Interview with Turing Award recipients on Neural Nets

We read some of the earliest work of Hinton and were inspired by the direction.   Also a lesson about hype and the lessons from the fringe of typical research. But this is still not complete enough to create strong AI.    Lots more to do.  So this interview is interesting, just not long or detailed enough.

Reaching New Heights with Artificial Neural Networks
By Leah Hoffmann 

Communications of the ACM, June 2019, Vol. 62 No. 6, Pages 96-ff
10.1145/3324011

2018 Turing Award recipients Yoshua Bengio, Geoffrey Hinton, and Yann LeCun

Once treated by the field with skepticism (if not outright derision), the artificial neural networks that 2018 ACM A.M. Turing Award recipients Geoffrey Hinton, Yann LeCun, and Yoshua Bengio spent their careers developing are today an integral component of everything from search to content filtering. So what of the now-red-hot field of deep learning and artificial intelligence (AI)? Here, the three researchers share what they find exciting, and which challenges remain.

There's so much more noise now about artificial intelligence than there was when you began your careers—some of it well-informed, some not. What do you wish people would stop asking you?

GEOFFREY HINTON: "Is this just a bubble?" In the old days, people in AI made grand claims, and they sometimes turned out to be just a bubble. But neural nets go way beyond promises. The technology actually works. Furthermore, it scales. It automatically gets better when you give it more data and a faster computer, without anybody having to write more lines of code.  ... "