Go is very complex, and the fact that DeepMind could tackle this complexity is a huge technical achievement. No minimax-based AI could have tackled such a large state space.<p>However, other problems have even larger state spaces. Imagine writing an AI which read project Euler problem descriptions (in English) and output working code (in some given programming language). Keep outputs limited to 100-line scripts, max 80 characters per line.<p>There&#x27;s roughly 100 usable characters in ASCII, so the possible space of 100-line programs is roughly:<p>(10^2)^(80 * 100) = 10^16000.<p>You could simplify this by having the AI work with predefined tokens rather than individual characters, but it&#x27;s still a vast amount of combinations. Then consider 1000-line or 10000-line programs, and you see how high a mountain AI still has to climb. Humans are able to &quot;compress&quot; this state space via conceptual reasoning, which is much more complex than the &quot;pattern recognition&quot; many deep learning researchers are chasing.<p>(See &quot;Introduction to Objectivist Epistemology&quot; for more on how humans think in concepts - I&#x27;m planning to write more at some point on how this book shows where the practical limits of AI lie).