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| Photo: La Tigre |
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?...
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.
Recommended Reading
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| Rebooting AI: Building Artificial Intelligence We Can Trust |
Source: WIRED
