Course code TPT37
Course title From complexity to Intelligence
Institution TELECOM ParisTech
Course address Télécom ParisTech 46, rue Barrault 13e arr.
City Paris
Minimum year of study 4th year
Minimum level of English Good
Minimum level of French None
Key words

Complexity, simplicity, artificial intelligence, cognition

Language english
Professor responsible Jean-Louis Dessalles
Telephone
Fax
Email dessalles@telecom-paristech.fr
Participating professors

JL Dessalles and Pierre Alexandre Murena

Number of places Minimum: 12, Maximum: 30, Reserved for local students: 15
Objectives

The mathematical notion of complexity has been invented 50 years ago to solve issues related to machine learning, randomness and proof theory. Complexity corresponds to the size of algorithms (and not to their speed; see caveat below). Complex objects cannot be described by short algorithms. The notion led to the development of Algorithmic Information Theory (AIT). Complexity and AIT have more recently been shown essential to address aspects of human intelligence, such as perception, relevance, decision making and emotional intensity. These aspects of cognition were sometimes considered mysterious and unpredictable. They can be regarded now as resulting in part from computations based on complexity and its converse, simplicity. For instance, abnormally simple situations such as a coincidence (two colleagues having dressed in purple independently) or a remarkable lottery draw (e.g. 1-2-3-4-5-6) are systematically perceived as unexpected and interesting. The design of intelligent systems must take advantage of this sensitivity of the human mind to complexity and to simplicity. [Caveat: This course does not address the notion of “computational complexity” which measures the speed of algorithms.]

Programme to be followed

This course begins with an introduction to the mathematical notion of complexity (also known as Kolmogorov complexity). The notion will be shown to be useful for the study of reasoning, for the definition of relevance (interestingness, unexpectedness), and for machine learning. We will also explore applications to the study of perception (hidden shapes, pattern recognition), of decision making (subjective probability), of responsibility and of emotional intensity. All these aspects will be studied using concrete examples. Half of the time will be devoted to personal work in lab sessions.

Prerequisites Ability to follow mathematical reasoning. Mastery of object-oriented programming. Elementary knowledge of the Python programming language is recommended.
Course exam

Students will also be asked to make a small original contribution and to present it orally. They will also have to answer a short quiz on the last day.

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