Machine “Learning”—An Entertainment [Industry] Edition

Yes, “Machine ‘Learning’,” too, has been one of my “research” interests for some time by now. … Machine learning, esp. ANN (Artificial Neural Networks), esp. Deep Learning. …

Yesterday, I wrote a comment about it at iMechanica. Though it was made in a certain technical context, today I thought that the comment could, perhaps, make sense to many of my general readers, too, if I supply a bit of context to it. So, let me report it here (after a bit of editing). But before coming to my comment, let me first give you the context in which it was made:


Context for my iMechanica comment:

It all began with a fellow iMechanician, one Mingchuan Wang, writing a post of the title “Is machine learning a research priority now in mechanics?” at iMechanica [^]. Biswajit Banerjee responded by pointing out that

“Machine learning includes a large set of techniques that can be summarized as curve fitting in high dimensional spaces. [snip] The usefulness of the new techniques [in machine learning] should not be underestimated.” [Emphasis mine.]

Then Biswajit had pointed out an arXiv paper [^] in which machine learning was reported as having produced some good DFT-like simulations for quantum mechanical simulations, too.

A word about DFT for those who (still) don’t know about it:

DFT, i.e. Density Functional Theory, is “formally exact description of a many-body quantum system through the density alone. In practice, approximations are necessary” [^]. DFT thus is a computational technique; it is used for simulating the electronic structure in quantum mechanical systems involving several hundreds of electrons (i.e. hundreds of atoms). Here is the obligatory link to the Wiki [^], though a better introduction perhaps appears here [(.PDF) ^]. Here is a StackExchange on its limitations [^].

Trivia: Kohn and Sham received a Physics Nobel for inventing DFT. It was a very, very rare instance of a Physics Nobel being awarded for an invention—not a discovery. But the Nobel committee, once again, turned out to have put old Nobel’s money in the right place. Even if the work itself was only an invention, it did directly led to a lot of discoveries in condensed matter physics! That was because DFT was fast—it was fast enough that it could bring the physics of the larger quantum systems within the scope of (any) study at all!

And now, it seems, Machine Learning has advanced enough to be able to produce results that are similar to DFT, but without using any QM theory at all! The computer does have to “learn” its “art” (i.e. “skill”), but it does so from the results of previous DFT-based simulations, not from the theory at the base of DFT. But once the computer does that—“learning”—and the paper shows that it is possible for computer to do that—it is able to compute very similar-looking simulations much, much faster than even the rather fast technique of DFT itself.

OK. Context over. Now here in the next section is my yesterday’s comment at iMechanica. (Also note that the previous exchange on this thread at iMechanica had occurred almost a year ago.) Since it has been edited quite a bit, I will not format it using a quotation block.


[An edited version of my comment begins]

A very late comment, but still, just because something struck me only this late… May as well share it….

I think that, as Biswajit points out, it’s a question of matching a technique to an application area where it is likely to be of “good enough” a fit.

I mean to say, consider fluid dynamics, and contrast it to QM.

In (C)FD, the nonlinearity present in the advective term is a major headache. As far as I can gather, this nonlinearity has all but been “proved” as the basic cause behind the phenomenon of turbulence. If so, using machine learning in CFD would be, by the simple-minded “analysis”, a basically hopeless endeavour. The very idea of using a potential presupposes differential linearity. Therefore, machine learning may be thought as viable in computational Quantum Mechanics (viz. DFT), but not in the more mundane, classical mechanical, CFD.

But then, consider the role of the BCs and the ICs in any simulation. It is true that if you don’t handle nonlinearities right, then as the simulation time progresses, errors are soon enough going to multiply (sort of), and lead to a blowup—or at least a dramatic departure from a realistic simulation.

But then, also notice that there still is some small but nonzero interval of time which has to pass before a really bad amplification of the errors actually begins to occur. Now what if a new “BC-IC” gets imposed right within that time-interval—the one which does show “good enough” an accuracy? In this case, you can expect the simulation to remain “sufficiently” realistic-looking for a long, very long time!

Something like that seems to have been the line of thought implicit in the results reported by this paper: [(.PDF) ^].

Machine learning seems to work even in CFD, because in an interactive session, a new “modified BC-IC” is every now and then is manually being introduced by none other than the end-user himself! And, the location of the modification is precisely the region from where the flow in the rest of the domain would get most dominantly affected during the subsequent, small, time evolution.

It’s somewhat like an electron rushing through a cloud chamber. By the uncertainty principle, the electron “path” sure begins to get hazy immediately after it is “measured” (i.e. absorbed and re-emitted) by a vapor molecule at a definite point in space. The uncertainty in the position grows quite rapidly. However, what actually happens in a cloud chamber is that, before this cone of haziness becomes too big, comes along another vapor molecule, and “zaps” i.e. “measures” the electron back on to a classical position. … After a rapid succession of such going-hazy-getting-zapped process, the end result turns out to be a very, very classical-looking (line-like) path—as if the electron always were only a particle, never a wave.

