The course aims at introducing students to relevant emerging technologies for sustainability, providing a basic but in-depth conceptual understanding of Blockchain and decentralised ledgers, Machine Learning and Artificial Intelligence, Network Science with applications especially to Smart Cities and Territorial Networks, highlighting its potential applications in the social, economic fields and business sectors, with particular attention to the renewed sustainability challenges. Students are encouraged to apply theoretical notions in practical sessions in order to solve empirical problems through a hands-on approach with intuitive visual tools. They are also encouraged to envisage new projects involving these technologies and try to solve small real world cases of interest.

The only prerequisite is a genuine curiosity in understanding emergent technologies and to be passionate about learning the implications that these technologies have over society.

This course is intended to transmit sophisticated technological concepts and their implications for society in a straightforward, motivating and easy-to-understand way, avoiding at the same time unnecessary technicalities and extreme simplifications. First module The field of Machine Learning and Artificial Intelligence have a history dating back to the 1950s and in the first week of the course, after a brief historical introduction, all the basic concepts of the methodology will be introduced, which differs greatly from traditional algorithm design. In the following weeks the four main methodological categories will be explored: regression, classification, clustering and dimensionality reduction. Then we will explore a highly topical subject, Network Science, the currently most powerful paradigm for understanding the complex dynamics of interactions in social and economic phenomena. Networks are also a fundamental starting point to understand the shaping of Smart Cities supported by the IoT technologies. Subsequently we will lay down the theoretical foundations of Artificial Neural Networks that we will deepen in the next one talking about Deep Learning, the latest application frontier of AI in solving the most challenging problems, with performance similar to the capabilities of human thought. The last weeks will be dedicated to the introduction of the novel paradigm of Generative AI, with the explanation of the basic concepts and usage of the Large Language Models (like ChatGPT, Gemini and the likes) and the visual generation of images and videos through the so called Diffusion Models. Second Module The second module is structured in two complementary parts. The first three weeks are devoted to an in-depth exploration of the blockchain universe. Blockchain was officially invented in 2009 by Satoshi Nakamoto (a nom de plume whose real identity has always been a mystery), but its origins date back to the 80's and 90's, when a group of people started to imagine how cryptography could allow exchange of information and creation of trust among individuals in a decentralised and private way. Blockchain is more than meets the eye, and cryptocurrencies are only the tip of the iceberg: we will delve into the concepts of competition and cooperation, of decentralisation and consensus, exploring examples from society and from nature to understand how players of a game can be incentivised to cooperate for a common good without the need of a central authority. We will learn what it means to mine a block and what is a smart contract, we will talk about Bitcoin, Ethereum, tokens and NFTs, as well as the integration of blockchain with IoT and Industry 4.0. The following three weeks shift the perspective towards corporate sustainability and ESG (Environmental, Social, Governance) as a strategic and data-driven lever for value creation, moving beyond a pure compliance logic. Students will learn how data architectures, information systems and digital platforms enable the effective management of ESG performance, supporting investment decisions, innovation and risk management. Within the broader framework of the Twin Transition (digital and ecological), the role of blockchain as an enabler of traceability, transparency and anti-greenwashing will be examined as a natural bridge between the two parts of the module. The module concludes with an Action Learning Lab on the ESG Strategy Canvas, where students work in groups to design data-driven sustainability initiatives, and with a final discussion on the future of emerging technologies and their societal role.

First module "On Intelligence" by Jeff Hawkins – Macmillan (book) "Superintelligence" by Nick Bostrom – Oxford (book) "Artificial Intelligence" by Alessandro Vitale – Egea (book in Italian) "Computing Machinery and Intelligence" by Alan Turing – Mind (paper: https://www.csee.umbc.edu/courses/471/papers/turing.pdf) Second module – Blockchain "Blockchain: Blueprint for a New Economy" by Melanie Swan (book) "The Tragedy of the Commons" by Garrett Hardin (paper) "The Evolution of Cooperation" by Robert Axelrod (book, optional) Second module – ESG and Data-Driven Sustainability "Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist" by Kate Raworth (book) GRI Standards – Global Reporting Initiative (https://www.globalreporting.org/standards/) EU Corporate Sustainability Reporting Directive (CSRD) and ESRS framework – overview materials from the European Commission (https://finance.ec.europa.eu/capital-markets-union-and-financial-markets/company-reporting-and-auditing/company-reporting/corporate-sustainability-reporting_en) Saberi, S. et al., "Blockchain technology and its relationships to sustainable supply chain management" – International Journal of Production Research (paper, optional) Class slides, datasets and simulated ESG dashboards provided during practical sessions.

On site lectures live exercises with students Case studies with the direct involvement of students Problem based learning Peer education

The student's knowledge will be assessed through three components, each contributing equally to the final grade: One final project-oriented group assignment on a topic that will possibly include elements of both modules and which will be proposed by the groups and has to be accepted by the teachers. Two personal assignments, one for each module of the course. For each assignment, students are required to submit a written mini-paper (approximately 1.000 words) on a specific topic assigned by the teachers; for the second module, the topic may be drawn from either the blockchain or the ESG / data-driven sustainability part, according to the student's interest and the teacher's assignment. The written submission is a mandatory prerequisite for the evaluation, but the actual grading is based on an in-itinere oral discussion held during the course, in which the student presents and defends the content of the mini-paper in dialogue with the teacher. The written paper serves as the basis and reference for the oral discussion, ensuring that students engage in depth with the subject while developing the ability to communicate their ideas effectively and respond to critical questioning. The aim of the project-oriented group assignment is to give students a way to test their capability in understanding the added value that emerging technologies can bring to a specific project. Ideally, students should be able to point out why the projects they are working on for their assignment are only feasible with the aid of the proposed technology, and how sustainability and ESG considerations are integrated in the design. The teachers will also value the capability of students to bring their own knowledge into the project, e.g. exploring the legal, economic or environmental aspects deriving from the implementation of a given technology within the project. The final project-oriented group assignment will count for 2/3 of the final grade. The aim of the personal assignments is to assess the capability of the student to explore in deeper detail a specific subject, starting from the knowledge received from the course. The form of a short mini-paper (approximately 1000 words) followed by an in-itinere oral discussion is chosen to stress both the importance of expressing concepts in a concise and understandable way (quality over quantity) and the ability to argue, defend and contextualise one's analysis in a live academic dialogue. Each personal assignment counts for 1/6 of the final grade, for a total of 1/3 (1/6 per module). Finally, at the end of each week there will be an exchange of views between the students and the teachers on the topics discussed during the week, in the form of a class debate. Students will be asked to elaborate a very short informal assignment in written or oral form, either before the class or immediately before the debate. These oral or written elaborations will be shared with the class as a primer for the debate. These intermediate assignments and the following debate will not be evaluated, as they are intended as a moment in which students can learn together via trial and error, through direct confrontation with the class and sharing of ideas.

For the two personal assignments, the topics will be assigned by the teacher. For the group assignment, the groups will be randomly formed by the teachers and the subjects will be decided by the groups. The teacher can accept or modify the subject of the group depending on its relevance to the topics treated throughout the course.