Robots will soon outnumber human beings and 3D printers will be used to create human organs, according to a survey of over 2,000 British adults. The next two decades will see technology radically impact healthcare and automotive industries, according to SMG Insight and YouGov research commissioned by London & Partners, with GP consultations available through virtual reality or driverless cars replacing traditional vehicles. The research was inspired by predictions made by Imperial College London's Tech Foresight research team ahead of London Tech Week, an event celebrating innovation with the UK's capital city. "London's technologists, scientists, medics and entrepreneurs are creating the future," said professor David Gann, vice president innovation at Imperial College London. "No city in the world enjoys London's quotient of talent, technology culture and capital. It is a potent combination. "London is an environment where ideas flourish, design and innovation is embraced and new technologies are transforming our lives for the better.

The ability to represent complex high dimensional probability distributions in a compact form is one of the key insights in the field of graphical models. Factored representations are ubiquitous in machine learning and lead to major computational advantages. We explore a different type of compact representation based on discrete Fourier representations, complementing the classical approach based on conditional independencies. We show that a large class of probabilistic graphical models have a compact Fourier representation. This theoretical result opens up an entirely new way of approximating a probability distribution. We demonstrate the significance of this approach by applying it to the variable elimination algorithm. Compared with the traditional bucket representation and other approximate inference algorithms, we obtain significant improvements.

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity.

The lasso and related sparsity inducing algorithms have been the target of substantial theoretical and applied research. Correspondingly, many results are known about their behavior for a fixed or optimally chosen tuning parameter specified up to unknown constants. In practice, however, this oracle tuning parameter is inaccessible so one must use the data to select one. Common statistical practice is to use a variant of cross-validation for this task. However, little is known about the theoretical properties of the resulting predictions with such data-dependent methods. We consider the high-dimensional setting with random design wherein the number of predictors $p$ grows with the number of observations $n$. Under typical assumptions on the data generating process, similar to those in the literature, we recover oracle rates up to a log factor when choosing the tuning parameter with cross-validation. Under weaker conditions, when the true model is not necessarily linear, we show that the lasso remains risk consistent relative to its linear oracle. We also generalize these results to the group lasso and square-root lasso and investigate the predictive and model selection performance of cross-validation via simulation.

Gaussian random fields are a powerful tool for modeling environmental processes. For high dimensional samples, classical approaches for estimating the covariance parameters require highly challenging and massive computations, such as the evaluation of the Cholesky factorization or solving linear systems. Recently, Anitescu, Chen and Stein \cite{M.Anitescu} proposed a fast and scalable algorithm which does not need such burdensome computations. The main focus of this article is to study the asymptotic behavior of the algorithm of Anitescu et al. (ACS) for regular and irregular grids in the increasing domain setting. Consistency, minimax optimality and asymptotic normality of this algorithm are proved under mild differentiability conditions on the covariance function. Despite the fact that ACS's method entails a non-concave maximization, our results hold for any stationary point of the objective function. A numerical study is presented to evaluate the efficiency of this algorithm for large data sets.

With laptops and hand-held devices slowly replacing pencils and paper, some educators question the importance of teaching handwriting in the classroom. Although some say it is a nonessential motor skill, researchers have found evidence that in fact it helps children pay attention to and understand the written language. Brain scans in children who did not yet know how to print revealed they are unable to distinguish letters and respond to them'the same as to a triangle'. Although some say it is a nonessential motor skill, researchers have found evidence this skill helps children pay attention to and understand the written language. Brain scans in children who did not yet know how to print yet revealed they are unable to distinguish letters and respond to them'the same as to a triangle' You have to see letters in'the mind's eye' in order to create them on a piece of paper she explained.

Today we announced our recent 10M financing led by Canvas Ventures, Trinity Ventures, and Microsoft to fuel the adoption of our new CrowdFlower AI solution. This post explores the "why" and the "what" behind this financing. We wanted to bring the economics of applying AI and Machine Learning within the reach of every business. Why did we think this was the right goal at the right time? It was based on observing two groups – first our own data science customers, second everyone else trying to deploy Machine Learning. For the past few years we've had a front row seat seeing the emergence of the data scientist role inside companies large and small.

Great hardware needs great software. To help data scientists and developers make the most of the vast opportunities in deep learning, we're announcing today at the International Supercomputing show, ISC16, a trio of new capabilities for our deep learning software platform. The three -- NVIDIA DIGITS 4, CUDA Deep Neural Network Library (cuDNN) 5.1 and the new GPU Inference Engine (GIE) -- are powerful tools that make it even easier to create solutions on our platform. NVIDIA DIGITS 4 introduces a new object detection workflow, enabling data scientists to train deep neural networks to find faces, pedestrians, traffic signs, vehicles and other objects in a sea of images. This workflow enables advanced deep learning solutions -- such as tracking objects from satellite imagery, security and surveillance, advanced driver assistance systems and medical diagnostic screening.

An online learning program which can tell when a student is becoming bored and inattentive is one of the key developments forecast to reshape university education in Australia in the next five years, according to a new report. The 2016 NMC Technology Outlook for Australian Tertiary Education says that so-called "affective computing", which is able to use video imagery of facial expressions to discern human emotions, will soon be coupled with online learning platforms to encourage students to keep their minds on their work. The report says this is likely to be adopted by universities in the next four to five years. It forecast "online learning situations wherein a computerised tutor reacts to facial cues of boredom in a student in an effort to motivate or boost their confidence." "Software technology will literally learn to learn, interpreting and responding to learners' most nuanced gestures and emotions – whether they are feeling bored, intimidated or satisfied," says Brenda Frisk, head of learning technology at Open Universities Australia, a partner in the report.

Andrew Ng, Chief Scientist for Baidu Research in Silicon Valley, Stanford University associate professor, chairman and co-founder of Coursera, and machine learning heavyweight, is authoring a new book on machine learning, titled Machine Learning Yearning. This isn't your typical machine learning book, however; it focuses on the skills and strategies needed to implement machine learning systems, as opposed to acting as an overview of various classification algorithms or the current state of the art science. The goal of this book is to teach you how to make the numerous decisions needed with organizing a machine learning project. Ng also notes that the book will be "around 100 pages, and contain many easy-to-read 1-2 page chapters." However, not only does the website provide an overview of the book, it states that, if you sign up to the email list by Friday, June 24th, you will gain free access to draft chapters as they are finished.