The Association for the Advancement of Artificial Intelligence (AAAI), whose annual conference begins this week, had its first meeting in 1980. But its AI lineage goes back even farther: two of its first presidents were John McCarthy and Marvin Minsky, both participants in the 1956 Dartmouth Summer Research Project on Artificial Intelligence, which launched AI as an independent field of study.

Like all AI conferences, AAAI was transformed by the deep-learning revolution, which many people date to 2012, when Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton’s deep network AlexNet won the ImageNet object recognition challenge with a 40% lower error rate than the second-place finisher.

Given the 10-year anniversary of that paper, and given that, in its long history, AAAI has seen AI research trends come and go, Amazon Science thought it might be a good time to contemplate what comes after the deep-learning revolution. So we asked Nikko Ström, a vice president and distinguished scientist in the Alexa AI organization, for his thoughts.

To begin with, Ström contests the dating of the revolution’s inception.

“Modern deep learning started around 2010 in Hinton’s lab,” Ström says. “Speech was the first application. There was a step function in accuracy, just like in image processing. Speech recognition systems around that time got 30% fewer errors from one year to the next because they started using these methods. Computer vision is a little bit of a bigger field than speech recognition, and visualizing problems is an easy way to understand them. So maybe that’s why it’s easier to get started with something like ImageNet or a vision task.”

Second, Ström thinks that the question of what will come after deep learning may be ill posed, because the definition of deep learning keeps evolving to incorporate new AI innovations.

“There’s a famous quote about Lisp in the 1970s by Joel Moses,” Ström says. “‘Lisp is like a ball of mud. Add