Artificial Intelligence

Objectives of the course

- Know and understand the basics of artificial intelligence and their relationship with contemporary society and with communication and the media;

- Develop the ability to use the knowledge and understanding indicated in the previous point to use the tools made available by artificial intelligence, understand their limits and intuit future developments.

Course programme

What is artificial intelligence, how is it evolving and what impact does it have on our existence. The main content of the course, in line with the general objectives of the degree course and with the specific objectives of the thematic area, aims to provide the basic elements for understanding the discipline of Artificial Intelligence (AI) both from an scientific-technological and social-cultural.

The lessons will be organized as follows:

• history and present of AI,
• information theory,
• models of the mind, robotics,
• AI and society,
• AI and the imaginary,
• emotions and consciousness in artificial agents.

The course will be structured as follows:

• Information and computation (Shannon, Syntax, semantics and intentionality, Turing machines, Turing Tests, languages ​​for AI, Short history, present and future of AI, GOFAI)

• Technologies (neural networks, robotics, semantic web, autonomous agents, robotics, artificial consciousness, singularity, pattern recognition, machine learning, deep learning)

• Models (Anthropomorphic robotics, Sensory-motor coordination, Situated and embodied cognition)

• AI and society (Roboethics, Social Robotics, AI and law, AI and work, AI and sexuality)

• Media (AI and imaginary, from Prometheus to Pinocchio, from Chapek to Westworld, from Metropolis to The Matrix, HER)

• Mind (Emotions and consciousness in natural and artificial agents, the hard problem of consciousness, zombies, the Chinese room, artificial consciousness)

• Cases (Google Assistant, Apple Siri, Amazon Alexa, Microsoft Bots, Atlas, Honda Asimo, Sony Aibo, Aldebaran Nao, Geminoidi, Boston Dynamics Atlas, IIT Icub).

• Deep Learning, Big Data, Artificial General Intelligence

Didactic Methods

In line with the educational objectives of the course, teaching will be organized in lectures, critical discussions of the topics covered, discussion of cases.

The teaching will adopt both a theoretical and a practical approach, alternating between case studies and theoretical exposure. In each lesson, one or more key concepts will be addressed through the analysis of as many case studies in order to put the proposed concepts into practice.