Teaching

This seminar examines philosophical questions regarding the nature, measurement, and attribution of intelligence across humans, non-human animals, and artificial systems. Co-taught with Professor Carlotta Pavese.

This course examines what minds are and how they fit into the natural world. We evaluate the main theoretical frameworks for understanding mental phenomena by analysing canonical arguments and contemporary debates.

This special subject provides an overview of the most pressing ethical and philosophical challenges raised by the rapid development of artificial intelligence and digital technologies. From algorithmic bias and surveillance capitalism to the prospect of superintelligence, this course will equip students with the conceptual tools to critically analyse the societal impact of AI.

This unit explores major philosophical and scientific traditions concerning consciousness and the self. It introduces fundamental questions about the nature of consciousness: What can scientific inquiry reveal about consciousness? How can a physical system like the brain generate conscious experience? In what ways is consciousness shaped by social and cultural contexts? The unit also examines philosophical and scientific perspectives on the self, addressing questions such as: Does the self truly exist as a discrete entity? Is the self constructed through narrative? What is the relationship between self and others?

Artificial Intelligence has made impressive progress in recent years. Nowhere has this progress been as impressive as in the field of Natural Language Processing (NLP), concerned with building algorithms that can parse and generate text in natural language. A new family of NLP algorithms, called Large Language Models (LLMs), exhibit a remarkable ability to generate fluent text. Paragraphs generated with LLMs are grammatically well-formed, topically relevant, and stylistically coherent – so much so that they can often fool human readers. This technological development raises fascinating questions at the crossroads between philosophy, computer science, and linguistics. Can we say that LLMs really understand language? Do they have any degree of semantic competence? Or are they simply manipulating text strings without encoding their meanings? What does it even mean to understand language in the first place? These are some of the questions we will discuss in this seminar, by reading philosophical work in conjunction with cutting-edge research from computer science and computational linguistics.

While references to Artificial Intelligence (AI) are pervasive in popular culture and in the technology industry, the phrase is rarely defined. What is AI? Is a thermostat an AI system? What about digital assistants like Siri and Alexa? Or DeepBlue, Watson, and AlphaGo – the first computer programs to beat humans at the games of chess, Jeopardy!, and go, respectively? Does AI even exist today? If not, will it ever exist, or is it an impossible project? Raising these deceptively simple questions takes us into deep philosophical territory. Despite some AI researchers’ professed disinterest for philosophy, the project of building AI is in fact intimately related to philosophical questions, so much so that it is often difficult to disentangle strictly “technical” issues from philosophical ones.

This course will explore various questions at the intersection between philosophy and AI. We will start by tracing the deep philosophical roots of the AI project. This historical overview will lead us to ask how we should define AI in the first place, which in turn raises interesting questions about the nature of intelligence in biological organisms and artificial systems. After distinguishing several notions of AI, we will critically examine influential arguments against the possibility of “Strong AI” – the project of building artificial persons that have the same mental capacities as humans.

We will also discuss concrete approaches to building AI: logic-based approaches, that rely on explicit rules and symbolic representations, and connectionism, that rely on artificial neural networks inspired by the brain. Taking a closer look at several active areas of AI research, we will explore their connections to philosophy: computer vision (philosophy of perception) and natural language processing (philosophy of mind and language).

Finally, we will explore ethical and aesthetic implications of AI research. The hypothetical prospect of creating superhuman artificial intelligence has prompted concerns about the “existential risk” that AI research might pose for the future of humanity. While some philosophers take these concerns very seriously, others dismiss them as little more than science-fiction. The latter emphasize that we should focus on more pressing ethical concerns regarding current and near-term uses of AI. A first set of applied issues regards the deployment of physical autonomous systems that will kill humans, either accidentally (e.g., autonomous vehicles) or by design (e.g., autonomous weapons). A second set of issues regards fairness and bias in AI systems used to curate data (e.g., recommendation engines, or social media feed curation), or assist decisions that have a significant impact of people’s lives (e.g., predicting repeat offenses, creditworthiness, or grades). The production of artworks made with algorithms also raises fascinating questions related to the philosophy of art.

Questions about the nature, role and scientific explanation of consciousness are central to Philosophy of Mind and are raised more indirectly in the Philosophy of Cognitive Science. This lecture series aims to give undergraduate students an overview of central issues in the philosophical and scientific study of consciousness.

In particular, the aim of this lecture series is to help students think about critical questions regarding what consciousness is, its place in the natural world, how it relates to physical processes, and how it can be scientifically investigated.