Better Neuromorphic Computing Leading to Intelligence?

A look at the history of neuromorphic computing.  Or the use of some  'forms' of the biological brain to provide 'intelligence'.    Artificial Neural Network methods already do this, but relatively weakly.  Mentioned is Terry Sejnowski and his current work at the Salk Institute.      We connected with Sejnowski when he was closely looking at diagnostic systems in the 80s.   Quite general overview here, and you have to sign in for the full article.  See also an outline of Sejnowski's work at Salk:    Worth following, as I do.

Neuromorphic computing finds new life in machine learning     by 7wData

Efforts have been underway for forty years to build computers that might emulate some of the structure of the brain in the way they solve problems. To date, they have shown few practical successes. But hope for so-called neuromorphic computing springs eternal, and lately, the endeavor has gained some surprising champions.

The research lab of Terry Sejnowski at The Salk Institute in La Jolla this year proposed a new way to train "spiking" neurons using standard forms of machine learning, called "recurrent neural networks," or "RNNs."

And Hava Siegelmann, who has been doing pioneering work on alternative computer designs for decades, proposed along with colleagues a system of spiking neurons that would perform what's called "unsupervised" learning.

Neuromorphic computing is an umbrella term given to a variety of efforts to build computation that resembles some aspect of the way the brain is formed. The term goes back to work by legendary computing pioneer Carver Mead in the early 1980s, who was interested in how the increasingly dense collections of transistors on a chip could best communicate. Mead's insight was that the wires between transistors would have to achieve some of the efficiency of the brain's neural wiring. ... "

Terrence Sejnowski Explains Deep Learning

We connected with  Terrence Sejnowski during the last go around of artificial intelligence, he was known for some classic AI applications of the time, so thought it would be useful to see his view of the current state of the technology.  He makes that case that back then you had to code an AI problem from the ground up.   Now, for some kinds of problems, and if you have enough of  the right data, you can have it learn its intelligence.  I see he has a book coming out on the topic as well that probably worth a look.  See also his page at the Salk Institute.

A pioneering scientist explains ‘deep learning’
Artificial intelligence meets human intelligence
By Angela Chen    @chengela in TheVerge

Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies.

Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution (out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence.

The Verge spoke to Sejnkowski about how “deep learning” suddenly became everywhere, what it can and cannot do, and the problem of hype.  .... '