Artificial intelligence (AI) has become a ubiquitous part of our lives. 从个性化建议到医学诊断, 人工智能多年来一直在塑造我们周围的世界. 尽管它无处不在, 人工智能继续存在, 如果不是完全在黑盒里, 至少在一个阴暗的地方. ISACA的 人工智能:机器学习入门, 深度学习, and Neural Networks seeks to illuminate that box with a clear and accessible guide, comprehensively covering the field with a generous offering of practical use cases, 例子和插图.
“At its core, AI is an umbrella term that is not uniformly unders太d. This publication arose from ISACA的 desire to provide essential information to digital trust practitioners, 不管工作功能如何, 最终形成了一个知识体系,乔恩·勃兰特说, ISACA导演, 专业实践与创新. “The primer is purposely comprehensive to increase the readers’ knowledge of AI and its inner workings, 不管他们对人工智能的熟悉程度如何.”
With a goal of appealing to those just beginning on their journeys in AI and those with some knowledge in the field, the publication offers a structured approach for readers to advance their understanding of AI by, 第一个, introducing fundamental AI concepts and the types of AI before progressing further into machine learning (ML), 大型语言模型(llm), 神经网络(NNs), 深度学习(DL), generative artificial intelligence (Generative AI) and AI’s future.
The immense promise of AI and the pace at which it has been incorporated into popular applications indicates a crucial need to address its potential risks thoughtfully and comprehensively, ensuring responsible development and deployment of applications driven by machine intelligence. 人工智能:机器学习入门, 深度学习, and Neural Networks 用一整章来解决这个问题, 可以说是书中最重要的一个, examining “the urgent need for responsible AI governance and a new regulatory authority dedicated to AI.”1
随着人工智能应用的继续, so, 太, 增加了网络攻击和意外使用的风险, requiring a concerted effort to effectively mitigate the multiple risks associated with it. 组织治理的及时覆盖, legislative actions and the development of frameworks for responsible AI equips readers with knowledge needed to navigate the complexities of the ethical and societal landscape of AI that has been evolving since the 1940s.
Tracing AI’s evolution from early theoretical concepts to the powerful applications we use today, the primer covers groundbreaking events in the advancement of machine intelligence, 包括ELIZA的开发, ，试图模拟类似人类的对话,”2 to the mainstream media attention given the historic chess match between IBM’s supercomputer Deep Blue and the reigning human chess champion, 卡斯帕罗夫, to recent developments in the subfield of Generative AI, which use generative adversarial networks (GANs) to output new content across media formats.
至于之前提到的“黑匣子”， 人工智能:机器学习入门, 深度学习, and Neural Networks examines the different types of AI that one might encounter categorized by functionality and capabilities. Whether reading the book from cover to cover or using it as a reference resource, readers will take from it knowledge covering traditional analytics and predictive AI, to emerging AI capabilities such as explainable AI (XAI) that make AI processes and outputs increasingly transparent and trustworthy.
这本书的很大一部分被保留了, 这是理所当然的, 机器学习(ML), a subfield of modern AI: “ML is an innovative subfield of AI that employs models and algorithms to enable machines to learn from data without explicit programming. These models and algorithms are based on various mathematical concepts such as statistics, 概率, 线性代数, 微积分, 和优化. 作为数据科学的重要组成部分, ML leverages advanced statistical methods to develop robust algorithms capable of solving complex problems.”3
Emphasizing the crucial role of data preparation in machine learning, the book covers various types of datasets and data processes, 从数据收集到数据分割训练, 验证, 和测试, 广泛探索监督式, 无人管理的, 强化学习, along with the various techniques each uses to provide their outputs across various domains. 它还检查神经网络, the foundation of DL – a powerful subset of machine learning that utilizes complex architectures to tackle increasingly intricate tasks. 各种DL架构, 比如自动编码器, 卷积神经网络(cnn), 递归神经网络, 和变压器, 描述, with particular attention given to their specific strengths and weaknesses.
Recognizing that AI is not a singular field of study but rather a multi-disciplinary one, 解决其技术和道德层面, this publication is designed for anyone engaged in using AI and monitoring and challenging its outputs; anyone exploring how machines learn and make decisions as well as generate human-like content; and anyone looking forward to what the future holds as AI begins to understand and respond to human emotions and social cues. 提供清晰易懂的解释， 人工智能:机器学习入门, 深度学习, and Neural Networks empowers its readers to engage in the current and future discourse on AI.
1 ISACA, 人工智能:机器学习入门, 深度学习, and Neural Networks, p. 137, 2024.
2 同前., p. 15
3 同前., p. 29
