Welcome to SKYNET. Google’s Artificial Intelligence Built Its Own AI That Outperforms Any Made by Humans
DECEMBER 6, 2017 AT 2:20 PM
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by Jake Anderson, The Anti Media:
With the exponential growth of machine learning, automation, and artificial intelligence, a certain anxiety over future job losses and human obsolescence has crept into popular culture. What’s to stop artificial intelligence from replacing humans across the board? Well, one might respond, they’ll still need humans to create them. Right?
According to researchers at Google Brain, their newest artificial intelligence (AI) creation, AutoML, is not only capable of creating its own AIs, it is better at it than humans. The project, an early example of recursively self-improving AI, involved the use of reinforcement learning to automate the development of machine learning templates.
AutoML (“ML” is short for machine learning) acted as a controller neural network that spawned a child AI called NASNet. AutoML played the role of supervisor and teacher for its child AI, overseeing its ability to perform a specific task over and over again. In this case, NASNet was charged with real-time object detection, which it completed with record efficiency.
According to CEO Sundar Pichai, AutoML solves one of the most intractable problems for deep learning software engineers, which is selecting the best architecture for a neural network.
Google’s researchers say the development of computer vision algorithms will not only help to expand the field — which, by some estimates, has only 10,000 people worldwide with the ability to write such complex mathematical algorithms — but that it could also lead to huge improvements in self-driving cars and even enhanced assistance for visually impaired humans.
While recursively self-improving AI will lead to the exponential growth of AI, experts say that democratizing the field of AI by allowing non-experts to develop AI applications will lead to human growth as well.
“We hope that the larger machine learning community will be able to build on these models to address multitudes of computer vision problems we have not yet imagined.”
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