withfries2

Thanks for the links. I'm a huge fan of Stephen Pinker.

Most deep learning people I talk to acknowledge the algorithms & data structures are only lightly inspired by brain anatomy. Michael Jordan makes this point well in this IEEE article: http://spectrum.ieee.org/robotics/artificial-intelligence/ma...

Donna Dubinsky and Jeff Hawkins and the team at Numenta are doing the most explicit biomimicry work I'm aware of. What else is happening that you know of?

Mostly, I wanted to highlight the importance of scale (data + compute) for the accuracy of deep networks.

cs702, this deck started as a short talk so I didn't have time to acknowledge all the great work leading up to the Google YouTube experiment. Your list is good, and I'd add Rosenblatt for the first perceptron, Rumelhart & McClelland for applying techniques to perception, Werbos for backpropogation, Fukushima for convolutional networks, and so many more.

I found these helpful while researching the history: http://www.andreykurenkov.com/writing/a-brief-history-of-neu... http://www.scholarpedia.org/article/Deep_Learning

What else have you found particularly useful?