This AI Boom Will Also Bust • Hacker News Board

Re: This AI Boom Will Also Bust

#101

Post by ma2rten » Sat, Dec 03, 2016, 7:06 PM UTC

I think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods.

The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they prefer models that are understandable and not a black box. Sometimes they prefer statistics because it tells you if your insights are significant.

The second one is Machine Learning Engineering. ML Engineers are Software Engineers that use Machine Learning to build products. They might work on spam detection, recommendation engines or news feeds. They care about building products that scale and are reliable. They will run A/B tests to see how metrics are impacted. They might use Deep Learning, but they will weight the pros and cons against other methods.

Then there are AI Researchers. Their goal is to push the boundaries of what computers can do. They might work on letting computers recognize images, understand speech and translate languages. Their method of choice is often Deep Learning because it has unlocked a lot of new applications.

I feel like this post is essentially someone from the first group criticizing the last group, saying their methods are not applicable to him. That is expected.

Re: This AI Boom Will Also Bust

#102

Post by RandyRanderson » Sat, Dec 03, 2016, 7:07 PM UTC

I'm old enough to have seen a lot of these boom/hype/bust cycles. I'm convinced that this time is, in fact, different.

To temper this I believe most decent user-visible changes will take ~5 years (as most actually useful software does) but the changes will be huge:

* The author cites computer driven cars. I think this will take place mostly on long-haul highway trucking instead of in cities first. Even so, this could mean a massive swath of truckers without work in a short 5yr epoch.

* We've already seem the effects of heavy astro-turfing/disingenuous information/etc in the last US election. This certainly changed the "national psyche" and may have changed the election outcome. There is heavy ML research going into making the agglomeration of ads and content almost compulsively watchable. Our monkey brains can likely only handle a few simple dimensions and only boolean or maybe linear relations and they certainly get trapped in local maxima/minima. Even trivial ML techniques can bring this compulsion from say 50% effectiveness to 95%+ (by some reasonable measure). Imagine a web that is so completely tailored to the user such that search results, ads and content is completely tailored to you. Verbs, adjectives entire copy all written to get you to that next click. This is different.

* Bots that seem like real ppl will be rampant. Are those 100 followers/likes/retweets actual ppl? Even years ago reddit (to gain popularity) faked users. Certainly this has only accelerated and will continue to as commercial and state actors see value to moving public opinion with these virtual actors. (ironically maybe only bots will have read this far?)

* Financial Product innovation - Few ppl actually understand this market (even within the banks) however the deals are usually in the 100+ million range. The products take advantage of tax incentives, fx, swaps, interest rates, etc in an ever increasing complexity. These divisions are still some of the most profitable parts of banks. It's likely that on deals where profits are measured in tens of millions on a single deal (several are made per quarter, per major bank). It's likely that ML algos will be put to use here as well not only optimizing current products but in current prod elaborations. I beleive these products to be a major source of inflation. Whereas the official numbers are ~2% I believe the actual inflation (tm) felt by most is more in the 7%+ range.

* State Surveillance and Actions - I hear ppl saying that mass surveillance hasn't been effective in stopping "terrorism", as if it would be ok if it did. Well, it will be effective and it will get very, very good at it. Of course terrorism is not defined anywhere so ...

* Customer Support - this, like transportation, is a major employer of unqualified workers. I believe in 10 years there will be maybe 1% of the current workforce in CSR work. The technology is here the software just has to be written.

It's not just the number of jobs displaced it's the velocity. If we look to the effective Predator-prey modeling:

https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equatio...

We see that the generally the solution takes 2 modes:

* stability - wolf/rabbit populations wax and wane together * crash - the wolves kill enough rabbits to make the remaining pop crash

Now I don't believe there will be a 'crash' but likely there will be a new normal (equilibrium) and getting there will not be pleasant.

Disclaimer: Yes, I do work in ML.

