EMERGING TECHNOLOGIES: AI, MACHINE LEARNING, BLOCKCHAIN, IOT, 5G, QUANTUM & EDGE COMPUTING
EMERGING TECHNOLOGIES: AI, MACHINE LEARNING, BLOCKCHAIN, IOT, 5G, QUANTUM & EDGE COMPUTING
Tania Di Mascio, Alessandro Chessa
Instructional goals
This course is designed to provide students with an introductory yet thorough understanding of vital emerging technologies. Throughout the semester, we will explore key concepts related to Blockchain and decentralized ledger technologies, Machine Learning and Artificial Intelligence, as well as Network Science as it applies, for example, to IoT and 5G. A special focus will be placed on applications within Smart Cities and Territorial Networks.
Students will engage with the technologies' wide-ranging potential impacts across social, economic, and legal dimensions. We will specifically address how these technologies intersect with contemporary challenges in sustainability.
The curriculum aims to equip students with practical skills for presenting innovative ideas and projects in a digital format. This includes learning to craft engaging landing pages using high-level tech solutions for the Web. Emphasis is placed on applying theoretical knowledge through practical, hands-on sessions. These sessions will help students address empirical problems using intuitive visual tools. Moreover, students will be encouraged to conceptualize and initiate projects that leverage these emerging technologies, focusing on addressing real-world challenges.
Intended learning outcomes
Knowledge and understanding: Students will acquire a foundational understanding of various emerging technologies, alongside a comprehension of their societal and legal impacts. They will also grasp key analytical and interpretative paradigms relevant to these technologies.
Ability to apply knowledge: Students will learn to effectively analyze emerging technologies using the introduced tools, synthesizing insights to delineate their societal impacts.
Autonomy of judgment: Students will be trained to distill objective insights from the complex interactions of these technologies, forming a solid foundation that can be practically applied to real-world projects.
Communication skills: Students will develop the ability to visually present analysis results using road graphics and engage audiences with concise public pitches. They will also gain proficiency in using web platforms to articulate new ideas and create impactful landing pages with advanced technological solutions.
Learning skills: Students will develop the ability to independently and creatively navigate the realm of emerging technologies. They will gain an understanding of these technologies' structures and the vast possibilities they offer, learning to harness their potential to transform society.
Course Contents
This course aims to demystify complex technological concepts and their societal implications in an engaging and accessible manner. We strive for clarity, avoiding excessive technical jargon and oversimplification. Throughout the semester, key areas such as Blockchain, Machine Learning and Artificial Intelligence, and the roles of 5G and the Internet of Things (IoT) will be explored, with emphasis on their societal impact.
By the end of this twelve-week course, students will understand these technologies' foundations and practical value. They will learn to distinguish between genuine utility and hype.
First module
The first module introduces design thinking, agile methodology, and project design. Students will learn about one-page sites, multi-page sites, and landing pages, focusing on landing page structure and strategies. UX and UI principles and best practices for presenting projects on the Web will be covered. A practical introduction to WordPress will guide students in developing a landing page.
Second module
This module delves into Machine Learning and Artificial Intelligence, starting with a historical introduction and covering key methodologies such as regression, classification, clustering, and dimensionality reduction. Network Science and its role in Smart Cities will be explored. Theoretical foundations of Artificial Neural Networks and Deep Learning will be discussed, along with AI's societal impact, including Explainable AI, AI Crime, and ethical issues. Creative possibilities of machines will also be considered.
Third module
Topics in this module include block mining, smart contracts, and blockchain network security. Major cryptocurrencies like Bitcoin and Ethereum will be examined, along with other digital assets. The focus will be on blockchain's role in shaping the future, emphasizing its impact on finance, supply chain management, and digital identity, and highlighting both opportunities and challenges.
Fourth module
The fourth module is a project wrap-up phase with co-design sessions involving lecturers. Students will finalize their projects and integrate content into their landing pages, refining their ideas for effective presentation.
Reference Books
First module:
“The Design Thinking Playbook: Mindful Digital Transformation of Teams, Products, Services, Businesses and Ecosystems” by Michael Lewrick, Patrick Link, Larry Leifer – Wiley (book, optional)
“Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability” (3rd Edition) by Steve Krugs – Pearson Education (book, optional)
“How Users Read On The Web” (article: https://www.nngroup.com/articles/how-users-read-on-the-web/)
Second 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,)
Third module:
“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)
Teaching Methods
This course employs an inquiry-based learning model enriched by continuous assessment and a variety of interactive teaching methods. Students will participate in:
On-Site Lectures: Traditional classroom lectures that provide foundational knowledge and contextual insights into emerging technologies.
Live Exercises: Practical, in-class activities where students apply learned concepts in real-time, enhancing their understanding through direct application.
Case Studies: In-depth analysis of real-world scenarios involving emerging technologies, with students actively engaging in problem-solving and decision-making processes.
