Deep Learning

Artificial intelligence pioneer says throw it all away and start again

Geoffrey Hinton harbors doubts about AI’s current workhorse. (Johnny Guatto / University of Toronto)

In 1986, Geoffrey Hinton co-authored a paper that, three decades later, is central to the explosion of artificial intelligence.

But Hinton says his breakthrough method should be dispensed with, and a new path to AI found.

… he is now “deeply suspicious” of back-propagation, the workhorse method that underlies most of the advances we are seeing in the AI field today, including the capacity to sort through photos and talk to Siri.

“My view is throw it all away and start again”

Hinton said that, to push materially ahead, entirely new methods will probably have to be invented. “Max Planck said, ‘Science progresses one funeral at a time.’ The future depends on some graduate student who is deeply suspicious of everything I have said.”

Hinton suggested that, to get to where neural networks are able to become intelligent on their own, what is known as “unsupervised learning,” “I suspect that means getting rid of back-propagation.”

“I don’t think it’s how the brain works,” he said. “We clearly don’t need all the labeled data.”

Source: Axios

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I prefer to be killed by my own stupidity rather than the codified morals of a software engineer

…or the learned morals of an evolving algorithm. SAS CTO Oliver Schabenberger

With the advent of deep learning, machines are beginning to solve problems in a novel way: by writing the algorithms themselves.

The software developer who codifies a solution through programming logic is replaced by a data scientist who defines and trains a deep neural network.

The expert who studied and learned a domain is replaced by a reinforcement learning algorithm that discovers the rules of play from historical data.

We are learning incredible lessons in this process.

But does the rise of such highly sophisticated deep learning mean that machines will soon surpass their makers? They are surpassing us in reliability, accuracy and throughput. But they are not surpassing us in thinking or learning. Not with today’s technology.

The artificial intelligence systems of today learn from data – they learn only from data. These systems cannot grow beyond the limits of the data by creating, innovating or reasoning.

Even a reinforcement learning system that discovers rules of play from past data cannot develop completely new rules or new games. It can apply the rules in a novel and more efficient way, but it does not invent a new game. The machine that learned to play Go better than any human being does not know how to play Poker.

Where to from here?

True intelligence requires creativity, innovation, intuition, independent problem solving, self-awareness and sentience. The systems built based on deep learning do not – and cannot – have these characteristics. These are trained by top-down supervised methods.

We first tell the machine the ground truth, so that it can discover its regularities. They do not grow beyond that.

Source: InformationWeek



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