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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

#81

Post by gumby »

coliveira wrote: post #12
andreasvc wrote: post #4

Cute title but the post didn't really address either reasonableness or effectiveness, but mostly claimed that the potential has not yet been realized. It's a pet peeve of mine to see these hackneyed joke titles referencing famous papers, "considered harmful" is another case in point. Let's just stick to descriptive titles.

It is a modern disease of CS and related areas. They think it is better to be "cute" than descriptive, as a way to attract attention.

It's an element of all disciplines and, more charitably, one aimed at achieving two functions. One is to say "this is a paper from someone embedded in the discipline and who speaks the same vocabulary as you". The other is to say "and this is intended to expound on a subject parallel to X (or following on from X, as the case may be)"

You can argue that this class of jargon is exclusionary or not. Agre, at UCLA, for example took the position that jargon was inherently a tool of dividing up groups into "in and out" (ironically he himself, with an MIT PhD, was very much in the in group).

I tend to consider jargon just a tool like any other, typically valuable because you can save a lot of time and gain clarity by saying "O(n^2)" or "trie" and assume your reader understands it.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#82

Post by sien »

maxerickson wrote: post #27
tyingq wrote: post #14

Earlier quoted context omitted.

That is impressive, but isn't Waymo driving on some limited number of pre-set routes? It would be nice if there were some credible, unbiased sources that were predicting when full level 5 might be viable. Edit: Maybe a somewhat credible opposing view. Though he may be crabby about his department being drained of talent, and so not unbiased: https://motherboard.vice.com/en_us/article/robotics-lab-uber...

I wonder if Waymo is dependent on their hyper detailed maps of if they are using them to speed up development of their systems. With a hyper detailed map, you can automatically score your vision/world modeling system (because the static elements are known). Without a detailed map, the scoring is much harder.

Would that allow the creation of a profitable MVP?

It's not 'general level 5' but it might be a transport system in cities with appropriate conditions that have been mapped.

Then it gets gradually expanded out to other cities.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#83

Post by seanmcdirmid »

77pt77 wrote: post #80
seanmcdirmid wrote: post #77

Earlier quoted context omitted.

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...

Ya, it seems like the last place! But the need is strong, the will is there. Baidu and many other Chinese companies are making big self drive car investments.

Self driving cars are just a toy in the USA, but in china they could optimize infrastructure that is extremely limited and unable to grow to meet demand. They are also not above changing the rules as needed (like when they put up fences in our office so we couldn't easily cross the street to our other building, all in the name of keeping pedistrians off what was otherwise a very untrafficed road.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#84

Post by tnecniv »

dkarapetyan wrote: post #36

I think this is because the kinds of problems that arise in system design are logical and symbolic in nature and the current crop of "AI" has no symbolic reasoning capabilities. All the current hype is about pattern matching. Very good pattern matching but just pattern matching nonetheless. Whereas when constructing a compiler or a JIT it's more like what mathematicians do by setting down some axioms and exploring th…

It's worth noting there have been some attempts to unify ANNs with formal logic specifications, but I haven't seen anything incredible.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#85

Post by abalone »

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…

> trucks self-driving on the regulated toll roads during time that drivers have their mandated rest

How would this be possible at anything less than level 4-5 autonomy? If they can take a nap, the truck's got to be fully autonomous.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#86

Post by est »

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…

> 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.

How about a mixed adoption, on 99% of pure-highway the truck is driver-less, but there are dedicated service stations for a human driver to take over into the urban area.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#87

Post by abalone »

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.

Ok then, how about Waymo's ex-lead Chris Urmson. "If you read the papers, you see maybe it's three years, maybe it's thirty years. And I am here to tell you that honestly, it's a bit of both."[1]

He goes on to explain that easier domains may come sooner (so maybe level 4), but his comments certainly pour some cold water on level 5.

[1] http://spectrum.ieee.org/cars-that-think/transportation/self...

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#88

Post by Ma8ee »

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.

I think that was Gurley's point. In many Asian countries the need is greater and the ability to sue lower, so self driving cars will be on public roads much earlier than in the US.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#89

Post by peteretep »

abalone wrote: post #85
PeterisP wrote: post #38

Earlier quoted context omitted.

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…

> trucks self-driving on the regulated toll roads during time that drivers have their mandated rest How would this be possible at anything less than level 4-5 autonomy? If they can take a nap, the truck's got to be fully autonomous.

Level 4 includes autonomy in the "operational design domain (ODD)" of a vehicle. I'd have thought that on a tollway that's being maintained specifically to support some self-driving trucks that the difficulty of Level 4 is a few orders of magnitude easier than cars in a residential district.

Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

#90

Post by azernik »

grzm wrote: post #56
gpvos wrote: post #54

Earlier quoted context omitted.

Regarding remote drivers: probably for the same reason we don't have remote harbour pilots.

Do you have a specific reason in mind? I have no experience in this area, so my own speculations are just that. One practical issue may be lag. Connectivity would also of course be very important. There are historical reasons as well, but given we're discussing new technology (automated trucking), there are plenty of things that will change as a result and may not have much sway going forward.

I'm not familiar with the actual design decisions involved, but I'd also imagine field of view for an on-the-spot operator would be better than for someone operating through cameras, unless you invest in a very expensive surround setup.
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