Re: This AI Boom Will Also Bust

#103

Post by goatlover » Sat, Dec 03, 2016, 7:07 PM UTC

ffwd wrote: post #75

rampage101 wrote: post #23

The more I get into machine learning and deep learning it seems like there is an incredible amount of configuration to get some decent results. Cleaning and storing the data takes a long time. And then you need to figure out exactly what you want to predict. If you predict some feature with any sort of error in your process the entire results will be flawed. There are a few very nice applications of the AI techniques…

> In the real world, most things are in a maybe state rather than yes/no. Not to get too far afield, but I disagree with this on a certain philosophical level. All states are yes/no. All states of all things should result in a yes/no and be differentiable, with enough data. This doesn't speak to the practicality of that but as far as I can tell the theoretical potential is huge, almost infinite even.

So we should just use Prolog to accomplish all our programming tasks?

Re: This AI Boom Will Also Bust

#104

Post by BeingIncubated » Sat, Dec 03, 2016, 7:09 PM UTC

goatlover wrote: post #97

bunderbunder wrote: post #94

Earlier quoted context omitted.

I doubt you'll get that, because nobody thinks that progress in machine learning will stop. An AI winter doesn't mean that progress stops. It means that businesses and the general public become disillusioned by AI's or ML's failure to live up to the popular hype, and stop throwing so much money at it. The hype then dies down. Research continues, though, until enough progress is made that machine learning starts to pr…

Why, as presumably rational agents, is there so much hype with certain technologies, even though past history should have taught us otherwise? Is it all for the VC? Is it the media just needing to sell stories? Usually there are smart, knowledgeable people involved in the hyping. They should know better.

There is no such thing as complete rational people, every person goes through bouts of rationality and irrationality, some are just more often rational than others. With that said nobody is immune from irrational emotion sometimes, for example have you been angry from something that wasn't worth it?

Re: This AI Boom Will Also Bust

#105

Post by goatlover » Sat, Dec 03, 2016, 7:11 PM UTC

BeingIncubated wrote: post #104

goatlover wrote: post #97

Earlier quoted context omitted.

Why, as presumably rational agents, is there so much hype with certain technologies, even though past history should have taught us otherwise? Is it all for the VC? Is it the media just needing to sell stories? Usually there are smart, knowledgeable people involved in the hyping. They should know better.

There is no such thing as complete rational people, every person goes through bouts of rationality and irrationality, some are just more often rational than others. With that said nobody is immune from irrational emotion sometimes, for example have you been angry from something that wasn't worth it?

Hell yeah I have been. And then realized it was stupid later.

Re: This AI Boom Will Also Bust

#106

Post by tannhaeuser » Sat, Dec 03, 2016, 7:14 PM UTC

bunderbunder wrote: post #94

RushAndAPush wrote: post #82

I've read every comment in this thread and its filled mostly with peoples self congratulatory intellectual views. Nobody, not even Robin Hansen himself has given a good, detailed argument as to why the current progress in Machine learning will stop.

I doubt you'll get that, because nobody thinks that progress in machine learning will stop. An AI winter doesn't mean that progress stops. It means that businesses and the general public become disillusioned by AI's or ML's failure to live up to the popular hype, and stop throwing so much money at it. The hype then dies down. Research continues, though, until enough progress is made that machine learning starts to pr…

Sorry but I don't think "AI winter" as a term is appropriate here.

The "AI winter" as I know it was around 1988. I've still vintage issues of "AI Expert" and other publications of the time (which I kept for their great linocut-style artwork, and because they were expensive items here around).

Back then it wasn't so much about ML (aka "Deep Learning") but anything Prolog, expert systems, artificial neural nets and their generalizations, and Lisp.

Re: This AI Boom Will Also Bust

#107

Post by sherbondy » Sat, Dec 03, 2016, 7:17 PM UTC

blackguardx wrote: post #91

agumonkey wrote: post #57

Earlier quoted context omitted.

ToF depth being single POV and depth infered from successive frames ?

A light source built into the camera is modulated in time. The camera uses this to infer depth. When the modulation is synced up right, the camera can see the light wave "travel" as it illuminates close objects first and then farther ones over successive frames. The camera isn't actually fast enough to capture the light wave traveling, but the timing between the shutter and the light source is shifted by nanoseconds…

Hey, 3D vision system amateur here, but very interested to learn more!

