It's always been about the links

Abstract The World Wide Web Consortium, W3C, is known for standards like HTML and CSS but there's a lot more to it than that. Mobile, automotive, publishing, graphics, TV and more. Then there are horizontal issues like privacy, security, accessibility and internationalisation. Many of these assume that there is an underlying data infrastructure to power applications. In this session, W3C's Data Activity Lead, Phil Archer, will describe the overall vision for better use of the Web as a platform for sharing data and how that translates into recent, current and possible future work. What's the difference between using the Web as a data platform and as a glorified USB stick? Why does it matter? And what makes a standard a standard anyway? Speaker Biography Phil Archer Phil Archer is Data Activity Lead at W3C, the industry standards body for the World Wide Web, coordinating W3C's work in the Semantic Web and related technologies. He is most closely involved in the Data on the Web Best Practices, Permissions and Obligations Expression and Spatial Data on the Web Working Groups. His key themes are interoperability through common terminology and URI persistence. As well as work at the W3C, his career has encompassed broadcasting, teaching, linked data publishing, copy writing, and, perhaps incongruously, countryside conservation. The common thread throughout has been a knack for communication, particularly communicating complex technical ideas to a more general audience.

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.