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| Photo: Saiman Chow |
In
less than a decade, computers have become extremely good at diagnosing
diseases, translating languages, and transcribing speech. They can
outplay humans at complicated strategy games, create photorealistic
images, and suggest useful replies to your emails.
Yet despite these impressive achievements, artificial intelligence has glaring weaknesses.
Machine-learning systems can be duped or confounded by situations they haven’t seen before...
Even well-trained scientists are apt to misinterpret correlations as signs of causation—or to err in the opposite direction, hesitating to call out causation even when it’s justified. In the 1950s, for example, a few prominent statisticians muddied the waters around whether tobacco caused cancer. They argued that without an experiment randomly assigning people to be smokers or nonsmokers, no one could rule out the possibility that some unknown—stress, perhaps, or some gene—caused people both to smoke and to get lung cancer.
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Source: MIT Technology Review
