A Personal Reflection on AI-Agent Adoption and Bayesian Learning

Summary

The author reflects on recent work studying quantitative methods and the development of the AI-agent industry. They argue that people may be pushing AI agents toward adoption faster than they can be responsibly or practically adapted, while research itself often takes much longer. The author is unsure whether hardware limitations explain the delay between research progress and deployment, mentioning Google as an example of a company that could release a model if it chose to do so. The post then turns to Bayesian theory, inspired by the work of Thomas Bayes, and asks whether its principles could be applied to create self-learning systems instead of relying primarily on large datasets. The author describes Bayesian work as both accessible and difficult, and invites programmers interested in translating these ideas into code to discuss the topic.