-5 autonomy, however, that doesn't matter for the specter of unemployment - the currently demonstrated level of availability seems clearly enough to put half of truck drivers out of work by all kinds of automation e.g. trucks self-driving on the regulated toll roads during time that drivers have their mandated rest; one driver supervising multiple trucks or driving…

> one driver supervising multiple trucks or driving a column of two-three trucks Could be introduced much earlier than fully autonomous mode.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#66

Post by dkarapetyan »

seanmcdirmid wrote: post #64
dkarapetyan wrote: post #63

Earlier quoted context omitted.

How is Toyota behind? Don't they have an entire robotics division? Google, Uber, and all the other guys had to cobble together something that looks like a robotics division but Toyota has all of that already figured out. I think if anyone pulls this off it is Toyota.

Robotics has little to do with self-driving cars at this point, which is heavily ML dependent while the former hasn't been so far (yet, that's changing).

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.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#67

Post by seanmcdirmid »

dkarapetyan wrote: post #66
seanmcdirmid wrote: post #64

Earlier quoted context omitted.

Robotics has little to do with self-driving cars at this point, which is heavily ML dependent while the former hasn't been so far (yet, that's changing).

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 man, Google will beat Toyota. Of course, Toyota could always flip the table and take some risks, but that isn't what Toyota is known for. If it happens great! But I don't think any smart money sees Toyota having a clear advantage ATM.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#68

Post by Retric »

jacquesm wrote: post #49
mturmon wrote: post #44

Earlier quoted context omitted.

One of the lessons of these first steps toward autonomous driving is: if you have a big array of sensors, and plenty of on board computing, then many driving problems, especially in semi controlled circumstances, like open highways, are within our grasp now. The sensors and computing are expensive now - especially lidar. But that's an engineering problem, as you suggest.

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.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#69

Post by dkarapetyan »

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 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 famously not getting along and their self-driving division seems to have been horribly mismanaged for a long time and we are now seeing the fallout in the form of lawsuits.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#70

Post by xfs »

Animats wrote: post #59
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...

Pratt has to say that because Toyota is so far behind. He was hired to build up a self-driving group within Toyota.

TRI's association with UMich and MIT makes them more biased towards issues about the difficulty of running self-driving cars in adverse weather conditions because that's the problem that all the researchers at UMich and MIT have had all the time and it's a problem that Google/Stanford have never had in always sunny California.

I have personally heard of the complaints of John Leonard (MIT) and Edwin Olson (UMich) about the severe lack of research of self-driving car robustness in weather conditions. The issue is real.

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