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Thursday, Nov 14, 2013 - 22:59 SGT
Posted By: Gilbert

Thursday Tech Bites

Exciting gizmo of the week's the panono ball, which takes a full Google Street View-like panorama of the area without any fuss. How does it work? The inventors basically packed thirty-six smartphone-type cameras into a (hopefully sturdy) ball, and added a smartphone-type accelerometer that triggers all the cameras once it detects that the panono ball has reached the peak of its trajectory. It can be deduced that to get a wide view, a good throwing arm would be nice, and it'll probably help to not put too much spin on it.

Mischievous fellows may note at this point that both defining components of the panono ball are present in a run-of-the-mill smartphone, which leads one to suspect that the feat might be achieved without the other 35 cameras. Of course, in this case, a load of spin would be extremely helpful, together with an industrial-strength casing.

Why would one do it, however, if he could instead make three billion dollars for an app that allows teenagers to send each other photos of themselves making duckfaces? Wait, so you can send pics to groups of friends via WhatsApp and a zillion other messaging systems? But SnapChat has another killer feature! It will delete those incriminating photos after a prespecified time period (in the process reclaiming server space), so one's youthful indiscretions will be forgotten... unless any recipient takes a screenshot for posterity.

In the technical sense, my own humble opinion is that it's not quite worth the price Facebook is willing to pay. However, SnapChat has captured millions of young 'uns who are apparently moving on to the Next Cool Thing, so I suppose Facebook feels that retaining this userbase is better than going the way of MySpace and Friendster. Not that it's even their own money that they're splurging, anyhow.

In contrast, back in sunny Singapore, the hottest tech name of the moment is... a hapless alleged drug addict who apparently can't even string some proxies together properly. Now, that lapse could be forgiven, every script kiddie has to begin somewhere; but bloody having "Computer Security at Anonymous" as your LinkedIn profile?! Come to think of it, perhaps this was why the Prime Minister could boldly make his pledge in the first place - he probably had the guy's photo on his table.


Ladies and gentlemen, I kid you not


It turns out that another group made the hit on the PM's website (or so it is said), however, which brings us to the indefatigability of Anonymous (not that I'm endorsing them, mind) - anybody can be the Messiah if he so desires. Myself, I'd settle for government web services being available outside office hours (note that while I hardly support all of the linked blog post's controversial assessments, this one did rankle)


Like all catchy ideas, it got copied soon enough


I'll conclude the roundup with a note on ersatz (no relation to the initiative at Brown), a startup that provides deep neural network services on a GPU cloud (which so happens to be one of my current hobbies, sans cloud), that has just entered private beta (see Youtube promo). Their offering relatively new developments like pooling and dropout sounded very encouraging... until I came to the pricing, which is forty-one cents per GPU-minute (though with up to 20% off in bulk)

Now, bear in mind that most practical demonstrations I have read about require hours, and more usually days, of training, 41¢/min really adds up - that's US$590 a day, which is about the minimum time I've been taking to get useful results on my datasets, with my admittedly not completely optimized code. Meanwhile, the shiniest and most powerful commercially available NVIDIA GPU, the GTX 780 Ti released just last week, costs but US$699, i.e. just over a day of computation.

Of course, if by GPU-minute they mean multiple GPUs, it would be better value for money; if so, we're getting into really new territory, with the network structure designed so as to reduce the communication required between individual processing units (note in Appendix E how few connections cross partitions in the celebrated Stanford-Google cat finder). Generally, this also seems to involve (mini-)batch backpropagation instead of the online form, which has its own issues, but I suppose the use can be justified with ultralarge systems.

Still, given how often (re-)training has to be done due to silly errors (believe me, I have my fair share of those), or simply to obtain a better model, one suspects that for some, it would make more sense to get their own GPUs (which moreover have resale value). Of course, it's a different matter if customers are paying for the knowledge on how to set things up, and given that they seem to be targeting the oil and finance industries, among others, this may well apply.



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