With the rising popularity of ChatGPT, you might be curious about the mechanics behind what we often refer to as 'AI'. Specifically, how do machines acquire their 'learning' abilities? At the core of machine learning lies a neural network, a complex structure that can be metaphorically compared to a Plinko board. Imagine a neural network as a series of intricate layers filled with numbers that intricately transform a 'message' (or 'token', 'vector' - the terminology varies with the depth of mathematical involvement) as it traverses through these layers. In the context of AI, we typically input a prompt, and the network processes this 'message' to generate a relevant output. This process, akin to the random bouncing of ping pong balls in our Plinko analogy, is not the primary concern for users or even the engineers who build these systems. What truly matters is the outcome. This encapsulates the essence of machine learning: the internal dynamics of the network remain somewhat enigmatic, even to its creators, but the focus is on its ability to continually adjust and refine itself to produce the desired results.

This video was designed to complement my full-length YouTube video on "Is A.I. a Threat to the Board Game Hobby?" To that end, the most important takeaway from this video is that machine learning can only learn from examples we give it. It can emulate things it has seen before, but it doesn't invent new ideas. This may be the biggest obstacle to fully AI-designed (and playable) board games, although how we may be able to work around this is part of the discussion of the rest of the video.