Why the quickest path to human-level AI may be letting it evolve on its own

Why the quickest path to human-level AI may be letting it evolve on its own

It’s become increasingly clear as we reach its limits that deep learning – a specific subset of AI technology – isn’t going to magically lead to human-level Artificial Intelligence.

deep learning may still go a bit further but I agree with the general sentiment here. https://t.co/nLiYj9iA1k

— Gary Marcus (@GaryMarcus) November 7, 2019

If we want robots that can think like us, we’ve got to stop giving them all the answers. Curiosity and exploration are the two key components of the human intellect that deep learning simply doesn’t provide. 

In a recent article in Quanta Magazine, writer Matthew Hutson describes the work of computer scientist Kenneth Stanley, who is currently working at Uber’s AI lab. Stanley’s pioneering work in the field of “neuroevolution” has paved the way for a new Artificial Intelligence paradigm that eschews traditional objective-based training models in favor of AI models that have no purpose but to explore and be creative.

Hutson writes:

Biological evolution is also the only system to produce human intelligence, which is the ultimate dream of many AI researchers. Because of biology’s track record, Stanley and others have come to believe that if we want algorithms that can navigate the physical and social world as easily as we can — or better! — we need to imitate nature’s tactics.

Instead of hard-coding the rules of reasoning, or having computers learn to score highly on specific performance metrics, they argue, we must let a population of solutions blossom. Make them prioritize novelty or interestingness instead of the ability to walk or talk. They may discover an indirect path, a set of steppingstones, and wind up walking and talking better than if they’d sought those skills directly.

Standard deep learning models use a black box – a set of weights and parameters that, ultimately, become too complex for developers to describe individually – to ‘brew’ up machine learning algorithms and tweak them until they spit out the right data. This isn’t intelligence, it’s prestidigitation. 

If AI could evolve its own solutions and combine those parameters with deep learning, it’d be closer to imitating human-level problem solving. At least, that’s what Stanley argues.

His research involves building evolutionary algorithms that can function in tandem with deep learning systems. In essence, rather than teaching an AI to solve a problem, he develops algorithms that sort of meander about seeing what they’re capable of. These systems aren’t rewarded for solving a problem lik

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