Stephen Downes

This is a topic we touched on a lot when we were defining connectivism back in the early 2000s. Connectivism was in part the thesis that the way individuals - and societies - learn is described by graph learning. Graph learning is explained mathematically with graph theory, with origins in Euler's Seven Bridges of Königsberg problem, and realized physically in any number of naturally occurring graphs, including the chirping of crickets, bird murmurations, metronomes on a plank, and social networks. Graph neural networks formed the basis for deep learning, which in short order became the AI models we know and love today. The next challenge, which is alluded to at the end of this article, is "ask how can we best integrate graph structured data with artificial intelligence (AI) to allow for the encoding of graphs for large language models (LLMs)?" Via Data Science Weekly.