LEADER 04632nam 22006615 450 001 9910869181103321 005 20240628125536.0 010 $a9789819731879$b(electronic bk.) 010 $z9789819731862 024 7 $a10.1007/978-981-97-3187-9 035 $a(MiAaPQ)EBC31506005 035 $a(Au-PeEL)EBL31506005 035 $a(CKB)32575089900041 035 $a(DE-He213)978-981-97-3187-9 035 $a(EXLCZ)9932575089900041 100 $a20240628d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCausality for Artificial Intelligence $eFrom a Philosophical Perspective /$fby Jordi Vallverdú 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (110 pages) 311 08$aPrint version: Vallverdú, Jordi Causality for Artificial Intelligence Singapore : Springer,c2024 9789819731862 327 $aChapter 1. Ground Zone: Definitions and Concepts about Causality -- Chapter 2. Causality and Artificial Intelligence -- Chapter 3. How Causality Works in non-Human Minds -- Chapter 4. Do Humans Think Causally, and How? Chapter 5. Pitfalls and Triumphs of Causal AI -- Chapter 6. Generative AI and Causality -- Chapter 7. Counterfactual Thinking for Machines -- Chapter 8. Defining and Debating Algorithmic Causality -- Chapter 9. Open Paradoxes: Retrocausality -- Chapter 10. My Kingdom for a Causal Algorithm. 330 $aHow 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. Professor Vallverdú is currently working on biomimetic cognitive architectures and multicognitive systems. His research has explored two main areas: epistemology and cognition. Since his early Ph.D. research on epistemic controversies, he has analyzed several aspects of computational epistemology. His latest research has focused on the causal challenges of machine learning techniques, particularly deep learning. One of his most promising advances is statistics meets causal graph reasoning (via Directed Acyclic Graphs), which still has several conceptual paths that need to be explored and identified. Counterfactual reasoning is a fundamental part of these open debates, which are under the analysis of Prof. Vallverdú. His current research is supported as part of the following projects: GEHUCT and ICREA Acadèmia. 606 $aArtificial intelligence 606 $aScience$xPhilosophy 606 $aMachine learning 606 $aCognitive neuroscience 606 $aComputers$xHistory 606 $aArtificial Intelligence 606 $aPhilosophy of Science 606 $aMachine Learning 606 $aCognitive Neuroscience 606 $aStatistical Learning 606 $aHistory of Computing 615 0$aArtificial intelligence. 615 0$aScience$xPhilosophy. 615 0$aMachine learning. 615 0$aCognitive neuroscience. 615 0$aComputers$xHistory. 615 14$aArtificial Intelligence. 615 24$aPhilosophy of Science. 615 24$aMachine Learning. 615 24$aCognitive Neuroscience. 615 24$aStatistical Learning. 615 24$aHistory of Computing. 676 $a006.3 700 $aVallverdú$b Jordi$0755843 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910869181103321 996 $aCausality for Artificial Intelligence$94170572 997 $aUNINA