Can anybody point me to some literature or reference materials about attempts to combine the inputs from multiple techniques simultaneously?

E.g. a device with stereo conventional cameras and infrared cameras & emitters which compares the resulting model from each input source/technique and actively re-adjusts final depth estimate?

Is "sensor fusion" the right jargon to use in this context?

Or, even crazier, a control system which actively jitters the camera's pose to gain more information for points in the depth map with lower confidence scores / conflicting estimates?

But maybe such a setup is overly complex and yields minimal gains in mixed indoor & outdoor scenarios?

Re: This AI Boom Will Also Bust

#108

Post by Animats » Sat, Dec 03, 2016, 7:22 PM UTC

blackguardx wrote: post #91

agumonkey wrote: post #57

Earlier quoted context omitted.

ToF depth being single POV and depth infered from successive frames ?

A light source built into the camera is modulated in time. The camera uses this to infer depth. When the modulation is synced up right, the camera can see the light wave "travel" as it illuminates close objects first and then farther ones over successive frames. The camera isn't actually fast enough to capture the light wave traveling, but the timing between the shutter and the light source is shifted by nanoseconds…

That's an older form of ranging. It's easier to do than pulse ranging, but you have to outshine ambient light at the color being used full time. The Swiss Ranger [1] is a good example of such a system. It's indoor only and short range. With a pulse system, you only have to outshine ambient light for a nanosecond, which is quite possible in sunlight.

I've been expecting good, cheap non-scanning laser distance imagers for a decade. In 2003, I went down to Advanced Scientific Concepts in Santa Barbara and saw the first prototype, as a collection of parts on an optical bench. Today ASC makes good units [2], but they cost about $100K. DoD and Space-X buy them. There's one on the Dragon spacecraft, for docking. That technology isn't inherently expensive, but requires custom semiconductors produced with non-standard processes such as InGaAs. Those cost too much in small volumes. There's been progress in coming up with designs that can be made in standard CMOS fabs.[3] When that hits production, laser rangefinders will cost like CMOS cameras.

[1] http://www.adept.net.au/cameras/Mesa/SR4000.shtml [2] http://www.advancedscientificconcepts.com/products/overview.... [3] https://books.google.com/books?id=Op6NCwAAQBAJ&lpg=PA64

Re: This AI Boom Will Also Bust

#109

Post by RushAndAPush » Sat, Dec 03, 2016, 7:25 PM UTC

bunderbunder wrote: post #94

RushAndAPush wrote: post #82

I've read every comment in this thread and its filled mostly with peoples self congratulatory intellectual views. Nobody, not even Robin Hansen himself has given a good, detailed argument as to why the current progress in Machine learning will stop.

I doubt you'll get that, because nobody thinks that progress in machine learning will stop. An AI winter doesn't mean that progress stops. It means that businesses and the general public become disillusioned by AI's or ML's failure to live up to the popular hype, and stop throwing so much money at it. The hype then dies down. Research continues, though, until enough progress is made that machine learning starts to pr…

> I doubt you'll get that, because nobody thinks that progress in machine learning will stop.

Robin Hansen is notoriously skeptical about the possibility that Deep Learning can make real gains. He for some reason thinks brain emulation is more likely to make large progress in AI.

>An AI winter doesn't mean that progress stops.

It doesn't completely stop, but progress would be at a snails pace.

> The hype then dies down. Research continues, though, until enough progress is made that machine learning starts to produce results that excite the public again, and the cycle goes into another hype phase.

I think we as a community may need to take a good long look at the hype cycle theory and be skeptical it has any merit.

Re: This AI Boom Will Also Bust

#110

Post by sidlls » Sat, Dec 03, 2016, 7:26 PM UTC

ma2rten wrote: post #101

I think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods. The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they…

It's also suffering from hype.

And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engineers and "scientists" as not doing "real" ML or AI is becoming a thing, similar to how some computer scientists deride engineering as not doing real computing.