The aim of this primer is to introduce the subject of knowledge engineering
in a concise but synthetic way to develop the reader's intuition about the
area.

本文旨在从简洁且综合的方式介绍知识工程学科，以发展读者对该领域的直觉。

知识工程入门

A Knowledge Engineering Primer

AI explanations are often mentioned as a way to improve human-AI
decision-making. Yet, empirical studies have not found consistent evidence of
explanations' effectiveness and, on the contrary, suggest that they can
increase overreliance when the AI system is wrong. While many factors may
affect reliance on AI support, one important factor is how decision-makers
reconcile their own intuition -- which may be based on domain knowledge, prior
task experience, or pattern recognition -- with the information provided by the
AI system to determine when to override AI predictions. We conduct a
think-aloud, mixed-methods study with two explanation types (feature- and
example-based) for two prediction tasks to explore how decision-makers'
intuition affects their use of AI predictions and explanations, and ultimately
their choice of when to rely on AI. Our results identify three types of
intuition involved in reasoning about AI predictions and explanations:
intuition about the task outcome, features, and AI limitations. Building on
these, we summarize three observed pathways for decision-makers to apply their
own intuition and override AI predictions. We use these pathways to explain why
(1) the feature-based explanations we used did not improve participants'
decision outcomes and increased their overreliance on AI, and (2) the
example-based explanations we used improved decision-makers' performance over
feature-based explanations and helped achieve complementary human-AI
performance. Overall, our work identifies directions for further development of
AI decision-support systems and explanation methods that help decision-makers
effectively apply their intuition to achieve appropriate reliance on AI.

此研究探讨了决策者对于任务预测和解释的一些直觉，发现了三种类型的直觉：关于任务结果、特征和 AI 限制。此外，该研究还通过两种类型的解释，探索了决策者如何运用自己的直觉来决定是否应该依赖 AI 预测，并发现了三条决策者运用自己直觉的路径。研究表明，AI 解释并不能总是提高决策者的决策效果并可能提高其对 AI 的过度依赖，但使用基于示例的解释则可能有助于完善人工智能的表现。

人的直觉在人工智能决策中的依赖作用及其解释

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

Artificial intelligence (AI) technologies revolutionize vast fields of
society. Humans using these systems are likely to expect them to work in a
potentially hyperrational manner. However, in this study, we show that some AI
systems, namely large language models (LLMs), exhibit behavior that strikingly
resembles human-like intuition - and the many cognitive errors that come with
them. We use a state-of-the-art LLM, namely the latest iteration of OpenAI's
Generative Pre-trained Transformer (GPT-3.5), and probe it with the Cognitive
Reflection Test (CRT) as well as semantic illusions that were originally
designed to investigate intuitive decision-making in humans. Our results show
that GPT-3.5 systematically exhibits "machine intuition," meaning that it
produces incorrect responses that are surprisingly equal to how humans respond
to the CRT as well as to semantic illusions. We investigate several approaches
to test how sturdy GPT-3.5's inclination for intuitive-like decision-making is.
Our study demonstrates that investigating LLMs with methods from cognitive
science has the potential to reveal emergent traits and adjust expectations
regarding their machine behavior.

本研究揭示了大型语言模型 (LLMs) 在某些情况下表现出具有人类直觉的行为，包括许多认知错误，并且通过使用 GPT-3.5 等最新技术的模型及进行人类直觉决策的试探研究证明了这一点，并探究了几种方法来测试 GPT-3.5 的倾向于类似直觉的决策及其机器行为等方面，以揭示出这些模型在认知科学方面的新兴特征和调整预期的潜力。

机器直觉：揭示 GPT-3.5 中类似人类直觉的决策制定

Machine intuition: Uncovering human-like intuitive decision-making in GPT-3.5

Principal component analysis (PCA) is a mainstay of modern data analysis - a
black box that is widely used but (sometimes) poorly understood. The goal of
this paper is to dispel the magic behind this black box. This manuscript
focuses on building a solid intuition for how and why principal component
analysis works. This manuscript crystallizes this knowledge by deriving from
simple intuitions, the mathematics behind PCA. This tutorial does not shy away
from explaining the ideas informally, nor does it shy away from the
mathematics. The hope is that by addressing both aspects, readers of all levels
will be able to gain a better understanding of PCA as well as the when, the how
and the why of applying this technique.

探究主成分分析的背后真相及数学原理，为所有级别的读者提供更好的理解和应用此技术的时间、方法和动机。