ARTIFICIAL INTELLIGENCE
- Overview
- Assessment methods
- Learning objectives
- Contents
- Delivery method
- Teaching methods
- Contacts/Info
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To test learning, the student may choose one of the following modes:
1.Preparation and presentation of an in-depth study.
The topic of the in-depth study must be agreed in advance with the lecturer. The student must prepare a presentation, with the support of slides, lasting about twenty minutes and highlighting the connections with the topics covered in the course. During the presentation, the lecturer will ask some specific questions (at least 4) to check the acquisition and understanding of the topics covered in the course.
2.Discussion of the course topics.
The student should present in detail one of the topics presented in the course of his/her choice. The lecturer will also ask some specific questions (at least 4) on the other topics covered in the course.
The final grade will be determined as follows: knowledge of the subject matter and specific terminology (40%); ability, during the exposition, to synthesize and analyze (20%); ability to independently formulate an adequately reasoned critical judgment (20%); mastery of expression and language (20%).
The course addresses the different types of artificial intelligence developed in the contemporary age to achieve increasingly sophisticated and engaging human-machine interaction results. In particular, the role that computers play in the development of our society will be highlighted, and the main theoretical and technical limitations to the realization of a machine that truly simulates human intelligence and mind will be discussed. The objective will be achieved by presenting, in an informal but adequate manner, the scientific basis necessary to ensure a proper understanding of the phenomena related to the application of computer science in its various aspects. In particular, the historical development, from the ancient Greece of Euclid and Aristotle to the early twentieth century, of the notions of mathematical reasoning, algorithm, and computability will be presented. Following the work of Gödel, Turing, and Church between 1930 and 1936, it will result in the construction of the theoretical foundations of computer science and the identification of the main limiting results.
The fundamental characteristics of modern computers, from Von Neumann's architecture to its technological realization to programming languages, will be presented. The scientific field of Artificial Intelligence will then be presented, illustrating its objectives, techniques and results to reflect on what are the current limits of this discipline with respect to the simulation of human intelligence. Finally, emerging technologies, like machine learning, deep learning and big data, which are significantly influencing the development of many aspects of society, will be analyzed.
Upon completion of the course the student will be able to:
- understand and describe the fundamental aspects of the scientific and technological foundations of computer science having awareness of what are the limits of its application;
- understand the difference between the techniques and problems that fall within the scope of traditional computer science and those that fall within the scope of Artificial Intelligence;
- understand the scope and possible social impact of the interaction between artificial intelligence, data science and emerging technologies such as big data, machine learning and deep learning;
- critically analyze the societal issues related to the massive application of such technologies;
- communicate the technologies being taught in an accomplished manner and with the correct terminology.
- From the axiomatic method to the crisis of fundamentals.
The axiomatic method. From Euclidean geometry to non-Euclidean geometries. The crisis of fundamentals. Hilbert's program and the decision problem.
- The mathematical theory of reasoning.
From syllogism theory to formal logic. Mathematical logic. Gödel's incompleteness theorems. Artificial intelligence and Gödelian arguments.
- Turing machines and computability theory.
Turing machines and the notion of computation. The problem of termination.
- Computers and programming.
From logic gates to circuits. Hardware and software. Von Neumann's machine. From assembler language to high-level languages (compilers, interpreters and structured programming).
- Artificial intelligence: context, goals and methods. Chess and artificial intelligence. Strong and weak artificial intelligence. The Turing test. The problem of the symbolic foundation of AI.
- Emerging technologies.
Neural networks, machine learning and deep learning, big-data, data analysis and the role of artificial intelligence
48 hours of frontal lectures
The lecturer receives by appointment, upon request by e-mail sent to the institutional address. The lecturer responds only to signed e-mails sent from the students' institutional address (students.uninsubria.it).