Checking date: 08/06/2022


Course: 2022/2023

Natural and Artificial Intelligence
(19013)
Study: Bachelor in Science, Technology and Humanities (374)


Coordinating teacher: GENOVA FUSTER, GONZALO

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Objectives
- To understand the classical concept of biologically based human intelligence. - To understand the technological concept of artificial intelligence based on the processing of information in a computational machine. - To understand the concept of computability introduced by Alan Turing, the basis of all computer science. - To understand the concept of a program stored in a computer as a set of instructions to execute an algorithm. - To understand the difference between a machine with a fixed program and a self-programming machine. - To understand the concept of technological singularity, and the limits faced from the computational paradigm. - To understand precisely the similarities and differences between natural intelligence and artificial intelligence. BASIC COMPETENCES Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study. SPECIFIC COMPETENCES Explain human cognition and intelligence on the basis of the construction of symbolic languages and systems. TRANSVERSAL COMPETENCES Display a capacity for organisation and planning and, at the same time, for adapting to new problems or situations. Work collaboratively in teams.
Skills and learning outcomes
Link to document

Description of contents: programme
1. The classical conception of intelligence. Intelligence, rationality and self-consciousness. Theoretical reason, productive reason, practical reason. 2. The sciences of the artificial. Machines and artifacts. Structure and purpose of a machine. 3. Intelligence understood as the capacity to solve problems. What problems can be solved. Computability. 4. Computational machines as a substrate of artificial intelligence. Turing and Von Neumann. 5. The paradigm shift: explicit programming vs. machine learning. Problem solving. Emulation of human behavior. 6. The future and limits of artificial intelligence. The technological singularity. Machines ethics: freedom and responsibility. 7. The way back: natural intelligence understood in the light of artificial intelligence.
Learning activities and methodology
TRAINING ACTIVITIES Theoretical classes Theoretical-practical classes Tutorials Group work Individual student work TEACHING METHODOLOGIES Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. Critical reading of texts recommended by the subject professor: Press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate knowledge of the subject. Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group. Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the subject, as well as practical case studies. Developing pieces of work and reports, individually or in group.
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • Dreyfus, H.L. . What Computers Can't Do: The Limits of Artificial Intelligence. New York: Harper and Row. 1972
  • Gelernter, D. . The Tides of Mind: Uncovering the Spectrum of Consciousness. . New York: Liveright. 2016
  • Tallis, R. . Why the Mind Is Not a Computer: A Pocket Lexicon of Neuromythology. Exeter: Imprint Academic. 2004
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.