The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football. For this investigation, three datasets of UEFA injury recordings were available. Different machine learning algorithms were used in order to build a predictive model. The performance of the machine learning models is then improved by using feature selection conducted through correlation-based subset feature selection and random forests. 2. Predicting injuries in professional football using exposure records: The relationship between exposure (in training hours and match hours) in professional football athletes and injury incidence was studied. A common problem in football is understanding how the training schedule of an athlete can affect the chance of him getting injured. The task was to predict the number of days a player can train before he gets injured. 3. Predicting intrinsic injury incidence using in-training GPS measurements: A significant percentage of football injuries can be attributed to overtraining and fatigue. GPS data collected during training sessions might provide indicators of fatigue, or might be used to detect very intense training sessions which can lead to overtraining. This research used GPS data gathered during training sessions of the first team of THFC, in order to predict whether an injury would take place during a week.

Recently, there has been a surge of interest in using spectral methods for estimating latent variable models. However, it is usually assumed that the distribution of the observations conditioned on the latent variables is either discrete or belongs to a parametric family. In this paper, we study the estimation of an $m$-state hidden Markov model (HMM) with only smoothness assumptions, such as H\"olderian conditions, on the emission densities. By leveraging some recent advances in continuous linear algebra and numerical analysis, we develop a computationally efficient spectral algorithm for learning nonparametric HMMs. Our technique is based on computing an SVD on nonparametric estimates of density functions by viewing them as \emph{continuous matrices}. We derive sample complexity bounds via concentration results for nonparametric density estimation and novel perturbation theory results for continuous matrices. We implement our method using Chebyshev polynomial approximations. Our method is competitive with other baselines on synthetic and real problems and is also very computationally efficient.

We introduce a new artificial intelligence (AI) approach called, the 'Digital Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in various domains (e.g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains. This allows some of the burden of creative content generation to be passed from humans to machines. The approach was tested in the domain of chess problem composition. We used it to automatically compose numerous sets of chess problems based on attributes extracted and recombined from chess problems and tournament games by humans, renowned paintings, computer-evolved abstract art, photographs of people, and classical music tracks. The quality of these generated chess problems was then assessed automatically using an existing and experimentally-validated computational chess aesthetics model. They were also assessed by human experts in the domain. The results suggest that attributes collected and recombined from chess and other domains using the DSNS approach can indeed be used to automatically generate chess problems of reasonably high aesthetic quality. In particular, a low quality chess source (i.e. tournament game sequences between weak players) used in combination with actual photographs of people was able to produce three-move chess problems of comparable quality or better to those generated using a high quality chess source (i.e. published compositions by human experts), and more efficiently as well. Why information from a foreign domain can be integrated and functional in this way remains an open question for now. The DSNS approach is, in principle, scalable and applicable to any domain in which objects have attributes that can be represented using real numbers.

Composition conventions are guidelines used by human composers in composing chess problems. They are particularly significant in composition tournaments. Examples include, not having any check in the first move of the solution and not dressing up the board with unnecessary pieces. Conventions are often associated or even directly conflated with the overall aesthetics or beauty of a composition. Using an existing experimentally-validated computational aesthetics model for three-move mate problems, we analyzed sets of computer-generated compositions adhering to at least 2, 3 and 4 comparable conventions to test if simply conforming to more conventions had a positive effect on their aesthetics, as is generally believed by human composers. We found slight but statistically significant evidence that it does, but only to a point. We also analyzed human judge scores of 145 three-move mate problems composed by humans to see if they had any positive correlation with the computational aesthetic scores of those problems. We found that they did not. These seemingly conflicting findings suggest two main things. First, the right amount of adherence to composition conventions in a composition has a positive effect on its perceived aesthetics. Second, human judges either do not look at the same conventions related to aesthetics in the model used or emphasize others that have less to do with beauty as perceived by the majority of players, even though they may mistakenly consider their judgements beautiful in the traditional, non-esoteric sense. Human judges may also be relying significantly on personal tastes as we found no correlation between their individual scores either.

