Building A.I. that can build A.I.

Building A.I. that can build A.I.

CITRIS researchers Pieter Abbeel and Sergey Levine discuss how computers can learn to invent new algorithms on their own through Deep Learning. Large tech companies such as Google, Microsoft, and Facebook hope to use this technology to build advanced systems with artificial intelligence.

New York Times: They are a dream of researchers but perhaps a nightmare for highly skilled computer programmers: artificially intelligent machines that can build other artificially intelligent machines.

With recent speeches in both Silicon Valley and China, Jeff Dean, one of Google’s leading engineers, spotlighted a Google project called AutoML. ML is short for machine learning, referring to computer algorithms that can learn to perform particular tasks on their own by analyzing data. AutoML, in turn, is a machine-learning algorithm that learns to build other machine-learning algorithms.

With it, Google may soon find a way to create A.I. technology that can partly take the humans out of building the A.I. systems that many believe are the future of the technology industry.

The project is part of a much larger effort to bring the latest and greatest A.I. techniques to a wider collection of companies and software developers.

The tech industry is promising everything from smartphone apps that can recognize faces to cars that can drive on their own. But by some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence.

Read the full story:  “Building A.I. that can build A.I.“,  by Cade Metz, The New York Times, Nov. 5, 2017