The growing influx of big data has helped the field, as well, introducing inferencing and other statistical methods that few would have predicted would play a such powerful role in technology, Cronin said. In the olden days of academic AI research, the amount of data that could be used to reason against was relatively sparse, compared to the mountains of stuff we have today.
Google has made bank from its massive set of data on its users, which it collected first and figured out how to make money from later. The company didn't get initially get hung up on "putting a lot of structure in the model," Cronin said. This has been termed by Google engineers as the "unreasonable effectiveness of data."
In the long haul, however, we will have to put more thought into deeper learning techniques than we have now, Cronin said. Today's methods just aren't going to get us to full artificial intelligence. "We need richer, more predictive models," Conin said, ones that can "routinely make predictions of what will happen."
One member of the audience, Juan Pablo Velez, a data analyst at New York data science consultancy firm Polynumeral, agreed with Cronin's assessment of AI.
"A lot of new innovation has come around in deep learning that has been rolled out in scale, like Google image search. But the research is very much tied to the agendas of big companies and it doesn't necessarily mean we are any closer to generalized machine intelligence," Velez said.
In many ways, we are at the same point in AI research where we've always been: moving forward rapidly in some aspects, while seemingly standing still in relation to the big goal, generalized artificial intelligence. As Facebook head of AI research Yann LeCun has said, AI research is like driving fast in the fog, where you can't see the next roadblock you will hit.
Until the day when we build a machine to look ahead into the fog for us, the future of AI will be uncertain for some time to come.
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