Seeing the Forest For the Trees
swframe writes "A new object recognition system developed at MIT and UCLA looks for rudimentary visual features shared by multiple examples of the same object. Then it looks for combinations of those features shared by multiple examples, and combinations of those combinations, and so on, until it has assembled a model of the object that resembles a line drawing. Popular Science has a summary of the research. I've been working on something similar and I think this accomplishment looks very promising."
I've heard of various other approaches -- to two different things, and I'm not sure which one the researchers are mainly going for. Is the goal here to produce a useful vision system for AI, or to get a better understanding of how the brain works? It seems like while these are compatible goals, it's helpful to distinguish them and decide which you care more about.
Revive the Constitution.
You know, two people or groups can arrive at the same conclusion, because it was obvious in the first place. And why is it so appropriate? What if the work had been done elsewhere, would that be inappropriate or offensive?
I should have been more specific in my first post.
David Marr's vision book (published in 1982 after his early death in 1980) is considered a seminal work in understanding human visual processing.
Marr was trying to describe how humans see. The new work at MIT is trying to allow computers to see. David Marr would be glad to see the developments, whether at MIT or elsewhere.
-Todd
Omne ignotum pro magnifico.
?There's nothing particularly unique about genetic algorithms with respect to learning? All systems operate within the constraints set by their programming.
Which should have been included into TFA from the start:
http://people.csail.mit.edu/leozhu/paper/RCM10cvpr.pdf
The main achievement claimed is that no image labeling or any additional data like viewport position was needed, the learning process was completely automated.