Abstract:

A job candidate has been pre-selected for shortlist by a neural net; an autonomous car has suddenly changed lanes almost causing an accident; the intelligent fridge has ordered an extra pint of milk. From the life changing or life threatening to day-to-day living, decisions are made by computer systems on our behalf. If something goes wrong, or even when the decision appears correct, we may need to ask the question, “why?” In the case of failures we need to know whether it is the result of a bug in the software,; a need for more data, sensors or training; or simply one of those things: a decision correct in the context, that happened to turn out badly. Even if the decision appears acceptable, we may wish to understand it for our own curiosity, peace of mind, or for legal compliance. In this talk I will pick up threads of research dating back to early work in the 1990s on gender and ethnic bias in black-box machine-learning systems, as well as more recent developments such as deep learning and concerns such as those that gave rise to the EPSRC human-like computing programme. In particular I will present nascent work on an AIX Toolkit (AI explainability): a structured collection of techniques designed to help developers of intelligent systems create more comprehensible representations of the reasoning. Crucial to the AIX Toolkit is the understanding that human-human explanations are rarely utterly precise or reproducible, but they are sufficient to inspire confidence and trust in a collaborative endeavour.

Speaker Bio:

Alan Dix is Director of the Computational Foundry at Swansea University. Previously he has spent 10 years in a mix of academic and commercial roles, most recently as Professor in the HCI Centre at the University of Birmingham and Senior Researcher at Talis. He has worked in human–computer interaction research since the mid 1980s, and is the author of one of the major international textbooks on HCI as well as of over 450 research publications from formal methods to design creativity, including some of the earliest papers in the HCI literature on topics such as privacy, mobile interaction, and gender and ethnic bias in intelligent algorithms. Issues of space and time in user interaction have been a long term interest, from his “Myth of the Infinitely Fast Machine” in 1987, to his co-authored book, TouchIT, on physicality in a digital age, due to be published in 2018. Alan organises a twice-yearly workshop, Tiree Tech Wave, on the small Scottish island where he has lived for 10 years, and where he has been engaged in a number of community research projects relating to heritage, communications, energy use and open data. In 2013, he walked the complete periphery of Wales, over a thousand miles. This was a personal journey, but also a research expedition, exploring the technology needs of the walker and the people along the way. The data from this including 19,000 images, about 150,000 words of geo-tagged text, and many giga-bytes of bio-data is available in the public domain as an ‘open science’ resource. Alan’s new role at the Computational Foundry has brought him back to his homeland. The Computational Foundry is a 30 million pound initiative to boost computational research in Wales with a strong focus on creating social and economic benefit. Digital technology is at a bifurcation point when it could simply reinforce existing structures of industry, government and health, or could allow us to radically reimagine and transform society. The Foundry is built on the belief that addressing human needs and human values requires and inspires the deepest forms of fundamental science.

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Update: The slides from the talk are available here.

Full Title

Visualization and the Universe: How and why astronomers, doctors, and you need to work together to understand the world around us.

Abstract:

Astronomy has long been a field reliant on visualization. First, it was literal visualization—looking at the Sky. Today, though, astronomers are faced with the daunting task of understanding gigantic digital images from across the electromagnetic spectrum and contextualizing them with hugely complex physics simulations, in order to make more sense of our Universe. In this talk, I will explain how new approaches to simultaneously exploring and explaining vast data sets allow astronomers—and other scientists—to make sense of what the data have to say, and to communicate what they learn to each other, and to the public. In particular, I will talk about the evolution of the multi-dimensional linked-view data visualization environment known as glue (glueviz.org) and the Universe Information System called WorldWide Telescope (worldwidetelescope.org). I will explain how glue is being used in medical and geographic information sciences, and I will discuss its future potential to expand into all fields where diverse, but related, multi-dimensional data sets can be profitably analyzed together. Toward the aim of bringing the insights to be discussed to a broader audience, I will also introduce the new “10 Questions to Ask When Creating a Visualization” website, 10QViz.org.

