AI and Geoeconomics

"Exploring the intersection of #AI and human potential. Can #GenerativeAI help us achieve a state of #Flow, that sweet spot of total absorption where we lose track of time and produce our best work? Let's dive into the possibilities. #CognitiveScience #ArtificialIntelligence" Mihaly Csikszentmihalyi Mihaly Csikszentmihalyi  Wikipedia Mihaly Csikszentmihalyi (1934-2021) was a HungarianAmerican psychologist who is best known for his work on the concept of "flow". He defined flow as "a state in which people are so involved in an activity that nothing else seems to matter; the experience is so enjoyable that people will continue to do it even at great cost, for the sheer sake of doing it." Csikszentmihalyi was born in Hungary in 1934. He studied psychology at the University of Budapest and the University of Chicago, where he received his PhD in 1965. After completing his PhD, Csikszentmihalyi taught at Lake Forest College and the University of Chicago. In ...

The rules of the game are undergoing significant and irreversible changes. The combination of AI/NLP paradigms and techniques with existing mathematical methods and engineering, such as Robust Control, is reshaping the landscape of systematic investment strategies. These advancements are introducing new territories, enriching behaviors, and enhancing the performance of investment strategies. They achieve this by incorporating new data, such as insights and reasoning shared by other knowledgeable individuals, enabling the detection of causal relationships beyond correlations, and facilitating better decision-making in the presence of uncertainties. However, it is important to remember that in this process, the pursuit of perfection can sometimes hinder progress. This paradigm shift raises the question: Is this the end of traditional quants or the emergence of a new breed? Are these AI-driven quants allies or adversaries to fundamental discretionary investment managers? Will they promote...

"Cutting plane" methods converge on the optimal values of a mathematical function by repeatedly cutting out regions of a much larger set of possibilities (gold sphere). Credit: Jose-Luis Olivares/MIT Optimisation is everywhere Source: Phys.org 23 October 2015 Optimisation problems are everywhere in engineering: Balancing design trade-offs is an optimisation problem, as are scheduling and logistical planning. The theory—and sometimes the implementation—of control systems relies heavily on optimisation, and so does machine learning, which has been the basis of most recent advances in artificial intelligence. Last October, at the IEEE Symposium on Foundations of Computer Science, a trio of present and past MIT graduate students won a best-student-paper award for a new "cutting-plane" algorithm, a general-purpose algorithm for solving optimisation problems. The algorithm improves on the running time of its most efficient predecessor, and the researcher...