Problem-Based Learning (PBL): This approach challenges students to learn through the structured exploration of complex, real-world problems, fostering critical thinking and solution-oriented skills.
Peer Education: Students will also benefit from collaborative learning experiences where they can teach and learn from their peers, promoting a deeper understanding of content through shared knowledge and diverse perspectives.
This multi-faceted approach ensures that students not only absorb theoretical knowledge but also develop practical skills through active participation and peer interaction, preparing them to tackle real-world challenges effectively.
Assessment Method
The skills will be assessed through a written exam, intermediate assessments and on a group project.
Written Exam The written exam, accounting for one-third of the final grade, will feature a combination of multiple-choice and open-ended questions. This exam will test students' ability to expand on specific topics using the knowledge acquired throughout the course.
Intermediate Assessments These assessments will occur regularly and involve a dynamic exchange of opinions in the form of class debates, accounting for another third of the final grade. Prior to each session, students will prepare a brief, informal response—either written or oral—to the module's topics. This preparation serves as both an introduction to the debate and a method for fostering a collaborative learning environment through trial and error and idea sharing.
Group Project The final component, making up the remaining third of the grade, is a group project. Students will select topics related to course modules, which must be approved by teachers. Groups will present their projects through a landing page developed during the course. This project assesses students' understanding of how emerging technologies can add value to real-world applications. Presentations should articulate why the technologies used are essential for the project's success. Additionally, students' ability to integrate their knowledge of legal or economic aspects into their projects will also be evaluated.
Thesis assignment criteria
For the group assignments, teachers will form teams by strategically considering the diverse competencies of each student, aligning with Design Thinking methodologies. This ensures a balanced mix of skills within each group, enhancing the potential for innovative solutions and comprehensive learning.
The topics for the group projects will initially be proposed by the students and then finalized in consultation with the teachers. Teachers reserve the right to accept, reject, or modify the proposed topics to ensure they are sufficiently relevant and aligned with the core content and objectives covered in the course.
Week 1
Overview of the course
Design Thinking principles.
Responsive Web Design.
Designing for the Web: one-page website, multi-page website, landing page.
Q&A session.
Week 2
How to structure an effective landing page.
Introduction to UX (user experience) and UI (user interface) principles.
Best practices for presenting projects on the Web.
Q&A session.
Week 3
Introduction to WordPress and its functionalities.
Development of a landing page, hands on lesson on creating a landing page with WordPress.
Q&A session.
Week 4
AI, Machine Learning, Deep Learning: an historical introduction
Supervised, Unsupervised learning
Training, validate and testing
Regularization: overfitting and underfitting
Hyperparameter tuning
Performance metrics
Feature engineering
Week 5
Linear and Logistic Regression
Classification
Clustering
Dimensionality Reduction
Week 6
Network Science and Complexity
Centrality Measures, Motifs, Community Detection
Recommender systems
Urban/Territorial Networks, IoT and Smart Cities
Knowledge Graph, Semantic Web and Ontologies
Applications in sustainability and SDGs
Week 7
Artificial Neural Networks (ANN)
Neuroscientific motivations and ANN Layouts
Constructing and Training Neural Networks
Optimization processes
Week 8
Deep Learning
CNN, RNN, LSTM
Reinforcement Learning
Chatbots
GPT3, Transfer Learning
AI and Society
AI Ethics
XAI: Explainable AI
Deep Fake and AI Crime
Deep Dreams and Creative Machines
Week 9
The birth of blockchain: bitgold, hashcash, the double spending problem and the concept of proof of work
Hashing, blocks, mining, minting
The Bitcoin blockchain and the Bitcoin
Q&A session
The emergence of Cooperation
Decentralisation and Consensus
The example of ants as a natural decentralised system
The Byzantine attack
Distributed ledgers
Permissioned VS permissionless
blockchains
Q&A session
Week 10
Ethereum
Smart Contracts
The Ethereum Virtual Machine and the Gas
Decentralised Applications (Dapps)
Soft and Hard Forks
Decentralised Autonomous Organisations (DAO)
The DAO attack
The “Blockchain is Law” motto and its implications
Q&A session
Alternatives to the Proof of Work: Proof of Stake, Proof of Space, Proof of Elapsed Time
Sustainability and ecological impact: the issues of the proof of work protocol
Week 11
Cryptocurrencies
Is Bitcoin just a bubble?
Privacy-by-design and zero-knowledge proof
Q&A session
ICOs and Whitepapers
Practical applications: an overview of some blockchain-based projects
Blockchain and emergent Technologies: 5G, IoT, Machine Learning and AI
Q&A session
The tragedy of the Commons
Tokens and NFts: overview and practical applications
The future of blockchain: is blockchain here to stay?
How could blockchain change the world in the future?
Blockchain and society
Blockchain and ethics
Week 12
Project Wrap-up