Nano Electronics and Computer Science Research Group

This is the website for the Nano Research group based at the University of Southampton ECS department, and details current research topics and the people connected with these. It shows some of the current research topics undertaken at the center, and gives an outline of what can be done for post graduate courses.

Computer Science? WTF?

What is Computer Science about?

Sample student reflective writing (Computer Science)

4 examples of student reflections

WAIS seminar: Computer Science, the Revolution in Schools

Tuesday 22nd April 2014 Speaker(s): Sue Sentance Organiser: Leslie Carr Time: 22/04/2014 15:00-16:00 Location: B32/3077 File size: 698 Mb Abstract Until recently, "computing" education in English schools mainly focused on developing general Digital Literacy and Microsoft Office skills. As of this September, a new curriculum comes into effect that provides a strong emphasis on computation and programming. This change has generated some controversy in the news media (4-year-olds being forced to learn coding! boss of the government’s coding education initiative cannot code shock horror!!!!) and also some concern in the teaching profession (how can we possibly teach programming when none of the teachers know how to program)? Dr Sue Sentance will explain the work of Computing At School, a part of the BCS Academy, in galvanising universities to help teachers learn programming and other computing skills. Come along and find out about the new English Computing Revolution - How will your children and your schools be affected? - How will our University intake change? How will our degrees have to change? - What is happening to the national perception of Computer Science?

How Minecraft Affects How Young People Learn About Computer Science

Abstract: As one of the newest art forms available to young people, gaming has become an increasing influence on young people’s education, even if not used in a classroom environment. This talk aims to explore examples of how video games have changed how young people understand and learn about certain subjects, with particular focus on how the indie title Minecraft allows them to learn about the world of Computer Science and how groups are looking to forward the cause of education though games.

Artificial Intelligence

Pearon's resource from the Brookshear Chapter

Mandevillian Intelligence: From Individual Vice to Collective Virtue

Abstract Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. In this talk, I will introduce the concept of mandevillian intelligence and review a number of strands of empirical research that help to shed light on the phenomenon. I will also attempt to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the cognitive impacts of a number of Web-based technologies (e.g., personalized search mechanisms). It also forces us to take seriously the idea that the exploitation (or even the accentuation!) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success. Speaker Biography Dr Paul Smart Paul Smart is a senior research fellow in the Web and Internet Science research group at the University of Southampton in the UK. He is a Fellow of the British Computer Society, a professional member of the Association of Computing Machinery, and a member of the Cognitive Science Society. Paul’s research interests span a number of disciplines, including philosophy, cognitive science, social science, and computer science. His primary area of research interest relates to the social and cognitive implications of Web and Internet technologies. Paul received his bachelors degree in Psychology from the University of Nottingham. He also holds a PhD in Experimental Psychology from the University of Sussex.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 1

In this class, we will discuss the course organization and provide a basic motivation for and introduction to the course. Readings: Web science: a provocative invitation to computer science, B. Shneiderman, Communications of the ACM 50 25--27 (2007) [Web link] Readings: Chapter 1 & 2, A Framework for Web Science, T. Berners-Lee and W. Hall and J. A. Hendler and K. O'Hara and N. Shadbolt and D. J. Weitzner Foundations and Trends® in Web Science 1 (2006) [Web link] Originally from: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

INFO2009 2012-13 Resource Group 15 - Web Science

Web Science - Group 15 created an interactive infographic which informs prospective applicants about the new Web Science undergraduate degrees offered at the University of Southampton, starting in October 2013. Web Science as a new and exciting field of research is also briefly outlined, supported by two video interviews with Dr Les Car, a web scientist.

The Age of Social Machines

Many of the most successful and important systems that impact our lives combine humans, data, and algorithms at Web Scale. These social machines are amalgamations of human and machine intelligence. This seminar will provide an update on SOCIAM, a five year EPSRC Programme Grant that seeks to gain a better understanding of social machines; how they are observed and constituted, how they can be designed and their fate determined. We will review how social machines can be of value to society, organisations and individuals. We will consider the challenges they present to our various disciplines.

Data Science, Microsoft and You

In this session we'll explore how Microsoft uses data science and machine learning across it's entire business, from Windows and Office, to Skype and XBox. We'll look at how companies across the world use Microsoft technology for empowering their businesses in many different industries. And we'll look at data science technologies you can use yourselves, such as Azure Machine Learning and Power BI. Finally we'll discuss job opportunities for data scientists and tips on how you can be successful!

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.