Conclusion? Be realistic about how smart the “dumb” “curve-fitting” involved in machine learning can at all get. Yet, at the same time, also remain open to all the application areas where it can be made it work—even including those areas where, “intuitively”, you wouldn’t expect it to have any chance to work!

[An edited version of my comment is over. Original here at iMechanica [^]]


 

“Boy, we seem to have covered a lot of STEM territory here… Mechanics, DFT, QM, CFD, nonlinearity. … But where is either the entertainment or the industry you had promised us in the title?”

You might be saying that….

Well, the CFD paper I cited above was about the entertainment industry. It was, in particular, about the computer games industry. Go check out SoHyeon Jeong’s Web site for more cool videos and graphics [^], all using machine learning.


And, here is another instance connected with entertainment, even though now I am going to make it (mostly) explanation-free.

Check out the following piece of art—a watercolor landscape of a monsoon-time but placid sea-side, in fact. Let me just say that a certain famous artist produced it; in any case, the style is plain unmistakable. … Can you name the artist simply by looking at it? See the picture below:

A sea beach in the monsoons. Watercolor.

If you are unable to name the artist, then check out this story here [^], and a previous story here [^].


A Song I Like:

And finally, to those who have always loved Beatles’ songs…

Here is one song which, I am sure, most of you had never heard before. In any case, it came to be distributed only recently. When and where was it recorded? For both the song and its recording details, check out this site: [^]. Here is another story about it: [^]. And, if you liked what you read (and heard), here is some more stuff of the same kind [^].


Endgame:

I am of the Opinion that 99% of the “modern” “artists” and “music composers” ought to be replaced by computers/robots/machines. Whaddya think?

[Credits: “Endgame” used to be the way Mukul Sharma would end his weekly Mindsport column in the yesteryears’ Sunday Times of India. (The column perhaps also used to appear in The Illustrated Weekly of India before ToI began running it; at least I have a vague recollection of something of that sort, though can’t be quite sure. … I would be a school-boy back then, when the Weekly perhaps ran it.)]

 

Haptic, tactile, virtual, surgery, etc.

Three updates made on 24th March 2016 appear near the end of this post.


Once in a while I check out the map of the visitors’ locations (see the right bar).

Since hardly any one ever leaves any comment at my blog, I can only guess who these visitors possibly could be. Over a period of time, guessing the particular visitors in this way has become an idle sort of a past-time for me. (No, I don’t obsess over it, and I don’t in fact spend much time on it—at the most half-a-minute or so, once in a while. But, yes, check, I certainly do!)

Among the recent visitors, there was one hit on 6th March 2016 coming from Troy, NY, USA (at 11:48:02 AM IST, to be precise). … Must be someone from RPI, I thought. (My blog is such that mostly only academics could possibly be interested in it; never the people with lucrative industrial jobs such as those in the SF Bay Area. Most of the hits from the Bay Area are from Mountain View, and that’s the place where the bots of Google’s search engines live.)

But I couldn’t remember engaging in any discussion with any one from RPI on any blog.

Who could this visitor possibly be? I could not figure it out immediately, so I let the matter go.


Yesterday, I noticed for the first time an ad for “several” post-doc positions at RPI, posted on iMechanica by Prof. Suvranu De [^]. It had been posted right on the same day: 6th March 2016. However, since recently I was checking out only my thread on the compactness of support [^], I had missed out on the main front page. Thus, I noticed the ad only today.

Curious, I dropped an informal email to Prof. De immediately, almost more or less by cognitive habits.


I am not too keen on going to the USA, and in fact, I am not even inclined to leave India. Reasons are manifold.

You, as every one else on the planet, of course comes to know all that ever happens to or in the USA. Americans make sure that you do—whether you like it or not. (Remember 9/11? They have of course forgotten it by now, but don’t you remember the early naughties when, imagining you to be just as dumb and thick-skinned as they are,  the kind of decibels they had pierced into your metaphorical ears (and in fact also in your soul)? Justifiable, you say? How about other big “controversies” which actually were nothing but scandals? Can’t you pick up one or two?)

Naturally, who would want to go to that uncivilized a place?

And even if you want to argue otherwise, let me suggest you to see if you can or cannot easily gather (or recollect) what all that has happened to me when I was in the USA?

So, the idea of trying to impress Dr. De for this post-doc position was, any which way, completely out of the question. Even if he is HoD at RPI.

And then, frankly, at my age, I don’t even feel like impressing any one for a mere post-doc; not these days anyway (I mean, some 6 years after the PhD defense, and after having to experience so many years of joblessness (including those reported via this blog)). … As far as I am concerned, either they know what and who I am, and act accordingly (including acting swiftly enough), or they don’t. (In the last case, mostly, they end up blaming me, as usual, in some or the other way.)

