Self-Learning AI: New Neuro-Inspired Computer Trains Itself

 

A COMPUTING ROAD LESS TRAVELED

A team of researchers from Belgium think that they are close to extending the anticipated end of Moore’s Law, and they didn’t do it with a supercomputer. Using an artificial intelligence (AI) algorithm called reservoir computing, combined with another algorithm called backpropagation, the team developed a neuro-inspired analog computer that can train itself and improve at whatever task it’s performing.

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The three biggest problems facing AI today

Speaking to attendees at a deep learning conference in London last month, there was one particularly noteworthy recurring theme: humility, or at least, the need for it.

While companies like Google are confidently pronouncing that we live in an “AI-first age,” with machine learning breaking new ground in areas like speech and image recognition, those at the front lines of AI research are keen to point out that there’s still a lot of work to be done. Just because we have digital assistants that sound like the talking computers in movies doesn’t mean we’re much closer to creating true artificial intelligence.

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Quantum Computing is coming and bringing massive disruption with it

Next year, we may see the launch of the first true quantum computers.

The implications will be staggering.

This post aims to answer three questions:

  1. What are quantum computers?
  2. What are their implications?
  3. Who’s working on them?

There’s a lot to unpack here, so hang tight, and let’s jump in!

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Report: AI will eliminate 6 percent of jobs in five years

Within five years robots and so-called intelligent agents will eliminate many positions in customer service, trucking and taxi services, amounting to 6 percent of jobs, according to a Forrester report.

“By 2021, a disruptive tidal wave will begin,” said Brian Hopkins, VP at Forrester, in the report. “Solutions powered by AI/cognitive technology will displace jobs, with the biggest impact felt in transportation, logistics, customer service, and consumer services.”

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The next trick for machine learning is generating videos from photos

Show a human any photograph and they’ll able to predict what happens next with pretty decent accuracy. The woman riding her bike will keep on moving. The dog will catch the frisbee. The man is going to have a pratfall. And so on. It’s such a basic skill that we don’t consider the vast amount of information that is used to make these predictions — concerning gravity, inertia, the nature of pratfalls, etc. — and teaching computers to do the same is proving to be a key challenge in machine vision.

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