Understanding AI’s Limitations

After years of hype, many people feel Artificial Intelligence has failed to deliver.

Doubts have been creeping in about whether today’s ai technology is really as world-changing as it seems. It is running up against limits of one kind or another, and has failed to deliver on some of its proponents’ more grandiose promises.

There is no question that ai—or, to be precise, machine learning, one of its sub-fields—has made much progress. Computers have become dramatically better at many things they previously struggled with. The excitement began to build in academia in the early 2010s, when new machine-learning techniques led to rapid improvements in tasks such as recognising pictures and manipulating language. From there it spread to business, starting with the internet giants. With vast computing resources and oceans of data, they were well placed to adopt the technology. Modern ai techniques now power search engines and voice assistants, suggest email replies, power the facial-recognition systems that unlock smartphones and police national borders, and underpin the algorithms that try to identify unwelcome posts on social media.

This is not the first wave of ai-related excitement. The field began in the mid-1950s when researchers hoped that building human-level intelligence would take a few years—a couple of decades at most. That early optimism had fizzled by the 1970s. A second wave began in the 1980s. Once again the field’s grandest promises went unmet. As reality replaced the hype, the booms gave way to painful busts known as “ai winters”. Research funding dried up, and the field’s reputation suffered.

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