An interesting but not widely known fact is that the Dartmouth conference was also attended by Ray Solomonoff.
Contrary to his colleagues he focused on questions of machine learning before learning became perceived as a worthwhile research direction by the AI community.
This has led Solomonoff to investigation of a question of universal sequence prediction. A couple of years later Solomonoff wrote a paper about such a system for prediction that used algorithmic probability (he is cited later as the original developer of algorithmic information theory which was later independently discovered by Kolmogorov who later acknowledged that the Solomonoff was the first). This method, Solomonoff's induction, is proven to be the most optimal (though incomputable) machine learning method possible.
He has never abandoned this project and for the rest of his life he focused on making more sophisticated system designs that are computable while still being proven to be optimal.
His latest system is called "Alpha", and it is designed as a machine for solving a sequence of function inversion and time limited optimization problems (a majority of science/engineering problems can be formulated this way) in a way that exploits experience gathered while solving these problems. This system, again, is proven to be optimal in a certain sense.
He also tried to implement this system with various practical optimizations, but it didn't converge fast enough on his training sequences and on the hardware of that time.
Still, with modern hardware it is a possibility that it could work. And the whole design is described in the papers, so people can (and actually do, though privately and perhaps without much success) implement this system.
This has led Solomonoff to investigation of a question of universal sequence prediction. A couple of years later Solomonoff wrote a paper about such a system for prediction that used algorithmic probability (he is cited later as the original developer of algorithmic information theory which was later independently discovered by Kolmogorov who later acknowledged that the Solomonoff was the first). This method, Solomonoff's induction, is proven to be the most optimal (though incomputable) machine learning method possible.
He has never abandoned this project and for the rest of his life he focused on making more sophisticated system designs that are computable while still being proven to be optimal.
His latest system is called "Alpha", and it is designed as a machine for solving a sequence of function inversion and time limited optimization problems (a majority of science/engineering problems can be formulated this way) in a way that exploits experience gathered while solving these problems. This system, again, is proven to be optimal in a certain sense. He also tried to implement this system with various practical optimizations, but it didn't converge fast enough on his training sequences and on the hardware of that time.
Still, with modern hardware it is a possibility that it could work. And the whole design is described in the papers, so people can (and actually do, though privately and perhaps without much success) implement this system.
Here are the relevant papers: http://world.std.com/~rjs/publications/IncLrn89.pdf http://world.std.com/~rjs/nips02.pdf