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DeepMind's Losses and the Future of Artificial Intelligence

DeepMind, likely the world’s largest research-focused artificial intelligence operation, is losing a lot of money fast, more than $1 billion in the past three years.

Certainly, genuine machine intelligence (also known as artificial general intelligence), of the sort that would power a Star Trek–like computer, capable of analyzing all sorts of queries posed in ordinary English, would be worth far more than that.

That technique combines deep learning, primarily used for recognizing patterns, with reinforcement learning, geared around learning based on reward signals, such as a score in a game or victory or defeat in a game like chess.

DeepMind gave the technique its name in 2013, in an exciting paper that showed how a single neural network system could be trained to play different Atari games, such as Breakout and Space Invaders, as well as, or better than, humans.

DeepMind’s StarCraft outcomes were similarly limited, with better-than-human results when played on a single map with a single “race” of character, but poorer results on different maps and with different characters.

(DeepMind’s recent results with kidney disease have been questioned in similar ways.) Deep reinforcement learning also requires a huge amount of data—e.g., millions of self-played games of Go.

The direct financial return, not counting publicity, has been modest by comparison, about $125 million of revenue last year, some of which came from applying deep reinforcement learning within Alphabet to reduce power costs for cooling Google’s servers.

Deep reinforcement learning could ultimately prove to be like the transistor, a research invention from a corporate lab that utterly changed the world, or it could be the sort of academic curiosity that John Maynard Smith once described as a “solution in search of problem.” My personal guess is that it will turn out to be somewhere in between, a useful and widespread tool but not a world-changer.

Alphabet might change the balance of its AI portfolio in various ways, but in a $100 billion-a-year revenue company that depends on AI for everything from search to advertising recommendation, it’s not crazy for Alphabet to make several significant investments.

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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