Series

SACHI Seminar: Rights-driven Development

Abstract:

Alex will discuss a critique of modern software engineering and outline how it systematically produces systems that have negative social consequences. To help counter this trend, he offers the notion of rights-driven development, which puts the concept of a right at the heart of software engineering practices. Alex’s first step to develop rights-driven practices is to introduce a language for rights in software engineering. He provides an overview of the elements such a language must contain and outlines some ideas for developing a domain-specific language that can be integrated with modern software engineering approaches. 

Bio:

Alex Voss, who’s an Honorary Lecturer here at the school and an external member of our group. Alex was also a Technology Fellow at the Carr Center for Human Rights Policy at Harvard’s John F. Kennedy School of Government and an Associate in the Department of Philosophy at Harvard.

Alex holds a PhD in Informatics and works at the intersection of the social sciences and computer science. His current research aims to develop new representations, practices and tools for rights-respecting software engineering. He is also working on the role that theories of causation have in making sense of complex socio-technical systems.

His research interests include: causality in computing, specifically in big data and machine learning applications; human-centric co-realization of technologies; responsible innovation; computing and society; computer-based and computer-aided research methods.

More about Alex: https://research-portal.st-andrews.ac.uk/en/persons/alexander-voss

Event details:

  • When: 28th February 2024 12:30 – 13:30
  • Where: Jack Cole 1.19

If you’re interested in attending any of the seminars in room 1.19, please email the SACHI seminar coordinator: aaa8@st-andrews.ac.uk so they can make appropriate arrangements for the seminar based on the number of attendees.

Distinguished Lecture Series: The Atomic Human: Understanding Ourselves in the Age of AI

  • Tuesday 12 March
  • Booth Lecture Theatre, Medical Sciences Building.

We look forward to welcoming Prof Neil Lawrence, Cambridge who will talk about ‘The Atomic Human: Understanding Ourselves in the Age of AI’.

A vital perspective is missing from the discussions we are having about Artificial Intelligence: what does it mean for our identity?

Our fascination with AI stems from the perceived uniqueness of human intelligence. We believe it is what differentiates us. Fears of AI not only concern how it invades our digital lives but also the implied threat of an intelligence that displaces us from our position at the centre of the world.

Atomism, proposed by Democritus, suggested it was impossible to continue dividing matter down into ever smaller components: eventually, we reach a point where a cut cannot be made (the Greek for uncuttable is ‘atom’). In the same way, by slicing away at the facets of human intelligence that can be replaced by machines, AI uncovers what is left: an indivisible core that is the essence of humanity.

By contrasting our own (evolved, locked-in, embodied) intelligence with the capabilities of machine intelligence through history, The
Atomic Human reveals the technical origins, capabilities, and limitations of AI systems, and how they should be wielded. Not just
by the experts, but by ordinary people. Either AI is a tool for us, or we become a tool of AI. Understanding this will enable us to choose
the future we want.

This talk is based on Neil’s forthcoming book to be published with Allen Lane in June 2024. Machine learning solutions, in particular
those based on deep learning methods, form an underpinning of the the current revolution in “artificial intelligence” that has dominated
popular press headlines and is having a significant influence on the wider tech agenda.

In this talk, I will give an overview of where we are now with machine learning solutions, and what challenges we face both in the
near and far future. These include practical application of existing algorithms in the face of the need to explain decision-making,
mechanisms for improving the quality and availability of data, dealing with large unstructured datasets.

Distinguished Lecture Series: Computer Science and the Environment

Thank you to Professor Gordon Blair for delivering this year’s distinguished lecture on Computer Science and the environment.

The series of talks explained the role of computer science in addressing the massive challenges associated with a changing climate.

Feedback was positive and the series was enjoyed by all!

From Left to Right: Jonathan Lewis, Blesson Varghese, Simon Dobson, Gordon Blair, Ian Miguel & Al Dearle (Back)

SICSA DVF Seminar – Dr André G. Pereira

We had our first School seminar of the semester today. The speaker was André G. Pereira visiting Scotland on a SICSA DVF Fellowship. André is working on AI Planning problems, an area that is closely related to the work of our own Constraint Programming research group.

Title: Understanding Neuro-Symbolic Planning

Abstract: In this seminar, we present the area of neuro-symbolic planning, introducing fundamental concepts and applications. We focus on presenting recent research on the problem of learning heuristic functions with machine learning techniques. We discuss the distinctions and particularities between the “model-based” and “model-free” approaches, and the different methods to address the problem. Then, we focus on explaining the behavior of “model-free” approaches. We discuss the generation of the training set, and present sampling algorithms and techniques to improve the quality of the training set. We also discuss how the distribution of samples over the state space of a task, together with the quality of its estimators, are directly related to the quality of the learned heuristic function. Finally, we empirically detail which factors have the greatest impact on the quality of the learned heuristic function.

