I know a lot of people in the West are baffled by the Mbappé-Mao storm in China - which in itself shows that Mbappé's ignorance doesn't come out of nowhere... To understand it, here's the best analogy I could think of: imagine the biggest Chinese star in the NBA, with sponsorship deals all over the US. One day he's asked for his favorite meme of himself. He giggles: "the one where they put my face on some guy, I think he was an American slave owner." When the "some guy" in question is George Washington. Whatever you think of the label, you know exactly how that would land in the US: an ungrateful foreigner getting rich off Americans, laughing at the country's founding father in an offensive way - at least to a big share of the population - without even knowing his name. A founding father is the story a nation tells itself about where it comes from. Laugh at him as a foreigner while cashing in on that nation's consumers, and that's not going to go well in any country. The label is also extremely un-nuanced: yes, all important historical figures have their share of darkness and Washington and Mao are no exception. China actually recognizes this: the official line is that he was "70% right and 30% wrong," and the Party's 1981 resolution condemned how his "personal arbitrariness gradually undermined democratic centralism in Party life and the personality cult grew graver and graver" (marxists.org/subject/china/d…) Same for Washington: it's officially acknowledged too. Or at least it was until this year: in January, the National Park Service tore down an exhibit in Philadelphia about the people Washington enslaved there, under a Trump executive order targeting content that "inappropriately disparage[s]" Americans (associationforpublicart.org/…). Which goes to show China isn't uniquely touchy about its founding father...

Green bubble texts are less secure. So why would Apple block a new app allowing Android users to chat with iPhone users on iMessage? Big Tech executives are protecting profits by squashing competitors. Chatting between different platforms should be easy and secure.

Some of you may wonder why I occasionally sound fairly confident commenting on certain aspects of AI. I'm mostly known as a musician and music business educator, but before any of that I studied mathematics at UC Santa Cruz in the early 1980s and graduated with highest honors. UCSC's math department at the time leaned heavily toward the abstract side of mathematics. My advisor was Gerhard Ringel, a renowned mathematician best known for his work in graph theory and for helping solve the famous Heawood Map Coloring Problem. He was one of the major figures in topological graph theory. He encouraged me to study something called Matroids for my undergraduate thesis. At the time, matroid theory was a fairly obscure corner of mathematics but he sensed its importance. Matroids are abstract structures that capture the concept of "independence" and provide a common language connecting linear algebra, graph theory, and optimization. What made matroids important is that they helped mathematicians understand why certain greedy optimization algorithms work and how to efficiently choose the "best" subset of things subject to complex constraints. That turns out to be a surprisingly deep problem. Back then this all felt very theoretical. But over the last several decades matroids, graph-theoretic methods, and related ideas such as submodular optimization became foundational tools in computer science. Today they show up in areas ranging from network design and data compression to feature selection, clustering, recommendation systems, and other optimization problems that sit underneath modern machine learning. While today's large AI models involve many newer ideas, some of the underlying mathematical questions are still variations of: What information should be selected? What can be discarded? How do you find the best solution in an unimaginably large search space? I've forgotten most of the technical details I once knew well. That's what happens after forty years. But I still remember the intellectual landscape, the kinds of problems people were trying to solve, and the mathematical intuition behind them. So when AI started advancing rapidly over the last few years, I made an effort to understand how modern machine learning actually works. One of the things that surprised me was not how different it was from what I studied, but how many of the same themes kept reappearing: graphs, optimization, high-dimensional spaces, independence, information selection, and efficient search. That certainly doesn't make me an expert on every aspect of AI. Far far from it. But it does mean I'm not approaching the subject entirely as an outsider, either. The road from a 1980s undergraduate thesis on matroids (while playing with Camper Van Beethoven) to modern machine learning is a lot shorter than it might appear.