Chess is not a game. Chess is a well-defined form of computation. You may not be able to work out the answers, but in theory, there must be a solution, a right procedure in any position---John von Neumann The advent of computer chess engines based, such as AlphaZero, LCZero and Stockfish 14 NNUE, provides us with the ability to study optimal play. AI chess algorithms are based on pattern matching, efficient search and data-centric methods rather than rules based. Together with an objective functions based on maximising the probability of winning, we can now see what optimal play and strategies look like. One caveat is the black-box nature of these algorithms and lack of insight into the features that are empirically learned from self play.

Ongoing advances in electronics and computing have introduced opportunities to achieve things that once seemed inconceivable: build autonomous machines, solve complex deep learning problems, and communicate instantaneously across the planet. Yet, for all the advances, today's systems--which rely on electronic processors--are grounded in a frustrating reality: the sheer physics of electrons limits their bandwidth and forces them to produce enormous heat, which means they draw vast amounts of energy. As demand for fast and low-energy artificial intelligence (AI) grows, researchers are exploring ways to push beyond electrons and into the world of photons. They are replacing electronic processors with photonic designs that incorporate lasers and other light components. While there is skepticism among some observers that the technology can transform analog computing, researchers in the optical space are now building systems demonstrating significant benefits in AI and deep learning.