- When: 2nd April 2019 14:00 - 15:00
- Where: Cole 1.33a
- Series: School Seminar Series
- Format: Seminar
This is joint work with Charlie Blake.
The most famous single-player card game is ‘Klondike’, but our ignorance of its winnability percentage has been called “one of the embarrassments of applied mathematics”. Klondike is just one of many single-player card games, generically called ‘solitaire’ or ‘patience’ games, for which players have long wanted to know how likely a particular game is to be winnable for a random deal. A number of different games have been studied empirically in the academic literature and by non-academic enthusiasts.
Here we show that a single general purpose Artificial Intelligence program, called “Solvitaire”, can be used to determine the winnability percentage of approximately 30 different single-player card games with a 95% confidence interval of ± 0.1% or better. For example, we report the winnability of Klondike to within 0.10% (in the ‘thoughtful’ variant where the player knows the location of all cards). This is a 30-fold reduction in confidence interval, and almost all our results are either entirely new or represent significant improvements on previous knowledge.
Ian Gent is professor of Computer Science at the University of St Andrews. His mother taught him to play patience and herself showed endless patience when he “helped” her by taking complete control of the game. A program to play a patience game was one of the programs he wrote on his 1982 Sinclair Spectrum now on the wall outside his office.
This talk is an overview of the VAMPIRE (Vessel Assessment and Measurement Platform for Images of the REtina) project, an international and interdisciplinary research initiative created and led by the Universities of Dundee and Edinburgh in Scotland, UK, since the early 2000s. VAMPIRE research focuses on the eye as a source of biomarkers for systemic diseases (e.g. cardiovascular, diabetes, dementia) and cognitive decline, as well as on eye-specific diseases. VAMPIRE is highly interdisciplinary, bringing together medical image analysis, machine learning and data analysis, medical research, and data governance and management at scale. The talk introduces concisely the aims, structure and current results of VAMPIRE, the current vision for effective translation to society, and the several non-technical factors complementing technical research needed to achieve effective translation.
Emanuele (Manuel) Trucco, MSc, PhD, FRSA, FIAPR, is the NRP Chair of Computational Vision in Computing, School of Science and Engineering, at the University of Dundee, and an Honorary Clinical Researcher of NHS Tayside. He has been active since 1984 in computer vision, and since 2002 in medical image analysis, publishing more than 270 refereed papers and 2 textbooks, and serving on the organizing or program committee of major international and UK conferences. Manuel is co-director of VAMPIRE (Vessel Assessment and Measurement Platform for Images of the Retina), an international research initiative led by the Universities of Dundee and Edinburgh (co-director Dr Tom MacGillivray), and part of the UK Biobank Eye andVision Consortium. VAMPIRE develops software tools for efficient data and image analysis with a focus on multi-modal retinal images. VAMPIRE has been used in UK and international biomarker studies on cardiovascular risk, stroke, dementia, diabetes and complications, cognitive performance, neurodegenerative diseases, and genetics.
Venue: The Old Course Hotel (Hall of Champions)
9:30 Lecture 1
10:30 Break with Coffee
11:15 Lecture 2
12:15 Break for Lunch (not provided)
14:15 Lecture 3
Lecture 1: Introduction to Scalable Intelligent Systems
Lecture 2: Foundations for Scalable Intelligent Systems
Lecture 3: Implications of Scalable Intelligent Systems
Professor Carl Hewitt is the creator (together with his students and other colleagues) of the Actor Model of computation, which influenced the development of the Scheme programming language and the π calculus, and inspired several other systems and programming languages. The Actor Model is in widespread industrial use including eBay, Microsoft, and Twitter. For his doctoral thesis, he designed Planner, the first programming language based on pattern-invoked procedural plans.
Professor Hewitt’s recent research centers on the area of Inconsistency Robustness, i.e., system performance in the face of continual, pervasive inconsistencies (a shift from the previously dominant paradigms of inconsistency denial and inconsistency elimination, i.e., to sweep inconsistencies under the rug). ActorScript and the Actor Model on which it is based can play an important role in the implementation of more inconsistency-robust information systems. Hewitt is an advocate in the emerging campaign against mandatory installation of backdoors in the Internet of Things.
