Artificial intelligence has come a long way since English mathematician, logician, and cryptographer Alan Turing’s seminal 1950 essay, “Computing Machinery and Intelligence,” which explored the idea of building computers capable of imitating human thought. In 1997, almost 50 years after Turing’s essay, AI posted a historic breakthrough when the IBM supercomputer Deep Blue won a chess match against reigning world champion Garry Kasparov. Since then, AI’s capabilities have improved rapidly, largely through advances in machine learning (ML), especially in ML models that use digital neural networks to classify text, images, or other data. (See “Machine Learning,” Econ Focus, Third Quarter 2018.) ML is now commonly used in industrial applications, and it underpins a vast number of consumer services, from Google searches to Netflix movie recommendations. Of more recent note, ML technology is the basis of the new generative AI programs, such as ChatGPT, designed to, among other things, conduct useful conversations with human beings. Financial institutions in the U.S. have hardly sat idle amid these developments. On the contrary, they have developed and implemented AI-based applications for a wide variety of purposes.