Core Courses

This course synthesises and elucidates material covered in the other core courses of the MA. It also introduces the areas covered in the electives. It proceeds by considering concrete contemporary scenarios involving AI. It focuses on how machine learning systems have been successfully deployed in many contexts, from gaming to triage, and on the issues – epistemic, practical, social, and ethical – that these raise in the present and near future. In the written assignments, students will be free to address and improve their knowledge concerning their personal AI-related interests. The students will also gain some ‘hands on’ experience using AI systems, such as ChatGPT and OpenArt.

Artificial intelligence is possible because we can automate reasoning. While the use of mechanical devices to solve problems goes back to the abacus, the 20th and 21st centuries have seen the development of extremely powerful methods for automating reasoning.

How can something like reasoning be done by a computer? And why do programmers use particular ways to automate it? This course provides an introduction to the automation of reasoning. It also gives students the tools from logic that they will need to understand other courses in the MAAIF.

This course provides an introduction to the ethics of AI for students of the MAAIF. After introducing the main ethics theories, as well as ethically relevant features of AI systems, this course discusses a series of practical ethical issues that are or will be caused by the widespread use of artificial intelligence in human society. The topics discussed range from issues that are already relevant today to other that may be exacerbated by future technologies like superintelligent AI systems. The course also discusses issues of AI governance and the legal regulation of AI.

We often think of artificial intelligence by analogy with human intelligence. Moreover, some of the most impressive machine learning systems, such as deep neural networks (DNNs), are modelled on the brain. But when, and to what extent, should we take such analogies seriously? Consider a chess-playing DNN, such as AlphaZero, for example. Does this generate representations of positions on chess boards, akin to mental representations?

And does it apply concepts such as piece mobility and space advantage, as human chess masters do, in deciding which moves to make? This course draws on the philosophy of mind and psychology to tackle such questions.

This capstone project enables students to deploy conceptual frameworks and research methodologies learned throughout the MAAIF, to explore in-depth relevant topics of personal interest, and to develop skills of critical research, analysis, presentation, and academic writing. The capstone research is conducted through individual or group (max 2 persons) work and direct supervision, with consultation meetings between students and advisors arranged roughly once a month. Students will work with a supervisor whose expertise includes topics relevant to their project and who will provide guidance and academic support with developing, researching, and writing the MAAIF capstone paper. Students choosing to work in pairs will be required to write a final paper of 7000-8000 words. Those working individually will be required to write a final paper of 5000-6000 words. Footnotes and quotations from primary and secondary sources in the main text are included in the word count, but appendices, tables, image captions, and the bibliography are excluded from the final word tally. The capstone project may focus primarily on themes and concepts from within the fields of AI and its impact.

• For full-time students, capstone project planning and supervision normally occurs in Term 2. Part-time students may plan and consult with supervisors in the second half of Year 1 and proceed to research and writing across Year 2.