Descriptif
This course addresses the issue of A.I. as a reverse-engineering problem: try to mimic, not only the performance, but also the processes, of natural intelligence. For example, a text-messaging app reading “The meeting is scheduled for tomorrow.” anticipates future tense: “Will [you be there]?”. It does so through mere statistical association between “tomorrow” and future tense. Could a machine detect that the message is about a future event, and then not only deduce that future tense is appropriate, but also retrieve the reason for attending the meeting?
This course is best adapted to students who want to acquire more than skills in the domain of Artificial Intelligence.
Objectifs pédagogiques
The objective is not only technical. It is an occasion to grasp the complexity and power of human intelligence, while drawing a line between capacities that can be implemented and those that remain challenging to reproduce.
- Leçon : 14
- Travaux Pratiques : 7
Diplôme(s) concerné(s)
Pour les étudiants du diplôme Data & Knowledge (D-K)
Basic knowledge in Logic (propositional logic and predicate logic) and in logic programming.
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Data & Knowledge (D-K)
Vos modalités d'acquisition :
First session: 30% lab questions + 30% paper + 10% presentation + 30% quiz.
Second session: 50% first session + 50% oral examination
Answers to questions during lab sessions are recorded and read (30%). In addition, each student will choose a technical topic (typically a topic studied during lab sessions), perform a micro-research on that problem (typically go beyond the lab exercises) and write a 4-page paper (30%).
Students will briefly present their work on the last day (10%). Students will also answer a small quiz on the last day (no documents). Students who would fail to pass this first round will have to prove that they master the main concepts taught in the course during an oral interview. The eventual note will be the mean of the first grade and this oral evaluation.
- Crédits ECTS acquis : 2.5 ECTS
Le coefficient de l'UE est : 2.5
La note obtenue rentre dans le calcul de votre GPA.
Programme détaillé
● Symbolic machine learning
● Cognitive knowledge representation
● Introduction to Natural Language Processing (syntax, semantics, relevance)
● Reasoning, complexity, simplicity
● Emotions and computation