Photographer: Jesse Orrico
“NEURAL NETWORKS ARE all the rage in Silicon Valley, infusing so many internet services with so many forms of artificial intelligence. But as good as they may be at recognizing cats in your online photos, AI researchers know that neural networks are still quite flawed, so much so that some wonder whether these pattern recognition systems are a viable path to more advanced—and more reliable—forms of AI,” wrote Cade Metz for wired.com on February 3, 2017.
Metz continued, “Able to learn tasks by analyzing vast amounts of data, neural networks power everything from face recognition at Facebook to translation at Microsoft to internet search at Google. They’re beginning to help chatbots learn the art of conversation. And they’re part of the movement toward driverless cars and other autonomous machines. But because they can’t make sense of the world without help from such large amounts of carefully labelled data, they aren’t suited to everything. And AI researchers have limited insight into why neural networks make particular decisions. They are, in many ways, black boxes. This opacity could cause serious problems: What if a self-driving car runs someone down and the world wants to know why?”
Read the full article here.
Cade Metz is a WIRED senior staff writer covering Google, Facebook, artificial intelligence, bitcoin, data centers, computer chips, programming languages, and other ways the world is changing.
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