Alphabet’s DeepMind lost $572 million last year. What does it mean?

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. DeepMind also has more than $1 billion in debt due in the next 12 months.

Does this mean that AI is falling apart?

WIRED OPINION ABOUT Gary Marcus is founder and CEO of Robust.AI and a professor of psychology and neural science at NYU. He is the author, with Ernest Davis, of the forthcoming Rebooting AI: Building Artificial Intelligence We Can Trust.

Not at all. Research costs money, and DeepMind is doing more research every year. The dollars involved are large, perhaps more than in any previous AI research operation, but far from unprecedented when compared with the sums spent in some of science’s largest projects. The Large Hadron Collider costs something like $1 billion per year and the total cost of discovering the Higgs Boson has been estimated at more than $10 billion. 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.

Still, the rising magnitude of DeepMind’s losses is worth considering: $154 million in 2016, $341 million in 2017, $572 million in 2018. In my view, there are three central questions: Is DeepMind on the right track scientifically? Are investments of this magnitude sound from Alphabet’s perspective? And how will the losses affect AI in general?

On the first question, there is reason for skepticism. DeepMind has been putting most of its eggs in one basket, a technique known as deep reinforcement learning. 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. The paper was an engineering tour de force, and presumably a key catalyst in DeepMind’s January 2014 sale to Google. Further advances of the technique have fueled DeepMind’s impressive victories in Go and the computer game StarCraft.

The trouble is, the technique is very specific to narrow circumstances. In playing Breakout, for example, tiny changes—like moving the paddle up a few pixels—can cause dramatic drops in performance. 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. To switch characters, you need to retrain the system from scratch.

LEARN MORE The WIRED Guide to Artificial Intelligence

In some ways, deep reinforcement learning is a kind of turbocharged memorization; systems that use it are capable of awesome feats, but they have only a shallow understanding of what they are doing. As a consequence, current systems lack flexibility, and thus are unable to compensate if the world changes, sometimes even in tiny ways. (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. That’s far more than a human would require to become world class at Go, and often difficult or expensive. That brings a requirement for Google-scale computer resources, which means that, in many real-world problems, the computer time alone would be too costly for most users to consider. By one estimate, the training time for AlphaGo cost $35 million; the same estimate likened the amount of energy used to the energy consumed by 12,760 human brains running continuously for three days without sleep.

But that’s just economics. The real issue, as Ernest Davis and I argue in our forthcoming book Rebooting AI, is trust. For now, deep reinforcement learning can only be trusted in environments that are well controlled, with few surprises; that works fine for Go—neither the board nor the rules have changed in 2,000 years—but you wouldn’t want to rely on it in many real-world situations.

Little Commercial Success

In part because few real-world problems are as constrained as the games on which DeepMind has focused, DeepMind has yet to find any large-scale commercial application of deep reinforcement learning. So far Alphabet has invested roughly $2 billion (including the reported $650 million purchase price in 2014). 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 be like the transistor, a research invention that changed the world, or it could be a “solution in search of problem.”

What works for Go may not work for the challenging problems that DeepMind aspires to solve with AI, like cancer and clean energy. IBM learned this the hard way when it tried to take the Watson program that won Jeopardy! and apply it to medical diagnosis, with little success. Watson worked fine on some cases and failed on others, sometimes missing diagnoses like heart attacks that would be obvious to first-year medical students.