How to Make A.I. Human-Friendly

Here is a brief excerpt from an article by Fei-Fei Li for The New York Times. To read the complete article, check out others, and obtain subscription information, please click here.

Illustration Credit: Elisa Macellari

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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.

[Here is the first goal.]

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.

Sometimes this difference is trivial. For instance, in my lab, an image-captioning algorithm once fairly summarized a photo as “a man riding a horse” but failed to note the fact that both were bronze sculptures. Other times, the difference is more profound, as when the same algorithm described an image of zebras grazing on a savanna beneath a rainbow. While the summary was technically correct, it was entirely devoid of aesthetic awareness, failing to detect any of the vibrancy or depth a human would naturally appreciate.

That may seem like a subjective or inconsequential critique, but it points to a major aspect of human perception beyond the grasp of our algorithms. How can we expect machines to anticipate our needs — much less contribute to our well-being — without insight into these “fuzzier” dimensions of our experience?

Making A.I. more sensitive to the full scope of human thought is no simple task. The solutions are likely to require insights derived from fields beyond computer science, which means programmers will have to learn to collaborate more often with experts in other domains.

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No technology is more reflective of its creators than A.I. It has been said that there are no “machine” values at all, in fact; machine values arehuman values. A human-centered approach to A.I. means these machines don’t have to be our competitors, but partners in securing our well-being. However autonomous our technology becomes, its impact on the world — for better or worse — will always be our responsibility.

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Here is a direct link to the complete article.

Fei-Fei Li is a professor of computer science at Stanford, where she directs the Stanford Artificial Intelligence Lab, and the chief scientist for A.I. research at Google Cloud.

 

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