AI Foundations: Algorithmic Information Theory
Delve into AI core principles by examining computational complexity, Kolmogorov complexity, and information theory's role in machine learning systems.
Delve into AI core principles by examining computational complexity, Kolmogorov complexity, and information theory's role in machine learning systems.
Dive deep into the theoretical underpinnings of Artificial Intelligence with this advanced course on Algorithmic Information Theory. Discover how this groundbreaking field provides a unifying framework for understanding machine learning, reasoning, mathematics, and even human intelligence. Learn to view AI systems as abstract computations aimed at compressing information, gaining powerful insights into their capabilities and limitations. This course covers key concepts such as Kolmogorov complexity, universal Turing machines, and algorithmic probability, bridging the gap between theoretical computer science and practical AI applications. Ideal for those seeking a deeper understanding of AI's theoretical foundations and its future potential.
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What you'll learn
Measure and compare information using algorithmic compression techniques
Apply algorithmic information principles to language detection and semantic similarity
Understand how probability and randomness can be defined in purely algorithmic terms
Analyze the theoretical limits of AI using concepts from AIT
Formulate optimal hypotheses for machine learning tasks using AIT principles
Apply AIT to solve analogies and detect anomalies in data
Skills you'll gain
This course includes:
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Graded assignments, exams
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Limited Access access
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There are 5 modules in this course
This advanced course explores Algorithmic Information Theory (AIT) and its profound implications for Artificial Intelligence. Over five comprehensive modules, students will delve into the theoretical foundations that underpin modern AI systems. The course begins by introducing the concept of complexity as code length, progressing to more advanced topics such as algorithmic probability and Gödel's theorem. Students will learn how to measure information through compression, compare algorithmic information with Shannon's information theory, and use these principles to detect languages and compute meaning similarity. The course also covers the application of AIT to machine learning, demonstrating how learning tasks can be viewed as complexity minimization problems. Advanced topics include the limits of AI as revealed by AIT, optimal hypothesis formation in learning tasks, and the algorithmic basis of subjective information, relevance, and even aesthetics. Throughout the course, students will gain a new perspective on AI, seeing various techniques - from clustering to neural networks - as methods of information compression. This theoretical framework not only deepens understanding of current AI systems but also provides insights into the future potential and limitations of artificial intelligence.
Describing data
Module 1
Measuring Information
Module 2
Algorithmic information & mathematics
Module 3
Machine Learning and Algorithmic Information
Module 4
Subjective information
Module 5
Fee Structure
Instructors
1 Course
Pioneering Cognitive Scientist Advancing Human Communication Theory
Jean-Louis Dessalles, born in 1956, serves as an associate professor at Télécom Paris in the LTCI laboratory and DIG research team, where he combines artificial intelligence with cognitive science to understand human communication. After graduating from École Polytechnique in 1976 and Télécom Paris in 1981, he earned his PhD in 1993 and HDR from Université Paris-Sorbonne in 2008. His groundbreaking research centers on the Theory of Simplicity, which he developed over the past decade to model narrative interest and argumentative relevance in human communication
1 Course
Innovative AI Researcher Specializing in Legal Language Processing
Nils Holzenberger has established himself as an Associate Professor at Télécom Paris and Institut Polytechnique de Paris, where he brings his expertise in legal Natural Language Processing and artificial intelligence applications to both research and education. Following his doctoral studies at Johns Hopkins University, he has built an impressive career investigating the intersection of AI and legal reasoning. His academic work spans crucial areas including statutory reasoning, legal text analysis, and the development of AI systems for legal applications. At Télécom Paris, he contributes to the Data & Artificial Intelligence program, teaching advanced courses that bridge theoretical concepts with practical applications. His research portfolio demonstrates a particular focus on how artificial intelligence systems can understand and process legal texts, while his teaching approach emphasizes making complex AI concepts accessible to students. Holzenberger's work continues to shape the understanding of how machine learning models can be applied to legal reasoning and statutory interpretation.
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