Abstract
Intelligence is a human construct to represent the ability to achieve goals. Given this wide berth, intelligence has been defined countless times, studied in a variety of ways and quantified using numerous measures. Understanding intelligence ultimately requires theory and quantification, both of which are elusive. My main objectives are to identify some of the central elements in and surrounding intelligence, discuss some of its challenges and propose a theory based on first principles. I focus on intelligence as defined by and for humans, frequently in comparison to machines, with the intention of setting the stage for more general characterizations in life, collectives, human designs such as AI and in non-designed physical and chemical systems. I discuss key features of intelligence, including path efficiency and goal accuracy, intelligence as a Black Box, environmental influences, flexibility to deal with surprisal, the regress of intelligence, the relativistic nature of intelligence and difficulty, and temporal changes in intelligence including its evolution. I present a framework for a first principles Theory of IntelligenceS (TIS), based on the quantifiable macro-scale system features of difficulty, surprisal and goal resolution accuracy. The proposed partitioning of uncertainty/solving and accuracy/understanding is particularly novel since it predicts that paths to a goal not only function to accurately achieve goals, but as experimentations leading to higher probabilities for future attainable goals and increased breadth to enter new goal spaces. TIS can therefore explain endeavors that do not necessarily affect Darwinian fitness, such as leisure, politics, games and art. I conclude with several conceptual advances of TIS including a compact mathematical form of surprisal and difficulty, the theoretical basis of TIS, and open questions.