Dan Connors – TRANSCRIPT
I’m going to start off today with a very important question, and that is what would you do with infinite computing power? I’m not asking what society or the government is going to do, I’m asking you a personal question: what would you do with infinite computing?
As a professor at University of Colorado, this is something I’m very close to, and this is actually how I start off each year talking to my students in “Computer Architecture: the Foundations of Computer Design.” I pose that question to them, and just like you, maybe you’ve already thought, your answer and what that’s going to be.
Maybe you want to use computers to make money by somehow predicting the stock market. Maybe that was your first guess. Maybe your second one is you will decrypt all encrypted material so that you don’t have any secrets out there. So while this question isn’t meant to be a philosophical question for my engineering students, it quickly comes out with many parallels in real life. The first thing we point out is that this is no longer a question, or at least we should be aware of this as far as our field.
So if I look at the annual report coming out of the Top 500, this is the listing of the Top 500 fastest supercomputers in the world. N equals 1 is the fastest, N equals 500 is the lowest of the 500. What’s very interesting to look at as far as supercomputers behave in terms of calculations per second, that’s the measurement we use typically to define the fastest computer, the number of math calculations per second. What’s fascinating about supercomputers is equally fascinating about where we’re at now in portable electronics: your laptop, your notebook, your iPad. So if we look at 2013 and look back 20 years to 1993, at the cost of millions of dollars back then to do that level of computing, you now have equally at 500 dollars, all within a hand’s reach.
So this brings up right away that this question isn’t even about talking about the future, x at scale level of a billion, trillion operations per second. It’s right now that we have to answer this question. What happens in other fields – and computers aren’t often given enough credit – is we think our own society should be more reactive to biology and chemistry. We have already started to regulate cloning of human tissue, as very well important in our society, but there’s also dangers of biological agents that we would say no individual person should be in possession of these things.
Likewise in chemistry. There are chemical compounds that you do not want in society. Right now you cannot go to a drug store and get 100 boxes of Sudafed because there’s a chance that you might go and make meth drugs, and so we prevent that in society. Same thing with chemical compounds; we don’t want hazardous material by individuals. But what about computers? How much computing power should an individual have? What would you do with it? Very often you think about computers as just being dangerous when you get a virus, when your computer has been hacked, and your data is stolen. But while that might seem personal, you aren’t yourself affected as far as your body.
We’ll see if that’s about to change. So where is the area that is most absorbing all of this computing power that’s come about? And I’m going to point you to computer vision, things you’d even take for granted today that the cheapest of cell phone can probably draw a box around the faces of your family and friends when you take a picture. It might be a free cell phone that you didn’t even have to pay for. So this is what is part of this overarching view of computer vision, which is how to find the objects of interest or information of interest in images or in video. So, while this is just the start, we can also extend that to other areas, so for instance, in the realm of looking to the context of a photo.
So rather than just a single object, we might want to detect what the scene contains. Not just face detection and face recognition but to detect if there are buildings, if it’s a cloudy day, anything in that context of information. With this, you start to think computers are catching up to humans. They are just going to mimic what we can already do. If I showed you this without the labels, you would be able to tag each of these as far as a subject matter.
When you try to equate that, a brain, and then a circuit, and you try to say, “Oh, well; give us some analogy.” Is a synapse and a wire the same thing? Is a neuron and a transistor the same thing? How many transistors make up a neuron, because we want to know when computers are going to be capable of holding artificial intelligence, or doing everything that humans can do. I want to dispel this right away, that when we are doing computer vision it is different from human vision in two ways. First, the development process. We as humans develop as infants looking up at faces, and we’re used to looking at a very small number of objects. That patterns us in a certain way.
Likewise, the math that computers do for computer vision is completely unrelated to how we ourselves are doing visual interpretation and perception. So don’t try to think that there’s a hybrid together that the brain and the circuit is going to come into one, or they’re going to be equivalent. Specifically, if you’re looking at this picture, there are four concentric rings that are not overlapping. You might get a little dizzy trying to prove that to yourself, OK? So this is again, a way that humans have developed their own intelligence, or their own visual understanding, that computers have no problem looking at this picture and saying there are four rings patterned by smaller squares or rectangles slightly shifted.