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That will tell us where we need to put research effort, and where that will lead to progress towards our Super Intelligence. The seven capabilities that I have selected below start out as concrete, but get fuzzier and fuzzier and more speculative as we proceed.
It is relatively easy to see the things that are close to where we are today and can be recognized as things we need to work on. When those problems get more and more solved we will be living in different intellectual world than we do today, dependent on the outcomes of that early work.
So we can only speak with conviction about the short term problems where we might make progress. And by short term, I mean the things we have already been working on for forty plus years, sometimes sixty years already. And there are lots of other things in AI that are equally hard to do today.
I just chose seven to give some range to my assertion that there is lots to do. Real perception Deep Learning brought fantastic advances to image labeling. Many people seem to Intelligence into success essay that computer vision is now a solved problem. But that is nowhere near the truth.
Below is a picture of Senator Tom Carper, ranking member of the U. He is showing what is now a well known particular failure of a particular Deep Learning trained vision system for an autonomous car.
The stop sign in the left has a few carefully placed marks on it, made from white and black tape. The system no longer identifies it as a stop sign, but instead thinks that is a forty five mile per hour speed limit sign.
But really how could a vision system that is good enough to drive a car around some of the time ever get this so wrong? Stop signs are red! Speed limit signs are not red. Surely it can see the difference between signs that are red and signs that are not red?
We think redness of a stop sign is an obvious salient feature because our vision systems have evolved to be able to detect color constancy. The data sets that are used to train Deep Learning systems do not have detailed color labels for little patches of the image.
And the computations for color constancy are quite complex, so they are not something that the Deep Learning systems simply stumble upon. We can see it is and say it is a checkerboard because it is made up of squares that alternate between black and white, or at least relatively darker and lighter.
But wait, they are not squares in the image at all. Our brain is extracting three dimensional structure from this two dimensional image, and guessing that it is really a flat plane of squares that is at a non-orthogonal angle to our line of sight—that explains the consistent pattern of squishing we see.
But wait, there is more.
One is surely black and one is surely white. Our brains will not let us see the truth, however, so I have done it for your brain. Here I grabbed a little piece of image from the top black square on the left and the bottom white square in the middle.
In isolation neither is clearly black nor white. Our vision system sees a shadow being cast by the green cylinder and so lightens up our perception of the one we see as a white square.
And it is surrounded by even darker pixels in the shadowed black squares, so that adds to the effect. The third patch above is from the black square between the two labeled as the same and is from the part of that square which falls in the shadow.
They will then pop into being the same shade of grey. This is just one of perhaps a hundred little or big tricks that our perceptual system has built for us over evolutionary time scales.Turnitin provides instructors with the tools to prevent plagiarism, engage students in the writing process, and provide personalized feedback.
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