I think the impressive thing here is that the GPU is presumably doing GIANT matrix multiplications in real time. A prediction from a neural net is just a series of matrix multiplications, and matrix multiplications are about n^2.8 in complexity, so you can see how matrix multiplications with thousands of rows/columns (often what these sorts of deep image classifiers involve) are hugely computationally expensive.
So it's definitely important for real time machine learning systems to have access to this kind of linear algebra power, but the actual machine learning techniques demonstrated are not super impressive. The hardware is. Which makes sense since this is an Nvidia demo.
From what i can understand what's even more impressive is that it was running on a beefed up version of their latest mobile SOC and not on some 5000$ compute GPU card.
Which means that this application can be both very affordable and very practical since people won't put a 300W GPU in their car.
Definitely agreed. When well-maintained and easy to use machine learning libraries meet very powerful, highly embeddable GPUs (or other dedicated linear algebra sort of hardware), I think we'll see a big revolution in the whole "smart object" field.
Right now you have iPhones and whatnot doing touch ID with fingerprints, but imagine if your phone could recognize you just by quickly analyzing the gyro data as you raise your phone and comparing it against the other thousand times you've pulled your phone out of your pocket, combined with the slight pressure readings near the touchscreen's edge because it's learned where you're fingers fall on the case.
^ contrived example I just thought of, but you get the idea.
Intel 'realsense' drone demo was, and I'm not into the smart/IoT trend, somehow impressive. A flying electromechanical bug on stage at a mainstream show, to me that was a small but real inflection point.
Yes because some where in my sentence i was referring to a 980 or a 780ti some where? Maybe it was a Titan Z, no maybe it's still the GTX 690 Ti which is still the fastest single card they made, or maybe that statement was referring to their latest COMPUTE card the Tesla K80 which costs 5000$, and contains 2 new Kepler cores (GM210) and requires about 300 watts of juice to run....
I'm not an expert but, from what I do know, it seems like the take-away here is that it's running on tech which is within reach of most consumers. Sure, academics are accomplishing more impressive feats in the lab. However, it looks like nVidia has brought these algorithms onto hardware which is probably not much different from what they're already selling to gamers at a reasonable price. That could end up being a really big deal and could boost applications of computer vision in consumer tech significantly.
Current state of the art is a bit better than this. See the bottom section of [1] for some of the latest publications.
However, building a real world working system has challenges that are different to the academic challenge of trying to classify the most classes possible in static images.
A few commenters already tell that this isn't really groundbreaking work, but how about for real time? And commodity hardware (this one is a few watt mobile chip)?