Biography: Dr. André G. Pereira is a professor at the Federal University of Rio Grande do Sul, Brazil. His research aims to develop and explain the behavior of intelligent systems for sequential decision-making problems. Dr. Pereira has authored several papers on top-tier venues such as IJCAI, AAAI, and ICAPS. These papers contribute towards explaining the behavior of heuristic search algorithms, how to use combinatorial optimization-based reasoning to solve planning tasks, and how to use machine learning techniques to produce heuristic functions. Dr. Pereira is a program committee member of IJCAI and AAAI. His doctoral dissertation was awarded second place in the national Doctoral Dissertation Contest on Computer Science (2017), and first place in the national Doctoral Dissertation Contest on Artificial Intelligence (2018). Dr. Pereira advised three awarded students on national events, including first place and finalist in the Scientific Initiation Work Contest (2018, 2022), and finalist in the Master Dissertation Contest on Artificial Intelligence (2020).

Systems Research Group seminars

The Systems Research Group is re-starting their seminars series from 6th May 2022. Seminars will take place every two weeks at 1pm, on Fridays. From May to July the seminars will be online (SRG Teams), while from September onward we aim to move them to a hybrid format. More information on the schedule can be found on the seminars page of the Systems Research Group site.

Welcoming Prof. Giovanna Di Marzo Serugendo for our DLS on Tuesday 9 November

As part of the schools Distinguished Lecture Series we look forward to welcoming Prof. Giovanna Di Marzo Serugendo on Tuesday 9 November.

Prof. Giovanna Di Marzo Serugendo  received her Ph.D. in Software Engineering from the Swiss Federal Institute of Technology in Lausanne (EPFL) in 1999. After spending two years at CERN (the European Center for Nuclear Research) and 5 years in the UK as Lecturer, she became full professor at the University of Geneva in 2010. Since 2016, she is the Director of the Computer Science Center of the University of Geneva, Switzerland. She has been nominated in 2018 among the 100 digital shapers in Switzerland. Her research interests relate to the engineering of decentralised software with self-organising and emergent behaviour. This involves studying natural systems, designing and developing artificial collective systems, and verifying reliability and trustworthiness of those systems. Giovanna co-founded the IEEE International Conference on Self-Adaptive and Self-Organising Systems (SASO) and the ACM Transactions on Autonomous Adaptive Systems (TAAS), for which she served as EiC from 2005 to 2011.

This event will be held on Teams with further details to follow.

Seminar – Richard Connor – 5th November

The second school seminar on 5th November at 2pm, on Teams.  If you do not have the Teams link available please contact the organiser, Ian Gent.

Dimensionality Reduction in non-Euclidean Spaces
Richard Connor
Deep Learning (ie Convolutional Neural Networks) gives astoundingly good classification over many domains, notably images. Less well known, but perhaps more exciting, are similarity models that can be applied to their inner layers, where there lurk data representations that can give a much more generic notion of similarity. The problem is that these data representations are huge, and so searching a very large space for similar objects is inherently intractable.
If we treat the data as high-dimensional vectors in Euclidean space, then a wealth of approximation techniques is available, most notably dimensionality reduction which can give much smaller forms of the data within acceptable error bounds. However, this data is not inherently a Euclidean space, and there are better ways of measuring similarity using more sophisticated metrics.
The problem now is that existing dimensionality reduction techniques perform analysis over the coordinate space to achieve the size reduction. The more sophisticated metrics give only relative distances and are not amenable to analysis of the coordinates. In this talk, we show a novel technique which uses only the distances among whole objects to achieve a mapping into a low dimensional Euclidean space. As well as being applicable to non-Euclidean metrics, its performance over Euclidean spaces themselves is also interesting.
This is work in progress; anyone interested is more than welcome to collaborate!

Georgios Gerasimou (University of St Andrews): Frontiers in computational revealed preference analysis

RESCHEDULED: please note the changed date and a non-standard time!

Abstract: Prest is a recently published piece of open-source software for computational revealed preference analysis that provides novel ways to estimate decision makers’ preferences over choice alternatives by analysing their observable choice behaviour. This software is informed by classic as well as recent developments in economic revealed preference theory. Some of the recent developments take the form of models that are computationally complex. This complexity currently hinders the inclusion of these models in the Prest toolkit. The presentation will first aim to describe the primary ideas underpinning Prest and illustrate them with examples from its existing toolkit. It will then proceed with a discussion of some of the challenges pertaining to the expansion of that toolkit with more models and operations. The presentation will be self-contained and no prior background in economics will be necessary.

Speaker Bio: Georgios is a Reader in Economics at the University of St Andrews, working mainly on decision theory and revealed preference analysis. In the latter research programme, Georgios’ work aims to improve our understanding of people’s decision processes and preferences through theoretical, experimental/empirical as well as computational methods. Georgios co-developed the Prest software program for computational revealed preference analysis (https://prestsoftware.com/).

Event details

  • When: 17th February 2020 14:00 - 15:00
  • Where: Cole 1.33b
  • Series: School Seminar Series
  • Format: Seminar