Greg Clark Secretary of State for Business, Energy and Industrial Strategy announced five new Artificial Intelligence centres of excellence for digital pathology and imaging, including radiology, using artificial intelligence (AI) medical advances. Established to assist hospitals make scans and biopsy images digital, the centres have the aim to free up more staff time for direct patient care in the NHS and is part of a bid to find new ways to speed up diagnosis of diseases to improve to outcomes for patients. The Industrial Strategy Challenge Fund is managed by UK Research and Innovation (UKRI) and will be based at universities and NHS facilities. The Artificial Intelligence centres are expected to be up and running in 2019. Health secretary Matt Hancock, said: "Artificial intelligence will play a crucial role in the future of the NHS – and we need to embrace it by introducing systems which can speed up diagnoses, improve patient outcomes, make every pound go further and give clinicians more time with their patients. "As part of our long-term plan, we will transform the NHS into an ecosystem of enterprise and innovation that allows technology to flourish and evolve." Professor Sir Mark Walport, chief executive of UKRI said: "The centres announced today bring together the teams that will develop artificial intelligence tools that can analyse medical images varying from x-rays to microscopic sections from tissue biopsies.

The UK's Artificial Intelligence revolution gets new backing, as the Business Secretary announces five new centres of excellence for digital pathology and imaging, including radiology, using AI medical advances. New centres announced today will bring together doctors, businesses and academics to develop products using these advances in digital technology to improve early diagnosis of disease, including cancer by detecting abnormalities. The products developed at the new centres will offer more personalised treatment for patients while freeing up doctors to spend more time caring for patients. The investment in large-scale genomics and image analysis will drive new understanding of how complex diseases develop, in a proactive step to ensure people get the right treatment at the right time. AI has the potential to revolutionise healthcare and improve lives for the better.

A global team of scientists have discovered AI can tell an individual's personality traits by analysing their eye movements. The scientists used machine learning to discover a link between eye movements and the personality of a person earlier this year. Armed with this information, the researchers deployed AI to analyse the eye movements of 42 students. The results were announced last week. In psychology, there are five basic personality traits: Extraversion, Agreeableness, Openness, Conscientiousness, and Neuroticism.

Russia's space agency says that one of the International Space Station's computers has malfunctioned, but the glitch doesn't pose any risks to the crew. Roscosmos said Tuesday that one of three computers in the station's Russian module has failed. It said Russian flight controllers plan to reboot it Thursday. The International Space Station photographed by Expedition 56 crew members from a Soyuz spacecraft after undocking. Roscosmos said Tuesday that one of three computers in the station's Russian module has failed.

MOSCOW – Russia's space agency says that one of the International Space Station's computers has malfunctioned, but the glitch doesn't pose any risks to the crew. Roscosmos said Tuesday that one of three computers in the station's Russian module has failed. It said Russian flight controllers plan to reboot it Thursday. The agency emphasized that the computer problem wouldn't affect the station's crew -- NASA's Serena Aunon-Chancellor, Russian Sergei Prokopyev and German Alexander Gerst. It said two other computers can maintain the station's operation.

The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration. Existing algorithms provide little information about their current policy's quality before executing it, and thus have limited use in high-stakes applications like healthcare. In this paper, we address such a lack of accountability by proposing that algorithms output policy certificates, which upper bound the suboptimality in the next episode, allowing humans to intervene when the certified quality is not satisfactory. We further present a new learning framework (IPOC) for finite-sample analysis with policy certificates, and develop two IPOC algorithms that enjoy guarantees for the quality of both their policies and certificates.

Supervised dimensionality reduction strategies have been of great interest. However, current supervised dimensionality reduction approaches are difficult to scale for situations characterized by large datasets given the high computational complexities associated with such methods. While stochastic approximation strategies have been explored for unsupervised dimensionality reduction to tackle this challenge, such approaches are not well-suited for accelerating computational speed for supervised dimensionality reduction. Motivated to tackle this challenge, in this study we explore a novel direction of directly learning optimal class-aware embeddings in a supervised manner via the notion of supervised random projections (SRP). The key idea behind SRP is that, rather than performing spectral decomposition (or approximations thereof) which are computationally prohibitive for large-scale data, we instead perform a direct decomposition by leveraging kernel approximation theory and the symmetry of the Hilbert-Schmidt Independence Criterion (HSIC) measure of dependence between the embedded data and the labels. Experimental results on five different synthetic and real-world datasets demonstrate that the proposed SRP strategy for class-aware embedding learning can be very promising in producing embeddings that are highly competitive with existing supervised dimensionality reduction methods (e.g., SPCA and KSPCA) while achieving 1-2 orders of magnitude better computational performance. As such, such an efficient approach to learning embeddings for dimensionality reduction can be a powerful tool for large-scale data analysis and visualization.

We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propose a model-specific interpretation method, called YASENN. Our method inherits many advantages of model-agnostic distillation, such as an ability to focus on the particular input region and to express an explanation in terms of features different from those observed by a neural network. Moreover, examination of distillation error makes the method applicable to the problems with low tolerance to interpretation mistakes. Technically, YASENN distills the network with an ensemble of layer-wise gradient boosting decision trees and encodes the sequences of neuron activations with leaf indices. The finite number of unique codes induces a partitioning of the input space. Each partition may be described in a variety of ways, including examination of an interpretable model (e.g. a logistic regression or a decision tree) trained to discriminate between objects of those partitions. Our experiments provide an intuition behind the method and demonstrate revealed artifacts in neural network decision making.

In this paper, we explore creative image generation constrained by small data. To partially automate the creation of cartoon sketches consistent with a specific designer's style, where acquiring a very large original image data set is impossible or cost prohibitive, we exploit domain specific knowledge for a huge reduction in original image requirements, creating an effectively infinite number of cartoon giraffes from just nine original drawings. We introduce "expressive augmentations" for cartoon sketches, mathematical transformations that create broad domain appropriate variation, far beyond the usual affine transformations, and we show that chained GANs models trained on the temporal stages of drawing or "complexity layers" can effectively add character appropriate details and finish new drawings in the designer's style. We discuss the application of these tools in design processes for textiles, graphics, architectural elements and interior design.

As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Optimization (PSO) algorithm and so is called PSOHMM. In order to overcome the statistical constraints in HMM, the paper develops re-normalization and re-mapping mechanisms to ensure the constraints in HMM. The experiments have shown that PSOHMM can search better solution than BWHMM, and has faster convergence speed.