Millions of people, many of whom have never thought much about computer science, are experimenting with generative AI models such as the eminently conversational ChatGPT and creative image generator DALL-E. While these products reflect less of a technological breakthrough than AI's emergence into the public consciousness, the traction they have found is guiding massive investment streams--investment shaping how this technology will be applied for years to come. For those of us who have long been bullish on AI's potential to transform society, especially in key areas such as health and medicine, recent months have felt very much like science fiction has come to life. However, as delightful as it is to explore these capabilities--GPT-4 for example exceeded the passing score by 20 points on the U.S. medical licensing exam--the results of doing so mainly serve to highlight their shortcomings. The ability to read, retain and regurgitate all such data on demand makes today's AI good at everything--but great at nothing.

Artificial intelligence (AI) might be the next big thing in learning. Our excitement about video discs, Second Life, and Google Glass may have been fleeting, but AI is already making inroads. If you're like me, you want to learn more. AI sits at the intersection of powerful computer processing, data, sensor technology, and the human desire for more efficient and effective outcomes. At its foundation, AI requires math.