Elective courses

What sorts of problems can be solved with a computer? This course provides students with a clear understanding of the possibilities and limitations of computable problems through an examination of fundamental concepts and results in proof theory, computability theory, and model theory. Specific topics of coverage may include: basic set theory including diagonalization and uncountable sets; meta-logic of first-order logic, including soundness and completeness; effective computability, undecidability, and incompleteness; the Löwenheim-Skolem and compactness theorems; philosophical issues concerning the nature of physical computers; machine learning and formal learning theory.

As artificial intelligence becomes increasingly powerful and less restricted in scope, researchers face an increasing number of increasingly weightier risks in developing it. These include risks involving the loss of control of AI systems, the intentional misuse of AI systems, conflicts between the goals of AI systems and the goals of researchers, and relying on AI systems whose inner functioning is opaque. After an introduction to risk and related concepts, this course explores the key risks arising from AI development as well as strategies for preventing and mitigating these risks. More specifically, the course will cover issues like the following. What is a risk? How can you mitigate a risk? How does risk arise with respect to developing AI systems? Could AI systems become too advanced or powerful for their programmers to control? How can researchers ensure that the goals of AI systems “align” with human values? Can we trust the outputs of AI systems whose internal functioning is opaque? Are there risks of an AI “arms race?” Should regulation or policy be introduced to help mitigate these risks? Are the risks of catastrophe great enough to warrant giving up on AI development altogether? 

This course aims to provide students with a comprehensive exploration of contemporary science-fiction stories and films, coupled with a historical and theoretical understanding of these works. Our primary objective is to introduce students to major works in SF literature and visual arts, with a specific focus on themes related to AI and reality. Throughout the course, we will examine how the effects of AI can be interpreted in both an optimistic (utopian) and a pessimistic (dystopian) way. For instance, many works of science fiction have addressed the idea that AI may eventually perform most of the work that is currently done by human beings. Some science fiction authors have written stories in which this is a positive development because it improves humans’ quality of life, while others have written stories in which this is a negative development because humans become too reliant on AI. In general, authors have explored various effects that AI might have on humans and human society through both positive and negative perspectives.

In the coming decades, we will increasingly come to rely on AI in making public policy and business decisions. The same advantages that enable AI systems to beat humans in chess enable it to better predict the implications of governmental policies or beat human traders in the stock market. But while these AI technologies promise to improve our decision-making in policy and business, they also pose new and serious threats. In this course, we examine the potential risks and rewards of AI for policy and business. This will include studying current efforts to regulate AI systems, e.g. the recent EU proposed Artificial Intelligence Act, China’s 2023 Interim Measures for the Management of Generative Artificial Intelligence Services, and court cases covering the use of copyrighted material in AI training data sets.

From locating black holes in the night sky, to predicting the structure of proteins, to identifying potential causes of cancer, AI techniques are now widely applied in science and medicine. Organized around a set of detailed case studies, this course examines some of the most pressing questions raised by these applications, such as: How do AI techniques complement – or supplant – more traditional scientific or therapeutic methods? To what extent should we embrace automated scientific research? And what are the potential ethical implications of these applications for clinicians?

This course provides an introduction to topics in the field of speculative AI, that is, future developments of AI that are probable but not yet within the realm of the technologically possible. The course will first focus on the question of how we can try to estimate future technological and social development and then discuss a series of particular AI applications that will likely become relevant to humanity in the future; for example: superintelligence and the singularity, alternative futures, long-term AI risks, cyborgs, cyborg identity and human enhancement, post- and transhumanism, smart cities, AI and the environment, effects of AI on poverty, human freedom, dignity and social structures, and the future of work.