COMSM0152 - Foundations of ProAI

Unit Information

This unit introduces students to a range of methodologies and applications across the field of AI. Each week will focus on a different theme, with three activities:

Required reading -- background reading to introduce the topic of the week
Guest seminars (usually Mondays, 1300-1400, Queen's 1.58) -- a guest speaker will present an introduction to their research area, connected to the week's topic
Group discussion (Fridays, 1200-1300, Queen's 1.69) -- student led discussion on the required reading and guest seminars. This is a chance to go over the important concepts and raise questions. The sessions will be chaired by a student, to facilitate the discussion (they are not required to present the topic). Link to spreadsheet with session chairs

This is a new unit, and you are a small cohort, so please do give us feedback at any time on how we can make the unit work better! In particular, if you find some topics assume prior knowledge you don't have, or would like to go further into certain topics, let us know. Please see below for some great all-round textbooks on AI, which may help you fill in some knowledge gaps... We also have a Team on MS Teams. Please do use it to ask us questions, discuss AI topics you are interested in, post interesting blogs, videos or papers, etc...

Staff

James Cussens (JC) Edwin Simpson (ES)

Unit Materials

TB1

TB2

Week	Reading (to complete before Monday's lecture)	Guest Seminar	Discussion chair (Link to spreadsheet with session chairs)	Lecturer responsible
1 (w/c 16/09/24)	Data and labels for image and Video understanding: ImageNet, the competition that kick-started the deep learning revolution in Computer Vision Kitti, a highly impactful dataset in vision and robotics. Optional extra: The winner of the ImageNet 2015 competition, ResNet.	Dima Damen -- tutorial on data and labels for video understanding	Jono	ES
2 (w/c 23/09/24)	Text embeddings and sequence processing: Deep contextualized word representations, Peters et al., 2018 Vector Semantics and Embeddings (intro to Section 6.4; 6.8 to end) RNNs and LSTMs (intro to Section 8.5)	Edwin Simpson	Moses	ES
3 (w/c 30/09/24)	Attention and Transformers. Both of the following are really good introductions to attention and transformers - you can pick one to read, or go through both if that helps your understanding. Please cover one of these before Monday: The Illustrated Transformer Jurafsky and Martin Chapter 9, "The Transformer" For the Friday reading group, please also read: Learning a Text-Video Embedding from Incomplete and Heterogeneous Data (and OPTIONALLY, for the keen) TOKEN MERGING: YOUR VIT BUT FASTER	Mike Wray	James	ES
4 (w/c 07/10/24)	Large language models: Jurafsky and Martin, Chapter 10 Jurafsky and Martin, Chapter 12, sections up to and inluding 12.4 Assessing the ability of LSTMs to learn syntax-specific dependencies, Linzen et al. (2016), TACL Beyond the limitations of any imaginable mecahnism: large language models and Psycholinguistics, Houghton, Kazanina and Sukumaran, Behavioral and Brain Sciences, (2023).	Conor Houghton -- linguistic perspectives on LLMs	David	ES
5 (w/c 14/10/24)	Philosophy of AI: An Analysis of the Interaction Between Intelligent Software Agents and Human Users Christopher Burr, Nello Cristianini and James Ladyman Language Models as Models of Language Raphaël Millière No Such Thing as a General Learner: Language models and their dual optimization Emmanuel Chemla and Ryan M. Nefdt	James Ladyman	Berenika	JC
6 (w/c 21/10/24)	CONSOLIDATION WEEK	(no seminar/reading group)
7 (w/c 28/10/24)	Knowledge representation and reasoning: R. Davis, H. Shrobe, and P. Szolovits. What is a Knowledge Representation? AI Magazine, 14(1):17-33, 1993 (16pp. + references) Delgrande, J. P., Glimm, B., Meyer, T., Truszczynski, M., and Wolter, F. (2023). Current and future challenges in knowledge representation and reasoning. arXiv preprint arXiv:2308.04161 (30pp. + appendices and an extensive bibliography) Additional reading: What are Non-classical Logics and Why Do We Need Them? An Extended Interview with Dov Gabbay and Leon van der Torre	Peter Flach	Moses	JC
8 (w/c 04/11/24)	Bayesian inference and decision making: An intuitive explanation of Bayes theorem (blog post) An introduction to variational autoencoders (Sections 1 and 2)	Laurence Aitchison (slides)	Jack	JC
9 (w/c 11/11/24)	Causality: Chapter 1 of Causal Inference in Statistics: A Primer by Pearl, Glymour and Jewell. You can skip Section 1.3 if you're already familiar with the material it contains. The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology by Krieger and Davey-Smith Comments on: The tale wagged by the DAG by Pearl Reply to Pearl: Algorithm of the truth vs real-world science by Krieger and Davey-Smith Supplementary reading: Schölkopf et al, Towards Causal Representation Learning	James Cussens	David	JC
10 (w/c 18/11/24)	Bias, fairness and transparency: European High-level expert group on AI, Ethics Guidelines for Trustworthy AI, read sections 4 and 5 of the assessment list on pp.28-30. Rebcca Burns, Artificial Intelligence is driving discrimination in the housing market, read up to the section "Not an excuse for lawbreaking behaviour." AI Fairness 360: read the short Credit Scoring tutorial via this link Miranda's slides	Miranda Mowbray	Jake	JC
11 (w/c 25/11/24)	Ethical and regulatory frameworks of AI: Charlesworth, A. Regulating Algorithmic Assemblages: Looking Beyond Corporatist AI Ethics, in Kohl, U. and Eisler, J. (eds.) (2022) Data-Driven Personalisation and the Law. Cambridge University Press. Charlesworth A. AI Regulation in the UK in Raposo, V.L. (ed.) The European Artificial Intelligence Act, Springer (forthcoming 2025). Additional Reading: Guihot, M., Matthew, A. F., and Suzor, N. P., (2020) Nudging Robots: Innovative Solutions to Regulate Artificial Intelligence, Vanderbilt Journal of Entertainment and Technology Law 20, 385-456. Additional Reading: EU Artificial Intelligence Act (Regulation (EU) 2024/1689) Chapter II - Prohibited AI Practices, Article 5 ; Chapter III- High Risk AI Systems, Arts. 6-17; Chapter V - General-Purpose AI Models, Arts. 51-56; Chapter X - Codes of Conduct & Guidelines, Art.95.	Andrew Charlesworth	Jack	JC
12 (w/c 09/12/24)	Assessment period	TB1 essay due
13 (w/c 13/01/25)	Discrete and continuous optimisation: placeholder,	James Cussens	(student name TBC)	JC
14 (w/c 20/01/25)	Reinforcement learning: placeholder,	Taku Yamagata	(student name TBC)	ES
15 (w/c 27/01/25)	Robotics and physically embodied agents: placeholder,	Nathan Lepora	(student name TBC)	ES
16 (w/c 03/02/25)	Multi-agent systems: placeholder,	Nirav Ajmeri	(student name TBC)	JC
17 (w/c 10/02/25)	Weak supervision: placeholder,	Raul Santos-Rodriguez	(student name TBC)	ES
18 (w/c 17/02/24)	CONSOLIDATION WEEK	No seminar/reading group
19 (w/c 24/02/25)	Human-in-the-loop AI -- design and evaluation: placeholder,	Kenton O'Hara	(student name TBC)	JC
20 (w/c 03/03/25)	Explainable and interpretable AI: placeholder,	Weiru Liu	(student name TBC)	ES
21 (w/c 10/03/25)	Privacy: placeholder,	Miranda Mowbray	(student name TBC)	JC
22 (w/c 17/03/25)	AIOps and deploying AI in production: placeholder,	TBC	(student name TBC)	ES
23 (w/c 24/03/25)	AI in healthcare and biomedicine: placeholder,	Zahraa Abdallah	(student name TBC)	ES
24 (w/c 16/09/24)	Assessement preparation week	TB2 essays due

