The first day of the conference comprises a range of workshops, to be held on Tuesday 13th December. Delegates will find these events to be especially valuable where there is a current need to consider the introduction of new AI technologies into their own organisations.
There will be four half-day workshops, and delegates are free to choose any combination of sessions to attend. The programme of workshops is shown below. Note that the first session starts at 11 a.m. to reduce the need for delegates to stay in Cambridge on the previous night.
There is a lunch break from 12.30-13.15 and there are refreshment breaks from 14.45-15.15 and from 16.45-17.00.
Workshops organiser: Professor Adrian Hopgood, University of Portsmouth, UK
Sessions 1 and 2 - Stream 1 (11.00-12.30 and 13.15-14.45 Lubbock Room)
AI Trends in Healthcare - Download slides (opens in new tab)
Professor Jeremy Wyatt, University of Southampton
- Prof. Adrian Hopgood (University of Portsmouth): "AI for predicting patient outcomes"
- Dr Chay Paterson (University of Manchester): "Machine learning for genomics"
- Dr Mercedes Arguello Casteleiro and Niamh Joyce (University of Southampton): "Reasoning with logics and embeddings for healthcare"
- Prof. Frans Coenen (University of Liverpool): "AI for pathology data prioritisation"
- Dr Chris Wroe (The British Medical Journal): "Health data structures and the HL7-FHIR standard for healthcare data exchange"
- Prof. Jeremy Wyatt (University of Southampton): "Challenges of mobilising Computable Biomedical Knowledge"
Sessions 1 and 2 - Stream 2 (11.00-12.30 and 13.15-14.45 Peterhouse Lecture Theatre)
Sustainability & AI
Dr. Mathias Kern, BT Applied Research
In this workshop, we will take a closer look at the sustainability impact of AI and ML systems but also at how AI can be used to help to reduce the environmental impact of technology in domains such as telecommunications.
- Dr Peter Garraghan (Lancaster University): “Sustainable AI systems”
- Dr Loïc Lannelongue (Cambridge University): “The environmental impact of machine learning: how
bad is it and what can we do about it?”
- Louise Krug (BT Applied Research): “How BT is using AI to achieve sustainability challenges”
- Prof Maziar Nekovee (University of Sussex): “AI for net-zero 6G”
Sessions 3 and 4 - Stream 1 (15.15-16.45 and 17.00-18.30 Lubbock Room)
Introduction to AI - Download slides (opens in new tab)
Professor Adrian Hopgood, University of Portsmouth, and Professor Lars Nolle, Jade University of Applied Sciences (Germany)
This workshop is an updated version of a similar one presented in 2019. It will be useful for newcomers to AI and also for specialists who wish to broaden their AI understanding. The workshop will introduce a wide range of AI tools including neural networks, rules, case-based reasoning, Bayesian updating, fuzzy logic, and genetic algorithms. It will also cover multiagent systems that combine different approaches. Practical applications will be highlighted.
- Part one (15.15-16.45)
- AI introduction
- Knowledge-based systems
- Machine learning
- Part two (17.00-18.30)
- Genetic algorithms for optimisation
- Multiagent systems
- Practical applications
Sessions 3 and 4 - Stream 2 (15.15-16.45 and 17.00-18.30 Peterhouse Lecture Theatre)
Dr Mercedes Arguello Casteleiro, University of Southampton, Dr Anne Liret, BT, and Dr Christoph Tholen, DFKI: German Research Center for Artificial Intelligence
Explainable AI (XAI) aims to enhance machine learning (ML) techniques with the aim of producing more explainable ML models that would enable human users to understand and appropriately trust ML models.
Part 1: Explainability hands-on in deep learning - Dr Mercedes Arguello Casteleiro, University of Southampton
Deep Learning algorithms are considered black box algorithms, where a close examination by humans does not reveal the features used to generate the prediction. This part of the workshop will focus on explainable AI for Deep Learning algorithms in domains with abundant unlabelled text, such as biomedicine. The workshop will exemplify how to provide predictions (outcome) with accompanying justifications (outcome explanation). The approach presented belongs to the new field of explainable active learning (XAL), combining active learning (AL) and local explanations.
- ECS Team (University of Southampton) working together with Perro - view ECS Team video (opens in new tab)
- Diego Maseda Fernandez (NHS England)
- Chris Wroe (BMJ)
- Manoj Kulshrestha (Mid Cheshire Hospital Foundation Trust)
- Alexandru Cotici, Peter Woolfson, and Joshi George (Manchester Centre for Clinical Neurosciences)
- Maria Jesus Fernandez-Prieto (University of Salford), Saihong Li (University of Stirling), and Nava Maroto (Universidad Politecnica de Madrid)
Part 2: Reusing Explanation experience - Dr Anne Liret, BT - view iSee demo (video opens in new tab)
Even with the growing list of explanation libraries that are published, real-world decision-makers still face the challenge of designing the right questions and measuring instruments to prove that the evaluation fits for purpose and brings benefits to the end-user. iSee (isee4xai.com) is an interactive toolbox for XAI with Case-based Reasoning at heart which focuses on explanation experience evaluation and reusing across different use cases. This part of the workshop will look at why it is important to model the end-to-end experience of user, will showcase explanation methods, and exemplify the importance of validation according to the intent of users and human perception, and present real examples.
- Anne Liret (BT Applied Research): “Evaluating and reusing explanation experience across use cases”
- David Corsar (Robert Gordon University): “Modelling explanation strategies and experiences with iSeeOnto”
- iSee team: “Demo of the iSee cockpit: Reusing explanation strategy in action”
- Matthew Wallwork (BT Technology): “Connected Care - supporting people in their homes”
- Mahsa Abazari Kia (University of Essex) and Aygul Garifullina (BT Technology): “Using NLP to understand complex technical notes - a telecoms case study”
Part 3: Application cases - Dr Christoph Tholen, Mattis Wolf, and Dr Frederic Stahl, DFKI: German Research Center for Artificial Intelligence - Download slides (opens in new tab)
This part of the workshop will focus on XAI applications in the maritime domain. Here, on one hand, safety concerns prevent the use of deep learning techniques for many applications. Nevertheless, XAI techniques have the potential to enable, for instance, control systems for autonomous ships. Another example is the use of convolutional neural networks (CNNs) for plastic waste identification and classification. In this use case, the acceptance of potential end users depends on the confidence of the human stakeholder in AI systems used. Here XAI methods, like result explanations, can help to increase the acceptance of the end users. In this workshop, both use cases and other possible applications of XAI in the maritime domain will be discussed.