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MIT researchers create world's first computer model that is able to adequately mimic artificial vision

Scientists in Tomaso Poggio's laboratory at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology have developed a computational model of how the human brain processes visual information that specifically mimics how it recognizes street scenes.  The research could be used to help repair damaged brain functions while helping researchers further understand some of the locked mysteries of the brain.  

The original intent behind the research for Poggio has been to successfully develop a model that would be able accurately portray a visual system that would not only be good for neuroscientists and psychologist but also for purposes related to computer science.  "That was Alan Turing's original motivation in the 1940s.  But in the last 50 years, computer science and AI have developed independently of neuroscience.  Our work is biologically inspired computer science."

In the enclosed image, the Poggio model for object recognition is able to receive input as regular unlabeled images of digital images from a Street Scene Database and will then generate an annotation that detects important parts of the street scene.  The system is also able to detect cyclists, buildings, trees, different roads, and the sky.

One of the biggest drawbacks of better development of artificial intelligence is that the human brain is mysterious and extremely complicated to mimic.  While computers are obviously much faster, humans are smarter -- drawing a bridge between the two has been difficult. 


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RE: <no subject>
By crimson117 on 2/27/2007 5:03:18 PM , Rating: 2
quote:
a good programmer can make a tetris program that plays a lot better than himself.... or checkers... or chess... you teach him the goal and the rules.

It's not like the computer asks you for the goal and the movement rules, then goes and plays by itself. Otherwise a chess manual text document could play, right?

You still have to program it to analyze millions of possible moves and the resulting outcomes, then use statistics to determine the path to take that will most likely lead to a favorable outcome. That's still just statistical calculation, though. Any human could do it, just a lot slower than a computer.

Even if you teach it to "learn" an opponent's playing style, such as watching what pieces are most often involved in that opponent's wins, and then making it a priority to capture those pieces early on, you've still programmed in that set of rules - the computer just has to fill in the blanks with its experiences. It will never write its own rules and be able to in turn teach them back to you.

The student may be millions of times faster, but the student will never actually surpass the teacher :)


RE: <no subject>
By oTAL on 3/6/2007 6:10:51 AM , Rating: 2
You are wrong... A simple example is checkers... In this simple case it's not about speed... it's about the amount of experience that can be accumulated (the use of such experience is, in a way, a form of 'inteligence'). By teaching a computer how to play and making it play against itself will generate, with a well built program and given enough time, an unbeatable opponent. While the programmer may not know anything other than the main rules, the computer learned, like human do, from its own experience. The difference is that for the computer it is easier to evoke that experience and not repeat the same mistake twice. The human advantage is in understanding and extrapolation - in highly complex environments human can extrapolate 'rules of thumb'. If a human player does a mistake it learns not only to avoid that mistake but also to avoid that 'family of mistakes' encompassing many similar situations with similar outcomes. Computers have a harder time understanding their mistakes and extrapolating them to unexplored situations.


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