Deep Learning will do Everything?

But there is still much to learn, to apply,  to understand.  The history is examined, the future is considered. 

AI godfather Geoff Hinton: “Deep learning is going to be able to do everything”

Nearly 30 years ago, Hinton’s belief in neural networks was contrarian. Now it’s hard to find anyone who disagrees, he says.  by Karen Haoarchive  in Technology Review

Geoffrey Hinton (talks AI:

On the AI field’s gaps: "There’s going to have to be quite a few conceptual breakthroughs...we also need a massive increase in scale."

On neural networks’ weaknesses: "Neural nets are surprisingly good at dealing with a rather small amount of data, with a huge numbers of parameters, but people are even better."

On how our brains work: "What’s inside the brain is these big vectors of neural activity."

The modern AI revolution began during an obscure research contest. It was 2012, the third year of the annual ImageNet competition, which challenged teams to build computer vision systems that would recognize 1,000 objects, from animals to landscapes to people.

In the first two years, the best teams had failed to reach even 75% accuracy. But in the third, a band of three researchers—a professor and his students—suddenly blew past this ceiling. They won the competition by a staggering 10.8 percentage points. That professor was Geoffrey Hinton, and the technique they used was called deep learning.

Hinton had actually been working with deep learning since the 1980s, but its effectiveness had been limited by a lack of data and computational power. His steadfast belief in the technique ultimately paid massive dividends. The fourth year of the ImageNet competition, nearly every team was using deep learning and achieving miraculous accuracy gains. Soon enough deep learning was being applied to tasks beyond image recognition, and within a broad range of industries as well.

Last year, for his foundational contributions to the field, Hinton was awarded the Turing Award, alongside other AI pioneers Yann LeCun and Yoshua Bengio. On October 20, I spoke with him at MIT Technology Review’s annual EmTech MIT conference about the state of the field and where he thinks it should be headed  .... "