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| Photo: Tim Appenzeller |
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| A conceptual illustration evokes a node in a neural network, which “learns” as connections between simulated neurons change in response to inputs. KIYOSHI TAKAHASE SEGUNDO/ALAMY STOCK PHOTO |
The flood of data can overwhelm human insight and analysis, but the computing advances that helped deliver it have also conjured powerful new tools for making sense of it all.
In a revolution that extends across much of science, researchers are unleashing artificial intelligence (AI), often in the form of artificial neural networks, on the data torrents. Unlike earlier attempts at AI, such “deep learning” systems don’t need to be programmed with a human expert’s knowledge.
Instead, they learn on their own, often from large training data sets, until they can see patterns and spot anomalies in data sets that are far larger and messier than human beings can cope with.
AI isn’t just transforming science; it is speaking to you in your smartphone, taking to the road in driverless cars, and unsettling futurists who worry it will lead to mass unemployment. For scientists, prospects are mostly bright: AI promises to supercharge the process of discovery...
Artificial intelligence, in so many words Just what do people mean by artificial intelligence (AI)? The term has never had clear boundaries. When it was introduced at a seminal 1956 workshop at Dartmouth College, it was taken broadly to mean making a machine behave in ways that would be called intelligent if seen in a human. An important recent advance in AI has been machine learning, which shows up in technologies from spellcheck to self-driving cars and is often carried out by computer systems called neural networks. Any discussion of AI is likely to include other terms as well.
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Source: Science Magazine
