Windows Colour Scheme

Windows offers several high contrast colour schemes which may be useful for users with vision impairments or specific learning difficulties such as dyslexia.

INFO1010 Mark Scheme for group presentations

Detailed mark scheme for group presentations, can be used to highlight the various aspects of the content and processes associated with a presentation which need to be addressed

COMP1205 Technical Report Marking Scheme

This document outlines the grading boundaries for marking the technical report. It can be used for marking sample reports and peer review. The COMP1205 course team use it to show how they mark a sample report.

Sustaining Success mark scheme

This explains the mark scheme for the portfolio

Technical report specification, 2015 allocations, mark scheme, links

Specification for technical report, allocations (2015) mark scheme. Also contains a links to supporting materials including Harvard referencing. A template for a technical report is found at http://www.edshare.soton.ac.uk/14581

Technical report mark scheme

Mark scheme for technical report. 2015 COMP1205 assignment is at http://www.edshare.soton.ac.uk/14582

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.