Porsche Design had another Mobile World Congress revelation besides a limited edition version of Huawei's Watch 2. The design group has also announced a laptop-tablet convertible and detachable hybrid of its own called Book One, which looks like it was designed to rival Microsoft's Surface Book. It runs on Windows 10 Pro and is loaded with all the feature's you'd expect on a Windows hybrid: it has Cortana and facial recognition through Windows Hello. Plus, you can take notes and draw all over the tablet's touchscreen using Windows Ink. Its other specs and hardware might look familiar, because they're very similar to Surface Book's. The device's 13.3-inch display has a 3,200 x 1,800 pixel resolution, it runs on an Intel Core i7 processor, has 512GB of storage, 16 GB of RAM, two USB-C and two USB 3.0 ports. Even its pricing is pretty similar: at $2,495, it's just $100 more expensive than the cheapest configuration of Microsoft's Surface Book.

Sony Corp. and Line Corp., Japan's most popular messaging service, are considering joining forces to develop devices powered by artificial intelligence. The companies are exploring opportunities around digital personal assistant technology to co-create a new communication experience, Sony said in a statement at the Mobile World Congress in Barcelona, Spain, on Monday. Sony unveiled concept earphones powered by Xperia Agent, a virtual butler that responds to voice commands and head gestures. While Sony's Xperia smartphones are an also-ran in a market dominated by Apple Inc. and Samsung Electronics Co., Chief Executive Officer Kazuo Hirai has resisted pressure to shutter the mobile business. He argues it will serve as a springboard into the nascent market for wearable and interconnected devices known as the Internet of Things.

This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.

Alphabet Jigsaw (formerly Google Ideas) officially unveiled this morning their new tool for fighting toxic speech online, appropriately called Perspective. Powered by a deep-learning model trained on more than 17 million manually reviewed reader comments provided by the New York Times, the model assigns a score to a given passage of text, rating it on a scale from 0 to 100%, similar to statements that human reviewers have previously rated as "toxic." What makes this new approach from Google so different than past approaches is that it largely focuses on language rather than ideas: for the most part you can express your thoughts freely and without fear of censorship as long as you express them clinically and clearly, while if you resort to emotional diatribes and name calling, regardless of what you talk about, you will be flagged. What does this tell us about the future of toxic speech online and the notion of machines guiding humans to a more "perfect" humanity? One of the great challenges in filtering out "toxic" speech online is first defining what precisely counts as "toxic" and then determining how to remove such speech without infringing on people's ability to freely express their ideas.

All of the glorious diversity that is the human race is derived, ultimately, from those two strands of material tightly wound around one another. But sometimes things go wrong. Many horrific diseases, like breast cancer, Huntington's disease and leukemia, are caused by genetic defects – "mistakes" somewhere in the 6 billion pairs of DNA chemical compounds. In this enormous, unique "database" we all possess, what is "normal" and what is a disease-causing mutation? To answer these questions, we need two things: a huge sample set of human DNA in a form that we can "sequence" (that is, enumerate each and every one of the six billion "base pairs") and massive amounts of computing and storage space to do our computations.

Microsoft Monday is a weekly column that focuses on updates in regards to the Redmond giant. This week, Microsoft Monday includes details about Windows 10 upgrade revealed, EU still not satisfied with data collection transparency, HoloLens used for designing operating rooms, the launch of Skype Lite, an investment in AirMap, Halo split-screen is returning, new features for the Mail and Calendar apps for Windows 10 and much more! Microsoft has recently announced that a fourth major Windows 10 upgrade is being planned. Microsoft made the announcement at Ignite Australia roughly about two weeks ago. The first three updates are known as November Update, Anniversary Update and Creators Update.

They have long endured stereotypes of the'dumb jock,' but according to new research, athletes may have better cognitive skills than the rest of us. A study in Sweden found that top-tier adolescent soccer players outperform the general population on'executive function' tests, which assess the processes that regulate thought and action, such as problem solving and multi-tasking. According to the researchers, these scores can actually be used to help predict how successful young players will be as athletes. The study in Sweden found that top-tier soccer players, even at a young age, outperform the general population on'executive function' tests, which assess the processes that regulate thought and action, such as problem solving and multi-tasking. In the study, researchers at the Karolinska Institutet investigated executive functions in 30 elite soccer players ages 12-19.

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by renewable sources and an energy storage system, taking into account the economical profit in energy trading with the main-grid. In particular, this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making in the microgrid. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. This approach will be referred in the following as fuzzy-HGA. Results are compared with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67\% in the considered energy trading problem yielding at the same time a simpler RB.

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

Supervised machine learning methods are algorithms for inductive inference. On the basis of a sample, they construct (learn) a computable model of a data generating process that facilitates inference over the underlying ground truth function and aims to predict its function values at unobserved inputs. Among supervised learning methods, nonparametric algorithms tend to offer greater flexibility to learn rich function classes. Unfortunately, many classical techniques for nonparametric regression, such as the Nadaraya-Watson estimator [21], [14] or the LOESS method, [6] suffer from a practical limitation: their regression performance depends on the choice of hyperparameters. While in principle, it would be possible to tune these to the data (in manner similar in spirit to the one we propose in this work), to the best of our knowledge, currently there is little understanding on how to do so with a global optimiser that offers theoretical performance guarantees on the optimisation solution. This means that in practice, one is left to engineer these hyperparameters (or the settings of an optimiser) by manual tuning in order to ensure good performance on a particular learning problem. Of course, this stands in opposition to the motivation for utilising nonparametric learning, especially in system identification: which is to facilitate flexible and fully automated black-box learning that does not require manual intervention.