Bringing machines closer to human competence is a fascinating challenge. We can hardly anticipate all the technical consequences that competent machines will have in domains such as human-machine interaction, intelligent search engines, machine translation, robotics, pattern recognition, knowledge mining and learning, adaptive planning or personal assistance.
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.
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.
- Enseignant responsable de l'UE: Fabian Suchanek