Speaker Bio:

Alyssa Goodman is the Robert Wheeler Willson Professor of Applied Astronomy at Harvard University, and a Research Associate of the Smithsonian Institution. Goodman’s research and teaching interests span astronomy, data visualization, and online systems for research and education. Goodman received her undergraduate degree in Physics from MIT in 1984 and a Ph.D. in Physics from Harvard in 1989. Goodman was awarded the Newton Lacy Pierce Prize from the American Astronomical Society in 1997, became full professor at Harvard in 1999, was named a Fellow of the American Association for the Advancement of Science in 2009, and chosen as Scientist of the Year by the Harvard Foundation in 2015. Goodman has served as Chair of the Astronomy Section of the American Association for the Advancement of Science and on the National Academy’s Board on Research Data and Information, and she currently serves on the both the IAU and AAS Working Groups on Astroinformatics and Astrostatistics. Goodman’s personal research presently focuses primarily on new ways to visualize and analyze the tremendous data volumes created by large and/or diverse astronomical surveys, and on improving our understanding of the structure of the Milky Way Galaxy. She is working closely with colleagues at the American Astronomical Society, helping to expand the use of the WorldWide Telescope program, in both research and in education.

Abstract:

The goals of building software in a professional environment are vastly different from those of a course assignment. In this talk, we’ll cover the differences between the environments, best practices during development and tips from years of experience with troubleshooting production issues.

Speaker Bio:

Becky Plummer is the software engineering team leader responsible for content collaboration applications for the Bloomberg Terminal and the Global Head of the Engineering Champions Program. Becky made a name for herself as a software engineer by creating the trade confirmation alerting system that was fully crash recoverable for the Bloomberg Fixed Income Electronic Trading platform. She created the Engineering Champions program in 2011 to empower developers to influence change and collaborate on improving the development environment tools. Finally, she has run both small scale implementation projects as well as cross engineering projects including hundreds of developers. She is a graduate of University of Maine and Columbia University with a Master’s degree in Computer Science. Joined Bloomberg LP in New York in 2006 and moved to London in 2014 to gain a global perspective.

More information at this link.

Abstract:

Moore’s Law has been the main driver behind the extraordinary success
of computer systems. However, with the technology roadmap showing a
decline in transistor scaling and hence the demise of Moore’s law,
computer systems will be increasingly specialised and diverse. The
consistent ISA contract is beginning to break down. As it stands,
software will simply not fit. Current compiler technology, whose role
is to map software to the underlying hardware is incapable of doing
this. This looming crisis requires a fundamental rethink of how we
design, program and use heterogeneous systems. This talk proposes a
new way of tackling heterogeneity so that, rather than deny and fear
the end of Moore’s law, we embrace and exploit it.

Speaker Bio:

Michael O’Boyle is a Professor of computer science at the University
of Edinburgh. He is best known for his work in incorporating machine
learning into compilation and parallelization, automating the design
and construction of optimizing technology. He has published over 100
papers and received three best paper awards. He was presented with
the ACM CGO Test of Time award in 2017. He is a founding member of
HiPEAC, the Director of the ARM Research Centre of Excellence at
Edinburgh and Director of the EPSRC Centre for Doctoral Training in
Pervasive Parallelism. He is a senior EPSRC Research Fellow and a
Fellow of the BCS.

Abstract:

Lurch is an open-source word processor that can check the steps in students’ mathematical proofs. Users write in a natural language, but mark portions of a document as meaningful, so the software can distinguish content for human readers from content it should analyze.

This talk begins with an overview of the most recent release of the system, the ways in which it impacts students’ learning of mathematical proofs, and how it needs to be improved in the future. I will then cover how we are making those improvements in the next version, which will lead naturally to an introduction of the Lurch Web Platform, a foundational set of tools that we will use to bring the project to the web.

That platform is available on GitHub for other mathematical software developers to use in their own projects. It includes a web editor with mathematical typesetting, an interface for marking up documents with mathematical (or other structured) meaning, OpenMath support, meaning visualization tools, and document dependence and sharing features, among others.

Speaker Bio:

Nathan Carter uses computer science to advance mathematics. He writes open source mathematics software for university mathematics education, in areas including mathematical logic and abstract algebra visualization. He is a past winner of the Mathematical Association of America’s Henry L. Alder Award for Distinguished Teaching by a Beginning College or University Mathematics Faculty Member and his first book, Visual Group Theory, won the 2012 Beckenbach Book Prize from that same society. His second book, Introduction to the Mathematics of Computer Graphics, was published in 2016. His current book project will be an edited volume entitled Data Science for Mathematicians, intended to help mathematics faculty make the transition into teaching and doing research in the fast-growing field of data science.