Chris Meserole at Brookings: “In the summer of 1955, while planning a now famous workshop at Dartmouth College, John McCarthy coined the term “artificial intelligence” to describe a new field of computer science. Rather than writing programs that tell a computer how to carry out a specific task, McCarthy pledged that he and his colleagues would instead pursue algorithms that could teach themselves how to do so. The goal was to create computers that could observe the world and then make decisions based on those observations—to demonstrate, that is, an innate intelligence.
The question was how to achieve that goal. Early efforts focused primarily on what’s known as symbolic AI, which tried to teach computers how to reason abstractly. But today the dominant approach by far is machine learning, which relies on statistics instead. Although the approach dates back to the 1950s—one of the attendees at Dartmouth, Arthur Samuels, was the first to describe his work as “machine learning”—it wasn’t until the past few decades that computers had enough storage and processing power for the approach to work well. The rise of cloud computing and customized chips has powered breakthrough after breakthrough, with research centers like OpenAI or DeepMind announcing stunning new advances seemingly every week.
The extraordinary success of machine learning has made it the default method of choice for AI researchers and experts. Indeed, machine learning is now so popular that it has effectively become synonymous with artificial intelligence itself. As a result, it’s not possible to tease out the implications of AI without understanding how machine learning works—as well as how it doesn’t….(More)”.