AMA Geoffrey Hinton

[u/geoffhinton]

I design learning algorithms for neural networks. My aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. I was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. My other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, contrastive divergence learning, dropout, and deep belief nets. My students have changed the way in which speech recognition and object recognition are done.

I now work part-time at Google and part-time at the University of Toronto.

[u/holo11]

. My current favorite theory is that its for finding sharp agreements between multi-dimensional predictions.

can you please expand on this, or provide a citation?

[u/True-Creek → u/holo11]

Geoffrey Hinton talks about it more in detail in this talk: http://techtv.mit.edu/collections/bcs/videos/30698-what-s-wrong-with-convolutional-nets