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How to Make AI Human Friendly

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How to Make AI Human Friendly

How to Make AI Human Friendly

For a field that was not well known outside of academia a decade ago, artificial intelligence has grown dizzyingly fast. Tech companies from Silicon Valley to Beijing are betting everything on it, venture capitalists are pouring billions into research and development, and start-ups are being created on what seems like a daily basis. If our era is the next Industrial Revolution, as many claim, A.I. is surely one of its driving forces.

It is an especially exciting time for a researcher like me. When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face. But thanks to the growth of big data, advances in algorithms like neural networks and an abundance of powerful computer hardware, something momentous has occurred: A.I. has gone from an academic niche to the leading differentiator in a wide range of industries, including manufacturing, health care, transportation and retail.

I worry, however, that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns.

I call this approach “human-centered A.I.” It consists of three goals that can help responsibly guide the development of intelligent machines.

First, A.I. needs to reflect more of the depth that characterizes our own intelligence. Consider the richness of human visual perception. It’s complex and deeply contextual, and naturally balances our awareness of the obvious with a sensitivity to nuance. By comparison, machine perception remains strikingly narrow.

uch collaboration would represent a return to the roots of our field, not a departure from it. Younger A.I. enthusiasts may be surprised to learn that the principles of today’s deep-learning algorithms stretch back more than 60 years to the neuroscientific researchers David Hubel and Torsten Wiesel, who discovered how the hierarchy of neurons in a cat’s visual cortex responds to stimuli.

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Article Credit: NY Times

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