AI & I: An Intellectual History of Artificial Intelligence (1版)

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【簡介】 A concise and illuminating history of the field of artificial intelligence from one of its earliest and most respected pioneers.AI & I is an intellectual history of the field of artificial intelligence from the perspective of one of its first practitioners, Eugene Charniak. Charniak entered the field in 1967, roughly 12 years after AI’s founding, and was involved in many of AI’s formative milestones. In this book, he traces the trajectory of breakthroughs and disappointments of the discipline up to the current day, clearly and engagingly demystifying this oft revered and misunderstood technology. His argument is controversial but well supported: that classical AI has been almost uniformly unsuccessful and that the modern deep learning approach should be viewed as the foundation for all the exciting developments that are to come. Written for the scientifically educated layperson, this book chronicles the history of the field of AI, starting with its origin in 1956, as a topic for a small academic workshop held at Dartmouth University. From there, the author covers reasoning and knowledge representation, reasoning under uncertainty, chess, computer vision, speech recognition, language acquisition, deep learning, and learning writ large. Ultimately, Charniak takes issue with the controversy of AI--the fear that its invention means the end of jobs, creativity, and potentially even humans as a species--and explains why such concerns are unfounded. Instead, he believes that we should embrace the technology and all its potential to benefit society.

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (1版)

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【簡介】 Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science.Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven resultsTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist’s decision-making processCultivates critical thinking throughout the entire data science life cycleProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutionsSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners