Artificial Intelligence: an Overview

The course will provide a non-technical overview of the artificial intelligence field. Initially, a discussion on the birth of AI is provided, remarking the seminal ideas and preliminary goals. Furthermore, the crucial weaknesses are presented and how these weaknesses have been circumvented. Then, the current state of AI is presented, in terms of goals, importance at national level, and strategies. Moreover, the taxonomy of the AI topics is presented.

This course deals with the problems created, aggravated or transformed by AI. It is intended to give students a chance to reflect on the ethical, social, and cultural impact of AI by focusing on the issues faced by and brought about by professionals in AI but also by citizens, institutions and societies. The course addresses these topics by means of case studies and examples analyzed in the light of the main ethical frameworks.

The purpose of the course is to help students understand the legal implications related to the design and use of artificial intelligence systems, providing an overview of the risks and legal protections that can be envisaged and giving an overview of the legislation and legal principles currently applicable on the subject. In particular, the profiles of civil and criminal liability, protection in terms of intellectual property and the impacts of AI on the fundamental rights of the individual - including privacy and the right to non-discrimination – will be examined.

This course will address the hardware technologies for machine and deep learning (from the units of an Internet-of-Things system to a large-scale data centers) and will explore the families of machine and deep learning platforms (libraries and frameworks) for the design and development of smart applications and systems.

The course provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the course briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These concepts will be explained through examples and case studies.