The notion of complexity has been invented 50 years ago to solve mathematical issues related to machine learning, randomness and proof theory. It 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 now be regarded 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. When crediting or blaming a person for an action (e.g. giving the wrong medicine to an allergic child), one considers the simplicity of the causal link leading to the consequences. One also considers the person’s ability to measure that simplicity. A dramatic event is perceived as more emotional if the victims can be defined simply (celebrities, friends’ friends), if the place is simple (famous location or close to one’s home) or if the circumstances are causally complex (e.g. the victim was unlikely to be there). The design of intelligent systems must take advantage of this sensitivity of the human mind to complexity and simplicity.
- This course does not address the notion of "computational complexity" which measures the speed of algorithms.
- This course is not about Complex Systems either (for this, see TPT-09: Emergence in complex systems).
Format des notesNumérique sur 20Littérale/grade européen
Pour les étudiants du diplôme IA : Intelligence ArtificielleL'UE est acquise si Note finale >= 10
- Crédits ECTS acquis : 2 ECTS
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