Dr Vidhyalakshmi Karthikeyan (Head of Data and Insights, YouView TV Limited)

Zero to ML

This talk is about the adoption of machine learning at YouView TV, the tech company behind the daily viewing experience of nearly 3 million consumers on BT & TalkTalk STBs and a number of Sony TVs. We will cover some of the key objectives and challenges in driving the adoption of ML in an organisation that is relatively early in its journey to unlock value from its data. We will progress to some use cases where we have successfully used ML within the organisation and outline our vision for these initiatives going forwards.

Vidhyalakshmi Karthikeyan leads the Data and Insight function at YouView TV. She is responsible for ensuring that YouView’s data is fit for purpose and creates value for a wide variety of stakeholders. This includes delivering timely and rigorous evidence from viewer behaviour data for better decision-making and viewer experiences. She started her career as a data scientist in 2009, did a PhD part-time and progressed to leading large customer experience-centric data programmes. She is an inventor on 24 patents and applications. She has won several national awards for her achievements and STEM community outreach activities. Vidhya is passionate about figuring out data solutions to business problems, working towards driving organisational culture change in data adoption across all parts of its lifecycle, from creation to exploitation.

Dr. Laura Weis (Future of Work Lead, Satalia)

AI-enabled Workforce Environments: empowering the new ‘net-work’

We are living through a fundamental transformation in the way we work, as we progressively move towards organisational models that are flatter, more fluid and collaborative, and increasingly dependent on knowledge assets. Work is increasingly becoming “net-work”, as the formal ‘command-and-control’ based hierarchies are replaced by more informal, dynamic networks that span across business functions and teams, incorporating a myriad of internal and external contributors. With this shift in structure comes a need to understand and intentionally lead it. This talk focuses on the critical role of advanced AI technology in the emergence and management of these interconnected workforce environments. While deep technical expertise is a must-have, success will also require a profound understanding of how to use this knowledge to drive increasingly complex business strategies. In this context, a people-centric approach has become increasingly important; an approach that continuously engages workers in this networked environment, fostering strategic and inclusive interaction and collaboration between people, people and work, and people and machines.

Laura Weis is passionate about designing people-centric workforce management strategies and technology, applying her vast experience in corporate profiling and advanced people analytics to support organisational decision-making. In her role as Head of Future of Work at Satalia, Laura brings together the intersection of work, workforce, workplace, and the employee experience. She received a PhD in Social & Organisation Psychology from UCL and is a recognised thought leader in organisational network analysis in the academic and corporate worlds.

Dr Detlef Nauck (Principal for AI & Data Science, BT)

What’s my AI doing?

Organisations are rushing to deploy AI solutions inspired by genuine business opportunities but sometimes also by hype or fear of missing out. Most of today’s AI solutions in organisations are new types of IT automation not driven by coded business logic but by statistical pattern recognition. This means we know that sometimes the AI will be wrong and that we need to mitigate risks in areas like bias/fairness, transparency, and accountability – to name a few. When organisations deploy vendor solutions instead of building them they face the additional challenge of finding out if the vendor follows some best practice guidelines for responsible AI that are compatible to their own. The advent of AI Regulation means that organisations not only need to convince themselves that their AI is under control, but that they will have to pass AI audits soon. In this talk I will take a look at the technical and organisational challenges we are facing when we are deploying and managing AI at scale.

Detlef Nauck is the Head of AI and Data Science Research for BT's Applied Research Division located at Adastral Park, Ipswich, UK. Detlef leads a programme spanning the work of 30+ international researchers who develop capabilities underpinning future AI systems. A key part of the work is to establish best practices in Data Science and Machine Learning leading to the deployment of responsible and auditable AI solutions that are driving real business value. Detlef is a computer scientist by training and holds a PhD and a Postdoctoral Degree (Habilitation) in Machine Learning and Data Analytics. He is a Visiting Professor at Bournemouth University and a Private Docent at the Otto-von-Guericke University of Magdeburg, Germany. He has published 3 books, over 120 papers, holds 15 patents and has 30 active patent applications.

