The promise and peril of AI
Elsewhere on Recursive Analysis: "In the framework we have outlined, risk characterization cannot succeed as an activity added at the end of a risk analysis, but must result from a recursive process that includes problem formulation, analysis, and deliberation. " - Stern, P. C., & Fineberg, H. V. (1996). Understanding Risk: Informing Decisions in a Democratic Society. (How do you derive a problem set for AI?) (Also ran the NAS book through Gemini 1.5 Pro which remarked: "The text focuses on risk characterization and an analytic-deliberative process. The closest term is "recursive process", which refers to a series of tasks and feedbacks that allow for learning by all participants in the risk decision process." That yielded an answer to the question on problem sets which led to another about how it related to risk analysis. These models might eventually be considered as types of participants, although the ethics might differ. And like other areas of tech, they may also introduce potential research impacts. The classic modes of discussions were journals and conferences. Models introduce multimodal decision-making. Also mixtures of experts. Ideally, that can be used to scale up complexity. The social reaction is often unknown, but other types of correlation may now be feasible. Futurists would be looking for how to characterize the odds. How to know relevant regulations and when something will be out of bounds. There is usually going to be another term for a new type of capitalism. Socialists do not seem to want to be on either of the extremes, East or West.)