Assessment Details

At the end of each Teaching Block students submit an essay of about 5,000 words (10 pages) on a research topic jointly chosen by them and their Academic Mentor. The essay should describe the background, state of the art, and open challenges with regard to the chosen topic. Each essay is assessed on a pass/fail basis in terms of scholarly content and academic writing. Narrative feedback is also provided, indicating strong points as well as areas for improvement. Passing the unit requires passing both essays. More guidance will be provided by the unit lecturers during the first term.

Text books

Bishop, C. M., Pattern recognition and machine learning (2006). This is one of the best ML textbooks and will provide a solid foundation across many aspects of ML. The book is freely available here.
Russell, S. and Norvig, P., Artificial Intelligence, A Modern Approach, 4th Edition (2020). The canonical introduction to AI, 3rd edition available at here.
Jurafsky, D. and Martin, J.H., Speech and Language Processing, 3rd edition drafts (2024). A great NLP textbook, but very readable and an interesting way to think about the challenges of designing AI systems in general. Also good for showing a contrast between deep learning and feature engineering approaches. Online only, here.
Murphy, K., Probabilistic Machine Learning: An Introduction (2022) and Murphy, K., Probabilistic Machine Learning: Advanced Topics (2023). A more recent ML textbook with particularly good coverage of probabilistic methods, freely available via here.

Some of you may prefer videos to books. Youtube has many video lectures, including the Machine Learning course from Stanford by Andrew Ng, which is very good.