Dr Sandy Brownlee (Senior Lecturer in Computing Science, Stirling University)

Sustainable Building Design through Evolutionary Algorithms and Optimisation

In recent years, evolutionary algorithms have increasingly been applied to the optimisation of real-world industrial problems. Optimisation of building designs is one such area: typical designs have large numbers of variables, including construction materials, dimensions and equipment specifications. All of these can affect construction cost, operational energy use and occupant comfort. Given that the lifespan of a typical building is measured in decades, the environmental impact of getting the design right is large. The goal of Evolutionary Multi-objective Optimisation (EMO) is to find a set of designs representing a trade-off between conflicting objectives such as cost vs energy efficiency. This trade-off can be used to support designers in decision-making. I will explore a few approaches to supporting decision-making aimed at revealing what drives the trade-offs between energy consumption, costs, and comfort, for typical building design optimisation problems at the small scale (individual building) and large scale (region-level housing stock).

Sandy Brownlee is a Senior Lecturer at the University of Stirling, where he leads the Data Science and Intelligent Systems Research Group. He is also a Visiting Fellow in Architecture, Building & Civil Engineering at Loughborough University. He holds a BSc and PhD in Computing Science, and worked as software engineer in industry, and in the Civil Service, before returning to academia in 2010. He has published more than 70 papers in international journals and conferences. His research interest focuses on "explainable" optimization: combining metaheuristics and exact methods with machine learning to both find optimal solutions and reveal insights into the problem, helping people make better decisions. He has applied these methods in transportation, civil engineering, software engineering, and healthcare.

Dr. José Miguel Rojas Siles (Sheffield University)

Search-based Automated Test Generation

Search algorithms are at the core of computer science, but searching for program inputs is not straightforward as the possible inputs for any real program is far too high to explicitly enumerate them all. Consequently, the search for effective tests needs to be informed by heuristics and needs to use algorithms that can cope with the complex structure and properties of programs under test and test data. Many different heuristics and algorithms have been proposed to address this problem. Search-based testing is a part of a larger domain in software engineering, where meta-heuristic search algorithms are used to solve complex software engineering problems. This talk presents a search-based approach to automatically generate executable unit tests for object-oriented programs and outlines current trends and challenges where modern AI can be applied in this context.

José Miguel Rojas Siles is a Lecturer in Software Testing at the Department of Computer Science. He received a PhD in Software and Systems from the Technical University of Madrid (Spain, 2013) and was a Research Associate at the Department of Computer Science at Sheffield (2014-2017) before joining the University of Leicester as a Lecturer in Software Engineering. His research work focuses on search-based automated test generation and its application in real-world software development scenarios. His interests include empirical software engineering, automated software testing, and software engineering education. His work has been published in the top venues of logic programming (ICLP), software engineering (ICSE and ASE), software testing (ISSTA and ICST) and search-based software engineering (SSBSE and GECCO) and has been awarded multiple distinguished paper awards."

Professor Edward Keedwell (Professor of Artificial Intelligence, Exeter University)

Augmented Evolutionary Intelligence: Using Humans and AI to Co-Design Solutions to Difficult Problems

The optimisation of the operation and design of systems is an important subfield of AI.  When coupled with accurate simulations and digital twins, these methods can support the decision making process and even innovate new methods and designs through automated search and optimisation algorithms.  However, these methods can lack an intuitive understanding of the system being optimised and including a human-in-the-loop has demonstrable benefits to the optimisation process.   This talk will describe our work on augmented evolutionary intelligence, a method that beneficially combines domain experts, machine learning and evolutionary algorithms to co-create solutions to optimisation problems, whilst minimising user fatigue.  The method has potential application to many domains and in this talk bin-packing and water distribution network optimisation will be presented as example case studies.

Edward Keedwell is Professor of Artificial Intelligence, and a Fellow of the Alan Turing Institute. He has research interests in optimisation (e.g. genetic algorithms, swarm intelligence, hyperheuristics) machine learning and AI-based simulation and their application to a variety of difficult problems in bioinformatics and engineering yielding over 160 journal and conference publications. He leads a research group focusing on applied artificial intelligence and has been involved with successful funding applications totalling over £3.5 million from the EPSRC, Innovate UK, EU and industry. Particular areas of current interest are the optimisation of transportation systems, the development of sequence-based hyperheuristics and human-in-the-loop optimisation methods for applications in engineering.