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Sunday, March 19, 2017

AI Is Getting Brainier: Will Machines Leave Us in the Dust? | Sci-Tech Today - Innovation

Photo: Ian Sample
"DeepMind's latest AI has cleared one of the important hurdles on the way to human-level AGI -- artificial general intelligence, with the goal of mirroring how the human brain works." says Ian Sample, science editor of the Guardian.
Photo: Sci-Tech Today

The road to human-level artificial intelligence is long and wildly uncertain. Most AI programs today are one-trick ponies. They can recognize faces, the sound of your voice, translate foreign languages, trade stocks and play chess. They may well have got the trick down pat, but one-trick ponies they remain. Google's DeepMind program, AlphaGo, can beat the best human players at Go, but it hasn't a clue how to play tiddlywinks, shove ha'penny, or tell one end of a horse from the other.

Humans, on the other hand, are not specialists. Our forte is versatility. What other animal comes close as the jack of all trades? Put humans in a situation where a problem must be solved and, if they can leave their smartphones alone for a moment, they will draw on experience to work out a solution.

The skill, already evident in preschool children, is the ultimate goal of artificial intelligence. If it can be distilled and encoded in software, then thinking machines will finally deserve the name.

DeepMind's latest AI has cleared one of the important hurdles on the way to human-level AGI -- artificial general intelligence. Most AIs can perform only one trick because to learn a second, they must forget the first. The problem, known as "catastrophic forgetting," occurs because the neural network at the heart of the AI overwrites old lessons with new ones.

DeepMind solved the problem by mirroring how the human brain works. When we learn to ride a bike, we consolidate the skill. We can go off and learn the violin, the capitals of the world and the finer rules of gaga ball, and still cycle home for tea. This program's AI mimics the process by making the important lessons of the past hard to overwrite in the future. Instead of forgetting old tricks, it draws on them to learn new ones.

Because it retains past skills, the new AI can learn one task after another. When it was set to work on the Atari classics -- Space Invaders, Breakout, Defender and the rest -- it learned to play seven out of 10 as well as a human can. But it did not score as well as an AI devoted to each game would have done. Like us, the new AI is more the jack of all trades, the master of none. 

Source: Sci-Tech Today