A framework for consensus modelling is introduced using Kleene's three valued logic as a means to express vagueness in agents' beliefs. Explicitly borderline cases are inherent to propositions involving vague concepts where sentences of a propositional language may be absolutely true, absolutely false or borderline. By exploiting these intermediate truth values, we can allow agents to adopt a more vague interpretation of underlying concepts in order to weaken their beliefs and reduce the levels of inconsistency, so as to achieve consensus. We consider a consensus combination operation which results in agents adopting the borderline truth value as a shared viewpoint if they are in direct conflict. Simulation experiments are presented which show that applying this operator to agents chosen at random (subject to a consistency threshold) from a population, with initially diverse opinions, results in convergence to a smaller set of more precise shared beliefs. Furthermore, if the choice of agents for combination is dependent on the payoff of their beliefs, this acting as a proxy for performance or usefulness, then the system converges to beliefs which, on average, have higher payoff.

Long before computers did in fact conquer the world, the influential German electronic group Kraftwerk calculated that probability, contemplating the changes to come by harnessing the latest musical gear and technology to create enduring work that soundtracked the birth of the Digital Age. On Sunday night, the quartet made its Hollywood Bowl debut by offering an overview of its career, including the metronomic 1974 classic "Autobahn," the menacing antinuke song "Radio-Activity," the genre-defining electro classic "Trans-Europe Express," the important synthesizer pop gems from the group's album "Computer World" and more. Mesmerizing to experience in the open air, the band looped bloops and beeps, thumps and bumps and sibilant fake high-hats to play songs once described by Detroit techno producer Carl Craig as "so stiff they were funky." The show was accompanied by 3-D visuals that locked image with music and required the 17,000-odd fans to don glasses, making the crowd seem teleported from an Atomic Age movie theater. The sight and sound presented a persuasive argument on the band's enduring influence, even if it revealed the ways that, like the beige and boxy early-era personal computers, the march of time renders even the most innovative technological expressions obsolete.

The release of Amazon's smartphone barely two years ago which has gone under could have as well never existed. The company received applause for its ideas regarding the'Fire Phone' with 3D screen effect powered by its four camera system developed in Lab 126, which is a Silicon Valley secretive web. This applause was later retrieved after it proved to be a commercial disaster. Developed with Amazon's OS with a high price margin, it got its users disappointed after it failed to pose as a challenge for Google or Apple smartphones. Apart from the faults that were seen in the phone, the company happened to enter the market when the market has already been taken over which was why they only managed to sell a few thousands while others were selling in millions and billion.

The Netflix logo is shown in this illustration photograph. Editor's Note: This is the third in a series of excerpts we are publishing from sociologist Jerry Davis's new book, "The Vanishing American Corporation: Navigating the Hazards of a New Economy." For more on the topic, watch last week's Making Sen e report below. Suppose you wanted to start an enterprise without leaving your couch. Imagine a hypothetical product: the iPhone Remote Drone Assassin App.

Researchers at Uppsala University have used computer modelling to study how brain tumours arise. The study, which is published today in the journal EBioMedicine, illustrated how researchers in the future will be able to use large-scale data to find new disease mechanisms and identify new treatment targets. The last ten years' progress in molecular biology has drastically changed how cancer researchers work. Instead of almost exclusively using different biological models, like cells, today large-scale statistical analyses are increasingly used to understand tumour diseases and find new therapies. Researchers at Uppsala University, together with colleagues at the University of Gothenburg, Chalmers University of Technology and University of Freiburg, have developed a new algorithm, aSICS, that uses large amounts of data to suggest hypotheses about "what causes what" in a cancer cell.

The London borough of Enfield has enlisted artificial intelligence Amelia to take on customer service tasks for its residents starting late this year. Developed by IPSoft, Amelia is a cognitive agent, capable of automating certain tasks as well as learn from its interactions. As to what makes Amelia a competent asset to the council, IPSoft says she is "[c]apable of analysing natural language, she understands context, applies logic, learns, resolves problems and even senses emotions." Amelia currently handles cognitive customer experience for professional services company Accenture and financial consultancy firm Deloitte. This will, however, be her first public sector role.