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Natural and Artificial Intelligence

Code: 106229 ECTS Credits: 6
Degree Type Year Semester
2504235 Science, Technology and Humanities OB 2 1


Francesc Xavier Roque Rodriguez

Teaching groups languages

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External teachers

Gonzalo Génova Fuster


There are none.

Objectives and Contextualisation

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.


  • Display a capacity for organisation and planning and, at the same time, for adapting to new problems or situations.
  • Explain human cognition and intelligence on the basis of the construction of symbolic languages and systems.
  • Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
  • Work collaboratively in teams.

Learning Outcomes

  1. Gain familiarity with the different programmes of naturalistic study of the mind and their functioning.
  2. Identify and evaluate the importance of the human factor in the development and use of symbolic systems.
  3. Identify formally correct and incorrect arguments by translating natural language utterances to formal language, and applying first-order logic to make demonstrations and deductions.
  4. Integrate elements from different areas of knowledge to analyse a situation and suggest actions or solutions.
  5. Make an informed judgement on the social and ethical challenges posed by artificial intelligence.
  6. Produce creative papers and personal projects in the corresponding area of study.
  7. Programme simple algorithms and appreciate the logic of their functioning.
  8. Promote team spirit and the integration of others' points of view.
  9. Understand the concepts of numbering system, algorithm and computability, and appreciate their historical and practical importance.
  10. Understand the notion of computability, and the concept of programme stored on a computer, as a set of instructions for executing an algorithm, and identify the difference between a machine with a fixed programme and a self-programmable machine.


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.


Theoretical classes.
Theoretical-practical classes.
Group work.
Individual student work.

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Title Hours ECTS Learning Outcomes
Type: Directed      
Lectures 33 1.32 9, 10, 1, 3, 2, 5
Practical-theoretical lectures 16 0.64 6, 1, 8, 4, 7, 5
Type: Supervised      
Essay supervision 4.25 0.17 9, 10, 6, 1, 3, 2, 4, 7, 5
Type: Autonomous      
Group work 32.5 1.3 9, 10, 6, 1, 8, 3, 2, 4, 7, 5
Individual student work 62.25 2.49 9, 6, 1, 3, 2, 4, 7, 5


Final exam.
Classroom participation.
Individual or group essays.

In the event of a student committing any irregularity that may lead to a significant variation in the grade awarded to an assessment activity, the student will be given a zero for this activity, regardless of any disciplinary process that may take place. In the event of several irregularities in assessment activities of the same subject, the student will be given a zero as the final grade for this subject.

Single assessment
Students who opt for the single assessment system will have to submit an essay (50%) and take an exam (50%), on the indicated date.

Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Classroom participation 20% 0 0 9, 10, 1, 3, 2, 7, 5
Final exam 30% 2 0.08 9, 10, 1, 3, 2, 7, 5
Group and individual essays 50% 0 0 9, 10, 6, 1, 8, 3, 2, 4, 7, 5


Basic References
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.

Basic Electronic Resources
Reaktor, Universidad de Helsinki. Elementos de IA. Curso online gratuito: https://www.elementsofai.com/es/


No specific software is required.