Ravi Kashyap

Ravi Kashyap

We discuss the objectives of automation equipped with non-trivial decision making, or creating artificial intelligence, in the financial markets and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. A consideration of these requirements allows us to propose a test of intelligence for trading programs, on the lines of the Turing Test, long the benchmark for intelligent machines. We discuss the application of this methodology to the dilemma in finance, which is whether, when and how much to Buy, Sell or Hold.

Ravi Kashyap

We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. This suggests that our attempts at AI could have been misguided. What we actually need to strive for can be termed artificial curiosity, AC, and intelligence happens as a consequence of those efforts. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. We start with the intuition for this line of reasoning and formalize it with a series of definitions, assumptions, ingredients, models and iterative improvements that will be necessary to make the incubation of intelligence a reality. Our discussion provides conceptual modifications to the Turing Test and to Searle's Chinese room argument. We discuss the future implications for society as AI becomes an integral part of life. We provide a road-map for creating intelligence with the technical parts relegated to the appendix so that the article is accessible to a wide audience. The central techniques in our formal approach to creating intelligence draw upon tools and concepts widely used in physics, cognitive science, psychology, evolutionary biology, statistics, linguistics, communication systems, pattern recognition, marketing, economics, finance, information science and computational theory highlighting that solutions for creating artificial intelligence have to transcend the artificial barriers between various fields and be highly multi-disciplinary.