ID:
509701
Duration (hours):
40
CFU:
6
SSD:
SOCIOLOGIA DEI PROCESSI CULTURALI E COMUNICATIVI
Year:
2025
Overview
Date/time interval
Primo Semestre (29/09/2025 - 12/12/2025)
Syllabus
Course Objectives
By the end of the course, students are expected to: a) understand the main stages in the history of the idea of problem computability in philosophy and mathematics, from Euclid to Alan Turing; b) comprehend how the numerical representation of the world and the concept of computability shape contemporary economic and social reality; c) correctly define the phenomenon of digitalization and the notions of big data, cloud computing, mathematical optimization, machine learning, and generative artificial intelligence; d) apply logic and techniques of prompt design in the use of selected generative AI platforms (e.g., ChatGPT, Midjourney).
Course Prerequisites
No specific IT skills are required for a proper understanding of the course content. Some familiarity with basic statistical concepts—such as variable, distribution, arithmetic mean, median, standard deviation, correlation, and inference—may be useful, though not essential. Students who feel the need are encouraged to review these concepts before or during the course, using one of the many manuals available in Italian or English (e.g., David S. Moore, Statistica di base, Milan: Apogeo Education, 2013).
Teaching Methods
The course includes: • lectures (with in-class learning assessment through Wooclap); • case study analysis; • guided practical exercises; • group project work. Lectures are supported by PowerPoint or PDF presentations, made available to students on the KIRO platform in a format compliant with accessibility standards for students with disabilities (structured headings, logical reading order, alternative text for images, self-descriptive links). Attendance, though not compulsory, is strongly recommended. In any case: • for the introductory and advanced sections, video recordings of each lecture are available on KIRO for students unable to attend in person; • for the laboratory section, students unable to attend in person are exempt from this part, which will not be included in their exam, as specified under Modalità di verifica dell'apprendimento.
Assessment Methods
The final exam consists of an individual oral test (approx. 15–20 minutes), which can be taken in Italian or English. Students may freely decide to take the exam as attending or non-attending students, communicating their choice at the beginning of the exam (no prior notice to the instructor is required). • Attending students: The exam evaluates the knowledge acquired regarding the topics presented and discussed during the lectures, including the laboratory section. Candidates will answer at least three questions, each covering a different topic selected by the instructor. The final grade (0–30 scale) is based on the depth of knowledge, understanding, and ability to integrate concepts acquired during the course, with equal weight assigned to each question. • Non-attending students: The exam focuses on one of the following monographs, chosen by the student (no prior notice to the instructor is required): • Alfio Quarteroni, L’intelligenza creata. L’AI e il nostro futuro, Milan: Hoepli, 2025. • Christopher Summerfield, These Strange New Minds. How AI Learned to Talk and What It Means, New York: Viking, 2025.
Texts
At the end of each lesson, a bibliography is provided relating to the topics covered. These are not compulsory readings to be tackled in preparation for the exam, but useful sources for further study.
Contents
The course is structured into three parts: an introductory section, an advanced section, and a laboratory section. • Introductory section (8 classes, 16 hours): provides the basic vocabulary required to understand the subject of the course, including: • the distinction between data, information, and knowledge; • the triad data–algorithm–solution; • the concept of databases and database management systems (DBMS); • elements in the history of artificial intelligence; • machine learning algorithms; • the difference between machine learning and deep learning; • large language models (LLMs) and generative AI: their functions (text/token classification, question answering, text and summary generation, translation, speech recognition, image classification); • transformers: how they work (attention mechanism and encoder–decoder architecture). • Advanced section (4 classes, 8 hours): examines the nature and historical roots of the phenomenon, highlighting the factors that explain its pervasiveness: the explosion of big data, the “datafication” of experience, and the diffusion of the principle of computability across major areas of economic and social life. Special focus is given to two domains—relevant to the field of Communication studies—where big data and applied AI are enabling significant transformations: • Information and Journalism • Marketing and Advertising • Laboratory section (8 sessions, 16 hours): consists of exercises in prompt engineering, i.e., structuring natural language commands for generative AI models in order to produce original and meaningful outputs (texts, images, audio, and video).
Course Language
Italian
Degrees
Degrees
COMMUNICATION, INNOVATION, MULTIMEDIA
Bachelor’s Degree
3 years
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People
People
Teaching staff
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