Hewitt is Board Chair of iRobust™, an international scientific society for the promotion of the field of Inconsistency Robustness. He is also Board Chair of Standard IoT™, an international standards organization for the Internet of Things, which is using the Actor Model to unify and generalize emerging standards for IoT. He has been a Visiting Professor at Stanford University and Keio University and is Emeritus in the EECS department at MIT.
A project to build the technology stack outlined in these lectures can bring Scalable Intelligent Systems to fruition by 2025. Scalable Intelligent Systems have the following characteristics:
Technology stack for Scalable Intelligent Systems is outlined below:
For example, pain management could greatly benefit from Scalable Intelligent Systems. Complexities of dealing with pain have led to the current opioid crisis. According to Eric Rodgers, PhD., director of the VA’s Office of Evidence Based Practice:
“The use of opioids has changed tremendously since the 1990s, when we first started formulating a plan for guidelines. The concept then was that opioid therapy was an underused strategy for helping our patients and we were trying to get our providers to use this type of therapy more. But as time went on, we became more aware of the harms of opioid therapy and the development of pill mills. The problems got worse.
It’s now become routine for providers to check the state databases to see if there’s multi-sourcing — getting prescriptions from other providers. Providers are also now supposed to use urine drug screenings and, if there are unusual results, to do a confirmation. [For every death from an opioid overdose] there are 10 people who have a problem with opioid use disorder or addiction. And for every addicted person, we have another 10 who are misusing their medication.”
Pain management requires much more than just prescribing opioids, which are often critical for short-term and less often longer-term use. [Coker 2015; Friedberg 2012; Holt 2017; Marchant 2017; McKinney 2015; Spiegel 2018; Tedesco, et. al. 2017; White 2017] Organizational aspects play an important role in pain management. [Fagerhaugh and Strauss 1977]
The situation in hospitals, nursing homes and homes for patients suffering from mental illnesses is increasingly challenging. The medical staff and special educators are often responsible for a large (and growing) number of residents, for which there is only a very limited time for one-to-one care. The risk of not being able to respond promptly to problems increases with the number of residents per medical staff. Moreover, elderly people find challenging giving and up independence when moving into a nursing home. Overnight, they find themselves in a place where care activities are structured, and at fixed times, with little freedom. However, many of these people either need regular medical care or are unable to live independently. The risk of injuries, falls, loss of consciousness or simply not being able to manage their health (e.g. take medication) leads to the decision to place the person in a socio-medical environment.
To be able to monitor residents in a nonintrusive manner would provide a certain degree of independence, safety and well-being for the residents and also relieve some of the pressure on nurses and educators. The ideal monitoring system should in fact be an ecosystem that includes sensors that can localise and detect resident’s activities and collect physiological data, a way of sending regular updates about the situation of the residents they take care of medical staff and a central monitoring system for both residents and medical staff and a logic to decide the most appropriate available person to intervene in case of problems with a resident. We propose an exploration of solutions that blend new technologies with a respect for human relationships in the context of a nursing home. This is to be achieved through an intelligent environment that monitors a resident’s general well-being unobstrusively, meaning both the physiological state, the activity and the location of the person.
Pascal Bruegger is a Professor in Computer Science at the School of Engineering and Architecture of Fribourg – University of Applied Sciences, Western Switzerland since 2013. He is responsible of the mobile technologies and applications curriculum in his department. His PhD subject was the creation of a holistic framework to design and implement ubiquitous computing systems supporting user activity and situation. With the widespread availability of smartphones, tablets and smartwatches, his research interest is oriented toward smart environments integrating mobile technologies. His goal is to gather different user data through mobile sensors in order to propose context base systems helping users carrying out their daily activities. For two years, Pascal, with his background in biology, has focused his research in physiological data and activities. Experienced in humanitarian ICT, Pascal has work many years for the International Committee of the Red Cross and has made several long-term missions across Africa and Asia. He managed large scale IT infrastructures and organised training seminars for specialists in humanitarian ICT. He is also ICT specialist in the Swiss rescue team.
The Carpentries (https://carpentries.org/) is a global community of
volunteers which teach foundational coding and data science skills to researchers
worldwide through Software Carpentry, Data Carpentry, and Library Carpentry
workshops. Being involved in the Carpentries since 2015, I organised and taught
at several workshops, developed new lessons, and trained new Carpentry instructors.
In my talk I will discuss the Carpentries pedagogical approach, and also consider
its applicability to teaching Computer Science students.