or are too “high frequency” (like static) rather than “low frequency” (more continuous, like actual real-world features). Just because AI systems sometimes end up in local minima, don’t conclude that this makes them any less like life. Humans—indeed, probably all life-forms—are often stuck in local minima. Take our understanding of the game of Go, which was taught and learned and optimized by humans for thousands of years. It took Als less than three years to find out that we’d been playing it wrong all along and that there were better, almost alien, solutions to the game which we’d never considered—mostly because our brains don’t have the processing power to consider so many moves ahead. Even in chess, which is ten times easier and was thought to be understood, brute- force machines could beat us at our own strategies. Chess, too, turned out, when explored by superior neural-network AI systems, to have weird but superior strategies we'd never considered, like sacrificing queens early to gain an obscure long-term advantage. It’s as if we had been playing 2D versions of games that actually existed in higher dimensions. If any of this sounds familiar, it’s because physics has been wrestling with these sorts of topological problems for decades. The notion of space being many-dimensional, and math reducing to understanding the geometries and interactions of “membranes” beyond the reach of our senses, is where Grand Unified Theorists go to die. But unlike multidimensional theoretical physics, AI is something we can actually experiment with and measure. So that’s what we’re going to do. The next few decades will be an explosive exploration of ways to think that 7 million years of evolution never found. We’re going to rock ourselves out of local minima and find deeper minima, maybe even global minima. And when we’re done, we may even have taught machines to seem as smart as a mosquito, forever descending the cosmic gradients to an ultimate goal, whatever that may be.