Program Description:  “The MSU / Westminster 21st Century Law Practice London Summer Program is a first of its kind, intensive study of technology, innovation, regulation, entrepreneurship and the international legal marketplace. With the deregulation of lawyers in the United Kingdom and the outgrowth of alternative legal services delivery models, London is poised to become the global leader in the legal services market. Our program will educate students about these new delivery models and help prepare students for the technology infused law jobs of the 21st Century.”

Educational Objectives:

My thanks to Neil Rose and all of the LegalFutures.co.uk conference organizers and speakers – it was a very interesting conference. As a byproduct of the modifications to the UK Legal Services Act, change is on the march in the UK legal services market. Keep your eye on these developments — as they may be coming to the US — sooner rather than later.

Artificial Intelligence is the science of making computer software that reasons about the world around it. Humanoid robots, Google Goggles, self-driving cars, even software that suggests music you might like to hear are all examples of AI. In this class, you will learn how to create this software from two of the leaders in the field. Class begins October 10.” Check out the syllabus here. Although I have previously highlighted Academic Earth – I am a little late on this New York Times coverage available here. See also Andrew Ng’s class on Machine Learning (which is also available right now on Academic Earth).

Kevin Slavin argues that we’re living in a world designed for — and increasingly controlled by — algorithms. In this riveting talk from TEDGlobal, he shows how these complex computer programs determine: espionage tactics, stock prices, movie scripts, and architecture.

Community detection in networks is an extremely important part of the broader network science literature. For quite a while, we have meant to highlight the extremely useful review article written by Mason Porter (Oxford) Jukka-Pekka Onnela (Harvard/Oxford) and Peter J Mucha (UNC). Rather than offer our description of the article, we thought it best to highlight commentary on the subject provided by the authors.

For example, in describing the paper over at Harvard’s Complexity and Social Networks Blog Jukka-Pekka Onnela posted the following… “Uncovering the “community” structure of social networks has a long history, but communities play a pivotal role in almost all networks across disciplines. Intuitively, one can think of a network community as consisting of a group of nodes that are relatively densely connected to each other but sparsely connected to other dense groups of nodes. Communities are important because they are thought to have a strong bearing on functional units in many networks. So, for example, communities in social networks can correspond to different social groups, such as family, whereas web pages dealing with a given subject tend to form topical communities.  The concept is simple enough, but it turns out that coming up with precise mathematical definitions and algorithms for community detection is one of the most challenging problems in network science. Recently, a lot of the research in this area has been done using ideas from statistical physics, which has an arsenal of tools and concepts to tackle the problem. Unfortunately (but understandably) relatively few non-physicists like to read statistical physics papers.”

These scholars quote Mark Newman noting “[T]he development of methods for finding communities within networks is a thriving sub-area of the field, with an enormous number of different techniques under development. Methods for understanding what the communities mean after you find them are, by contrast, still quite primitive, and much needs to be done if we are to gain real knowledge from the output of our computer programs.”  They later note “the problem of how to validate and use communities once they are identified is almost completely open.”

Anyway, if you are interested in learning more about this important piece of the network science toolkit … we suggest you read this paper!

We enjoyed today’s discussion at the Harambeenet Conference here in the Duke Computer Science Department.  The conference is centered upon network science and computer science education. It features lots of interdisciplinary scholarship and applications of computer science techniques in novel domains.

We are looking forward to an interesting final day of discussion and hope to participate in allied future conferences.