This is an AI related post on the nature and philosophy of intelligence. In the various fields that study the mind, human or otherwise, there are many definitions (and lack of) for the term 'intelligence'. What is it, how can we measure it, how can we reproduce it? What implications does this have in the fields of AI, machine learning, and data science? A paper [1] by Shane Legg and Marcus Hutter, attempted to survey the definition from these various fields. The following are some sample…

The use of formal statistical methods to analyse quantitative data in data science has increased considerably over the last few years. One such approach, Bayesian Decision Theory (BDT), also known as Bayesian Hypothesis Testing and Bayesian inference, is a fundamental statistical approach that quantifies the tradeoffs between various decisions using…

Fish schools, bird flocks, and bee swarms. These combinations of real-time biological systems can blend knowledge, exploration, and exploitation to unify intelligence and solve problems more efficiently. There’s no centralized control. These simple agents interact locally, within their environment, and new behaviors emerge from the group as a whole. In the world of…