The waves of hype around getting machines to think, and the subsequent disillusions borne of the marginal results, led the field to go through a number of what have been calledAI Winters, in which research funding dries up, and progress slows.
We probably will not see another AI Winter, if only because too many large companies, notably Google and Facebook, are basing their business models on using intelligence computing to better intuit what their users are looking for, Cronin said. Other companies offer AI-assisted technologies, such as such as Apple with Siri, and IBM with Watson.
In many ways, today's AI systems are a direct lineage of the first AI systems built in the 1960s, such as the Eliza -- the psychiatric advice-dispensing program still used for some Twitterbots today -- and Perceptron, one of the first precursors to deep-learning neural networks.
Such early AI systems were "deeply flaw and limited. They were just very basic in their capabilities," Cronin said. Nonetheless, "you can draw a direct lines from those early systems to the work we're doing today in AI," he observed. "Watson is what we wished Eliza would be."
After years of very little progress, though, we are increasingly becoming awash in ever-more astounding forms of AI-like assistance for specific tasks. The pace of advance is happening at a rate that have surprised "even people who have been in the field for a long time," Cronin said.
Self-driving vehicles, on the precipice of becoming commercially available, were considered to be almost an unachievable technology as little as 10 years ago.
Perhaps this is due to the change in funding for AI research. Governments with research money to spare have always invested in researchers with grand ambitions. And for many years, small commercial research organizations such as SRI International and Cycorp moved forward the state of the art.
These days, AI research has benefactors across most of the major IT and Internet companies, such as Google, Facebook and Microsoft Research. Many smaller startups, flush with venture capital, are also pushing the envelope.
"The work is increasingly applied to commercial [projects] rather than academic" ones, Cronin said. As a result, AI technologies are now operating at larger scales than they ever did in the academic days. "Deep learning on its own, done in academia, doesn't have the [same] impact as when it is brought into Google, scaled and built into a new product."
As a result, AI methods, such as machine learning, are now being integrated into commercial services and products, at a speedier pace than ever before. Cronin noted that Watson and Siri are more notable as "big integration projects" than for pioneering new forms of intelligence.
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