In perfect information board games like chess, AI search algorithms can be successful, but in games like poker, intuition and human decision-making are crucial factors. AI can use Monte Carlo rollouts to simulate game outcomes, but it cannot replace human intuition.
The pattern recognition approach used by AlphaGo has similarities with the approach used by human grandmasters. While some believe that human grandmasters can quickly recognize the right move and the value of a position, the speed of the search tree quickly determines the credibility of a move.
Natural language processing has taken over a game of Diplomacy, which is much more complicated than traditional games such as chess or poker. The game is designed to incentivize players to put a stop to one superpower, in contrast to games like Settlers of Catan where natural language processing could also be implemented.
The podcast discusses how current AI builders and neural networks function, how they learn from human interactions, and the potential for advancements to improve lifetime experiences and human interactions online.
The capabilities of neural networks in deep learning have been surprising and beautiful, particularly with regards to achievements in chess play without human supervision. Despite this, there is still work to be done in solving problems in computer vision and vision for robots.