I hope you enjoy them, and it would be great if you would leave brief reviews of these books in the comments below – I'm sure all the authors would appreciate your comments and shares. About the Author Lee Baker is an award-winning software creator with a passion for turning data into a story. A proud Yorkshireman, he now lives by the sparkling shores of the East Coast of Scotland. Physicist, statistician and programmer, child of the flower-power psychedelic '60s, it's amazing he turned out so normal! Turning his back on a promising academic career to do something more satisfying, as the CEO and co-founder of Chi-Squared Innovations he now works double the hours for half the pay and 10 times the stress - but 100 times the fun! He also wanted to be rich, famous and good looking.

World chess champion Magnes Carlsen (right) won't play his computer or play the game like a computer. Instead, he chooses his strategy based on what he knows about his opponent. World chess champion Magnes Carlsen (right) won't play his computer or play the game like a computer. Instead, he chooses his strategy based on what he knows about his opponent. Next month, there's a world chess championship match in New York City, and the two competitors, the assembled grandmasters, the budding chess prodigies, the older chess fans -- everyone paying attention -- will know this indisputable fact: A computer could win the match hands down. They've known as much for almost 20 years -- ever since May 11, 1997.

A university without any teachers has opened in California this month. It's called 42 - the name taken from the answer to the meaning of life, from the science fiction series The Hitchhiker's Guide to the Galaxy. The US college, a branch of an institution in France with the same name, will train about a thousand students a year in coding and software development by getting them to help each other with projects, then mark one another's work. This might seem like the blind leading the blind - and it's hard to imagine parents at an open day being impressed by a university offering zero contact hours. But since 42 started in Paris in 2013, applications have been hugely oversubscribed. Recent graduates are now working at companies including IBM, Amazon, and Tesla, as well as starting their own firms.

My team and I attended several legaltech-focused conferences this month and of course, as expected, machine learning and AI were the topics that everyone wanted to discuss in between sessions. Yet interestingly, at the Emerging Legal Technology Forum put on by Legalx, one of the panelists -- Mark Tamminga, leader of innovation initiatives at Gowling WLG -- was against using these terms in reference to emerging legal technologies. He pointed out that often what is being called AI is really not that at all, and felt that these words were being used as fancy buzzwords that escape the real mechanics of these technologies. As someone deeply involved in the development community here in Toronto, I wholeheartedly agree with his perspective. Many of the conversations occurring in legaltech around what people are calling machine learning are actually algorithmic solutions preprogrammed (that's right, programmed by humans) to do a particular task; nothing that deviates greatly from anything that's already been done many years ago.

The AI'nightmare machine': Spooky Google algorithm transforms famous sights into horror scenes The DeepDream algorithm transfers a photograph of the Eiffel Tower in Paris to a horror scene, in a style called'Fright Night', according to the website. 'We use state-of-the-art deep learning algorithms to learn how haunted houses, or toxic cities look like,' the researchers said Interested viewers can help MIT find the essence of horror on the website, or look at more of the pictures the Nightmare Machine has generated on Instagram. A normal photograph of St Basil's Cathedral in Moscow is pictured left. The Nightmare Machine team is making photographs of famous landmarks appear scary. In creating a network that works against itself, researchers believe it will eventually learn to be more precise in its output.

Interfacing a kinetic action of a person to an action of a machine system is an important research topic in many application areas. One of the key factors for intimate human-machine interaction is the ability of the control algorithm to detect and classify different user commands with shortest possible latency, thus making a highly correlated link between cause and effect. In our research, we focused on the task of mapping user kinematic actions into sound samples. The presented methodology relies on the wireless sensor nodes equipped with inertial measurement units and the real-time algorithm dedicated for early detection and classification of a variety of movements/gestures performed by a user. The core algorithm is based on the approximate Bayesian inference of Vector Autoregressive Hierarchical Hidden Markov Models (VAR-HHMM), where models database is derived from the set of motion gestures. The performance of the algorithm was compared with an online version of the K-nearest neighbours (KNN) algorithm, where we used offline expert based classification as the benchmark. In almost all of the evaluation metrics (e.g. confusion matrix, recall and precision scores) the VAR-HHMM algorithm outperformed KNN. Furthermore, the VAR-HHMM algorithm, in some cases, achieved faster movement onset detection compared with the offline standard. The proposed concept, although envisioned for movement-to-sound application, could be implemented in other human-machine interfaces.

We present a derivation and theoretical investigation of the Adams-Bashforth and Adams-Moulton family of linear multistep methods for solving ordinary differential equations, starting from a Gaussian process (GP) framework. In the limit, this formulation coincides with the classical deterministic methods, which have been used as higher-order initial value problem solvers for over a century. Furthermore, the natural probabilistic framework provided by the GP formulation allows us to derive probabilistic versions of these methods, in the spirit of a number of other probabilistic ODE solvers presented in the recent literature. In contrast to higher-order Runge-Kutta methods, which require multiple intermediate function evaluations per step, Adams family methods make use of previous function evaluations, so that increased accuracy arising from a higher-order multistep approach comes at very little additional computational cost. We show that through a careful choice of covariance function for the GP, the posterior mean and standard deviation over the numerical solution can be made to exactly coincide with the value given by the deterministic method and its local truncation error respectively. We provide a rigorous proof of the convergence of these new methods, as well as an empirical investigation (up to fifth order) demonstrating their convergence rates in practice.

Gasper Tkacik IST Austria Am Campus 1 A - 3400 Klosterneuburg, Austria In many applications, it is desirable to extract only the relevant aspects of data. A principled way to do this is the information bottleneck (IB) method, where one seeks a code that maximizes information about a'relevance' variable, Y, while constraining the information encoded about the original data, X. Unfortunately however, the IB method is computationally demanding when data are high-dimensional and/or non-gaussian. Here we propose an approximate variational scheme for maximizing a lower bound on the IB objective, analogous to variational EM. Using this method, we derive an IB algorithm to recover features that are both relevant and sparse. Finally, we demonstrate how kernelized versions of the algorithm can be used to address a broad range of problems with nonlinear relation between X and Y.

Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes to infer the unknown topology. Structural equation models (SEMs) are capable of incorporating exogenous inputs to resolve inherent directional ambiguities. However, conventional SEMs assume full knowledge of exogenous inputs, which may not be readily available in some practical settings. The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the well-known parallel factor (PARAFAC) decomposition. It turns out that second-order piecewise stationary statistics of exogenous variables suffice to identify the hidden topology. Capitalizing on the uniqueness properties inherent to high-order tensor factorizations, it is shown that topology identification is possible under reasonably mild conditions. In addition, to facilitate real-time operation and inference of time-varying networks, an adaptive (PARAFAC) tensor decomposition scheme which tracks the topology-revealing tensor factors is developed. Extensive tests on simulated and real stock quote data demonstrate the merits of the novel tensor-based approach.

A dream is a succession of images, ideas, emotions, and sensations that usually occurs involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not fully understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]