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Some may start doubting machine learning techniques like Deep Learning or Deep Reinforcement Learning. If you consider Google’s Deep Mind losing $572 million a year, many hope for the future of Artificial Intelligence will falter like this.
Here is the conclusion of this article:
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.
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