At DeepMind, a subsidiary of Alphabet, Silver has led the development of techniques that let computers learn for themselves how to solve problems that once seemed intractable.
In November 2019, DeepMind released details of MuZero, a version that learns to play these and other games—but crucially without needing to know the rules beforehand.
The real world is massively complex, and we haven’t built something which is like a human brain that can adapt to all these things.
We are, of course, looking at ways to apply MuZero to real world problems, and there are some encouraging initial results.
The first step in taking that journey is to try to understand what it even means to achieve intelligence? Because it certainly flies in the face of how a lot of people view AI, which is that there’s this incredibly complex collection of mechanisms involved in intelligence, and each one of them has its own kind of problem that it’s solving or its own special way of working, or maybe there’s not even any clear problem definition at all for something like common sense.
Many people view reinforcement learning as one of many hammers that you could apply to solve the many problems that we need to solve in AI.
If we want to try and describe intelligence as best as possible, I think reinforcement learning essentially characterizes what we really mean by intelligence.
The beauty of MuZero is that because it’s building its own model, it’s starting to understand how the world works—to imagine things.