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.

翻译：智能是人类用于表征实现目标能力的一种建构。在这一宽泛定义下，智能已被无数次定义、以多种方式研究，并通过众多度量标准进行量化。理解智能最终需要理论和量化，而这二者都难以捉摸。我的主要目标是识别智能及其相关领域中的核心要素，探讨其面临的挑战，并提出基于第一性原理的理论。我聚焦于人类定义且为人类服务的智能，并常与机器智能进行对比，旨在为生命、集体、人工智能等人类设计系统，以及非设计的物理化学系统中更普适的表征奠定基础。我讨论了智能的关键特征，包括路径效率与目标准确性、作为黑箱的智能、环境影响、应对意外信息的灵活性、智能的回归性、智能与难度的相对性本质，以及包含进化在内的智能时间变化。基于难度、意外信息与目标解析准确性这些可量化的宏观系统特征，我提出了第一性原理的"智能理论"（TIS）框架。所提出的不确定性/求解与准确性/理解的分区尤为新颖，因为它预测通向目标的路径不仅具有准确实现目标的功能，还能作为实验探索，提升未来可达成目标的概率，并拓展进入新目标空间的广度。因此，TIS 能够解释诸如休闲、政治、游戏和艺术等未必影响达尔文适应度的行为。最后，我总结了 TIS 的若干概念进步，包括意外信息与难度的紧凑数学形式、TIS 的理论基础及待解决问题。