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.

智能是一种人类构造的概念，代表实现目标的能力。该论文旨在通过理论和定量分析，讨论智能的中心要素、挑战并提出一种基于第一原则的理论。关键特征包括路径效率、目标准确性等，并提出一种首次原则的智能理论框架 (TIS)。

智能理论：概念、模型、影响

A Theory of Intelligences: Concepts, Models, Implications

This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue
State Tracking. The goal of this task is to predict the intents and slots in
each user turn to complete the dialogue state tracking (DST) based on the
information provided by the task's schema. Different from traditional
stage-wise DST, we propose an end-to-end DST system to avoid error accumulation
between the dialogue turns. The DST system consists of a machine reading
comprehension (MRC) model for non-categorical slots and a Wide & Deep model for
categorical slots. As far as we know, this is the first time that MRC and Wide
& Deep model are applied to DST problem in a fully end-to-end way. Experimental
results show that our framework achieves an excellent performance on the test
dataset including 50% zero-shot services with a joint goal accuracy of 0.8652
and a slot tagging F1-Score of 0.9835.

本文介绍了 DSTC 8 轨道 4：基于模式的对话状态跟踪的方法以及测试结果。我们提出了一种端到端的 DST 系统，该系统包括针对非分类插槽的 MRC 模型和对于分类插槽的 Wide & Deep 模型。通过实验，证明我们的方法在测试数据集中得到了优异的性能。