Roberto
Amadini

Since the first mechanical automations of the 17th century, one of the great dreams of the humanity has been the construction of machines exhibiting human behaviors and intelligence. Artificial intelligence (AI) is a discipline whose goal is to realize this dream by using the most different techniques, from symbolic computation based on logic to sub-symbolic models inspired by the structure of the brain, such as neural networks.

The last few years have witnessed a sensational exploit of AI applications in many different fields – with a relevant growth of investments – inducing many experts to believe that the dream will come true in a short time.

This is inducing many companies to reconsider their business strategies and, more generally, requires a deep rethinking of several crucial aspects of our society.

The course will provide an introductory overview of the various existing AI techniques, focusing on their industrial applications, discussing future opportunities and challenges, touching also on some social, economic and ethic implications.

Data Science and Business Analytics
Artificial Intelligence and Innovation Management
Finance and Fintech

Managers and Domain Experts face quite often the challenge of taking decisions on complex matters, characterized by a highly complex scenario, and huge quantity of data and/or knowledge and/or analytical models. Decision Support Systems (DSS) are a class of tools that help the users to understand pros and cons of possible alternative decisions, thus augmenting the awareness of the decision maker about the decision process and its impact.

A first part of the course is devoted to tools and solutions for representing the knowledge and the decision process adopted by Domain Experts. Decision criteria and rules can be documented in a formal, still human-readable way and, more important, can be used to feed software (such as Rule Engines) to fully automatize part of, or whole decision processes. Recent standards like, e.g., the Decision Model and Notation (DMN), will be introduced during a hands-on session, where the audience will directly experiment the advantages of documenting and automatizing decision processes by means of rule-based approaches.

A second part of this course is devoted to introduce the audience to DSS that exploit data and machine learning techniques to learn models of complex domains. These models are in turn used to predict and simulate the scenarios, so as to compare alternative decisions and their expected effects. Visualization techniques then help the decision maker to confront different decisions, w.r.t. optimal solutions for a given objective, or multi-objective decision criteria.

Artificial Intelligence and Innovation Management