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Titlebook: Causality for Artificial Intelligence; From a Philosophical Jordi Vallverdú Book 2024 The Editor(s) (if applicable) and The Author(s), unde

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發(fā)表于 2025-3-21 18:28:25 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Causality for Artificial Intelligence
副標(biāo)題From a Philosophical
編輯Jordi Vallverdú
視頻videohttp://file.papertrans.cn/243/242041/242041.mp4
概述Is essential reading for all machine learning and AI designers wanting to understand the nature of causal thinking.Presents thoughts at the interface of various academic disciplines or fields, includi
圖書封面Titlebook: Causality for Artificial Intelligence; From a Philosophical Jordi Vallverdú Book 2024 The Editor(s) (if applicable) and The Author(s), unde
描述.How can we teach machine learning to identify causal patterns in data?? This book explores the very notion of “causality”, identifying from a naturalistic and evolutionary perspective how living systems deal with causal relationships. At the same time, using this knowledge to identify the best ways to apply such biological models in machine learning scenarios...One of the more fundamental challenges for AI experts is to design machines that can understand the world, identifying the basic rules that govern reality.? Statistics are powerful and fundamental for this process, but they are only one of the necessary tools. Counterfactual thinking is the other part of the necessary process that will help machines to become intelligent. This book explains the paths that can lead to algorithmic causality...It is essential reading for those who are not afraid of thinking at the interface of various academic disciplines or fields (AI, machine learning, philosophy,? neuroscience, anthropology, psychology, computer sciences), and who are interested in the analysis of causal thinking and the ways in which cognitive systems (natural or artificial) can act in order to understand their environment
出版日期Book 2024
關(guān)鍵詞Deep Learning; Machine Learning; Computer Sciences; Artificial Intelligence; Causality; Algorithm; Philoso
版次1
doihttps://doi.org/10.1007/978-981-97-3187-9
isbn_softcover978-981-97-3189-3
isbn_ebook978-981-97-3187-9
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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發(fā)表于 2025-3-21 20:35:25 | 只看該作者
Statistics for Industry and Technologylogical challenges arising when imparting AI systems with the ability to comprehend causation. Drawing from philosophy, cognitive science, and AI research, the chapter prompts reflection on profound philosophical questions shaping AI’s evolution. It envisions AI systems transcending mere prediction
板凳
發(fā)表于 2025-3-22 03:49:03 | 只看該作者
Aaron Childs,K. S. Sultan,N. Balakrishnannderstanding exhibit flexibility and adaptability. Recent research by Johnston, Brecht, and Nieder is explored, highlighting statistical inference abilities in crows and challenging the traditional view of this skill as uniquely human. The discussion extends to the Cambrian explosion, associating th
地板
發(fā)表于 2025-3-22 04:59:51 | 只看該作者
Advances in Stochastic Simulation Methodsinformation processing, and the necessity for context integration. Bioinspiration emerges as a key element in developing AI systems that not only solve problems but also exhibit creativity, draw inspiration from diverse sources, and generate original ideas. Thirteen successful strategies for impleme
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發(fā)表于 2025-3-22 14:28:13 | 只看該作者
https://doi.org/10.1007/978-3-319-29975-4rns related to the dilution of statistics in natural language interactions with AI, emphasizing the need for a balanced approach that maintains accessibility while upholding the rigor of formal statistical training, particularly in academic contexts. The discussion concludes by questioning the feasi
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發(fā)表于 2025-3-22 18:56:47 | 只看該作者
Carlos Pérez-Galván,I. David L. Bogleearning, neural network vulnerability to adversarial examples, and the challenge of operating within non-Euclidean spaces. The latter part of the chapter addresses criticisms of AI and robotics, categorizing them into anti-technological attitudes and humanist views. Critiques based on fear of unpred
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發(fā)表于 2025-3-23 05:18:31 | 只看該作者
Book 2024us academic disciplines or fields (AI, machine learning, philosophy,? neuroscience, anthropology, psychology, computer sciences), and who are interested in the analysis of causal thinking and the ways in which cognitive systems (natural or artificial) can act in order to understand their environment
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發(fā)表于 2025-3-23 07:41:56 | 只看該作者
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