OK, so, coming back to what I wrote Dr. De. It was more in the nature of a loud thinking about the question of whether I should at all apply to them in the first place or not. … Given my experience of the other American post-docs advertised at iMechanica, e.g. those by Prof. Sukumar (UC Davis), and certain others in the USA, and also my experience of the Americans of the Indian origin (and even among them, those who are JPBTIs and those who are younger to me by age), I can’t keep any realistic expectation that I would ever receive any reply to that email of mine from Prof. De. The odds are far too against; check out the “follow-up” tag. (I could, of course, be psychically attacked, the way I was, right this week, a few days ago.)

Anyway, once thus grown curious about the subject matter, I then did a bit of a Web search, and found the following videos:

The very first listing after a Google search (using the search string: “Suvranu De”; then clicking on the tab: “videos”) was the one on “virtual lap band – surgical simulation”: [^].

Watching that video somehow made me sort of uneasy immediately. Uneasy, in a minor but a definitely irritating way. In a distinctly palpable way, almost as if it was a physical discomfort. No, not because the video carries the scene of tissue-cutting and all. … I have never been one of those who feel nervous or squeamish at the sight of blood, cuts, etc. (Most men, in fact, don’t!) So, my uneasiness was not on that count. …

Soon enough (i.e., much before the time I was in the middle of that video), I figured out the reason why.

I then checked out a few more videos, e.g., those here [^] and here [^]. … Exactly the same sense of discomfort or uneasiness, arising out of the same basic reason.

What kind of an uneasiness could there possibly be? Can you guess?

I don’t want to tell you, right away. I want you to guess. (Assume that an evil smile had transitorily appeared on my face.)

To close this post: If you so want, go ahead, check out those videos, see if it makes you uncomfortable watching some part of an implementation of this new idea. Then, the sin of the sins (“paapam, paapam, mahaapaapam” in Sanskrit): drop me a line (via a comment or an email) stating what that reason possibly could be. (Hint: It has nothing to do with the feely-feely-actually-silly/wily sort of psychological reasons. )

After a while, I will come back, and via an update to this post let you know the reason.


Update 1:

Yahoo! wants you to make a note of the “12 common mistakes to avoid in job interview”: [^]. They published this article today.


Update 2 (on 24th March 2016):

Surprise! Prof.  De soon enough (on 18th March IST) dropped me an email which was brief, professional, but direct to the point. A consequence, and therefore not much of a surprise: I am more or less inclined to at least apply for the position. I have not done so as of today; see the Update 3 below.


Update 3 (on 24th March 2016):

Right the same day (on 18th March 2016 about 10:00 PM IST), my laptop developed serious hardware issues including (but not limited to) yet another HDD crash! The previous crash was less than a year ago, in last June  [^].

Once again, there was  loss of (some) data: the initial and less-than-25%-completed drafts of 4+ research papers, some parts (from sometime in February onwards) of my notes on the current course on CFD, SPH, etc., as well as some preliminary Python code on SPH). The Update 2 in fact got delayed because of this development. I just got the machine back from the Dell Service last evening, and last night got it going on a rapid test installation of Windows 7. I plan to do a more serious re-installation over the next few days.


Update 4 (on 24th March 2016):

The thing in the three videos (about haptics, virtual surgery) that made me uncomfortable or uneasy was the fact that in each case, the surgeon was standing in a way that would have been just a shade uncomfortable to me. The surgeon’s hands were too “free” i.e. unsupported (say near elbow), his torso was stooping down in a wrong way (you couldn’t maintain that posture with accuracy in hands manipulation for long, I thought), and at the same time, he had to keep his eyes fixed on a monitor that was a bit too high-up for the eyes-to-hands coordination to work right. In short, there was this seeming absence of a consideration of ergonomics or the human factors engineering here. Of course, it’s only a prototype, and it’s only a casual onlooker’s take of the “geometry,” but that’s what made me distinctly uncomfortable.

(People often have rationalistic ideas about the proper (i.e. the least stress inducing and most efficient) posture.  In a later post, I will point out a few of these.)

 


 

A Song I Like:
(filled-in on 24th March 2016)

(Marathi) “thembaanche painjaN waaje…” [“rutu premaachaa aalaa”]
Singers: Avadhoot Gupte, Vaishali Samant
Music: Shashank Powar
Lyrics: Ravi Wadkar (?)

[An incidental note: The crash occurred—the computer suddenly froze—while I was listening to—actually, watching the YouTube video of—this song. … Somehow, even today, I still continue liking the song! … But yes, as is usual for this section, only the audio track is being referred to. (I had run into this song while searching for some other song, which I will use in a later post.)]


[Some editorial touches, other than the planned update, are possible, as always.]

[E&OE]