> My money is on the fact that human intelligence guiding a search cuts another couple orders of magnitude off of how much of a long-shot discovering an intelli

> My money is on the fact that human intelligence guiding a search cuts another couple orders of magnitude off of how much of a long-shot discovering an intelligent algorithm would be via a random search. I certainly agree that directing/biasing evolutionary processes can be a reasonably efficient search strategy. The difficulty is that it still seems to be a fallback, suited to problems where we don't have a more informed strategy. For example, if we can calculate gradients, we're probably better off doing gradient descent. If we can perform deduction/induction on symbols, we're probably better off doing that. Those problems where evolutionary processes seem well suited are those where we might not know how to bias the search. I think the best place for such approaches at the moment is meta-level algorithms, where "smarter" algorithms (like gradient descent) are applied to the underlying problems we care about, but the parameters, policies, etc. evolve on top (e.g. step sizes, which algorithms to use, when to restart, scheduling concurrent attempts, etc.). That's the approach taken by NEAT for example, where a genetic algorithm comes up with neural network topologies, whilst those neural networks are trained using backpropagation.