Telecom Paris
Dep. Informatique & Réseaux JL. Dessalles ← Home page juillet 2022 
IA325: Algorithmic Information and Artificial Intelligence
Lecturers: JeanLouis Dessalles
and  PierreAlexandre Murena 
Etienne Houzé 
Creating Artificial intelligence is one of the greatest challenges in the history of humankind. Programs are said to be "intelligent" because they solve difficult problems, such as playing the game of Go. Unfortunately, Artificial intelligence is often perceived as no more than that, just a collection of brilliant, innovative methods to solve problems. Most people don’t imagine that intelligent behaviour can be universally described in terms of algorithmic information.
There is currently a growing interest in Complexity and AIT for their role in the theoretical foundations of Artificial Intelligence. Moreover, practical approaches to complexity based on compression techniques or minimum length descriptions offer efficient techniques in machine learning. AIT plays an important role in mathematics, for instance to set limits to what a formal theory or an intelligent system can do. More recently, AIT has been shown essential to address aspects of human intelligence, such as perception, relevance, decision making and emotional intensity.
Caveat:
Chapter 1. 
Description complexity

Complexity measured by code length.
Complexity of integers. Conditional Complexity. 
Chapter 2.  Measuring Information through compression 
Compressibility.
Language recognition through compression. Huffman codes  Complexity and frequency. Zipf’s law. "Google" distance  Meaning distance. 
Chapter 3. 
Algorithmic information applied to mathematics

Incomputability of C.
Algorithmic probability  Algorithmic Information. Randomness. Gödel’s theorem revisited. 
Chapter 4. 
Machine Learning and Algorithmic Information

Induction  Minimum Description Length (MDL).
Analogy as complexity minimization. Machine Learning and compression. 
Chapter 5. 
Subjective information and simplicity

Cognitive complexity.
Simplicity and coincidences. Subjective probability & subjective information. Relevance. 
All contributions that pass will be grouped together into a document made accessible to all.