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Fei-Fei Li: How To Build Human-Centered AI
March 12, 2018
Today's anxieties over job losses are just the start. Universities, corporations, and government need to start working together to build human-centric AI, the Chief AI Scientist for Google Cloud has argued in an op-ed for The New York Times last week.
Fei-Fei Li is world-renowned as one of the biggest innovators in the field. As well as being the chief of AI research at Google Cloud, Li heads the Stanford AI Lab and continues to work as a computer science professor. Most significantly, she played a leading role in ImageNet, a crowdsourced dataset of millions of training photographs compiled to advance machine vision technology.
"I worry that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society," Li argues in the piece. "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."
This is what she calls 'human-centered AI', and it consists of three goals that she believes can responsibly guide the development of intelligent machines.
[caption id="attachment_10790" align="alignleft" width="314"] Fei-Fei Li[/caption]
"First, AI 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."
"Making AI 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."
Such a collaboration would, she argues, represent a return to 'the roots' of the AI field. "Younger AI enthusiasts may be surprised to learn that the principles of today's deep-learning algorithms stretch back more than 60 years."
"Reconnecting AI with fields like cognitive science, psychology, and even sociology will give us a far richer foundation on which to base the development of machine intelligence. And we can expect the resulting technology to collaborate and communicate more naturally, which will help us approach the second goal of human-centered AI: enhancing us, not replacing us."
Li highlights a trend "toward automating those elements of jobs that are repetitive, error-prone, and even dangerous. What's left are the creative, intellectual and emotional roles for which humans are still best suited."
"No amount of ingenuity, however, will fully eliminate the threat of job displacement. Addressing this concern is the third goal of human-centered AI: ensuring the the development of this technology is guided, at each step, by concern for its effect on humans."
Additional potential pitfalls, Li outlines, include bias against underrepresented communities in machine learning; the tension between AI's appetite for data and the privacy rights of individuals; as well as the geopolitical implications of a global intelligence race.
She calls on universities to foster interdisciplinary connections between computer science, social science, and the humanities, and governments to encourage greater computer literacy among young girls, racial minorities, and other 'underrepresented groups'. Corporations, meanwhile, should combine their aggressive investment in intelligent algorithms with ethical A.I. policies that 'temper ambition with responsibility'.
Fei-Fei Li continues to evangelize for a human-focused AI, and here, she's keen to highlight that 'human values are machine values': "No technology is more reflective of its creators than AI. It has been said that there are no 'machine' values at all, in fact; machine values are human values. A human-centered approach to AI 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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