Since the 1950s, artificial intelligence has repeatedly overpromised and underdelivered. While recent years have seen incredible leaps thanks to deep learning, AI today is still narrow: it's fragile in the face of attacks, can't generalize to adapt to changing environments, and is riddled with bias. All these challenges make the technology difficult to trust and limit its potential to benefit society. On March 26 at MIT Technology Review's annual EmTech Digital event, two prominent figures in AI took to the virtual stage to debate how the field might overcome these issues. Gary Marcus, professor emeritus at NYU and the founder and CEO of Robust.AI, is a well-known critic of deep learning.

Instead we should say, "Can symbols help us?" And the answer is, overwhelmingly, yes. Almost all the world's software is built on symbols. And then you have to say, "Empirically, does the deep-learning stuff do what we want it to do?" And the problem so far is it has been model free.