Skepticism on Deep Learning, Reinforcement Learning, and DeepMind

Gary Marcus, NYU professor of psychology and neural science, is skeptical in light of DeepMind’s loss of $572M last year:

My own guess?

Ten years from now we will conclude that deep reinforcement learning was overrated in the late 2010s, and that many other important research avenues were neglected. Every dollar invested in reinforcement learning is a dollar not invested somewhere else, at a time when, for example, insights from the human cognitive sciences might yield valuable clues. Researchers in machine learning now often ask, “How can machines optimize complex problems using massive amounts of data?” We might also ask, “How do children acquire language and come to understand the world, using less power and data than current AI systems do?” If we spent more time, money, and energy on the latter question than the former, we might get to artificial general intelligence a lot sooner.


Deep learning has been so hyped that it will be difficult to meet expectations. And reinforcement learning has serious challenges when applied to real-world environments. But they are both revolutions in AI and will alter computing forever.