"Thou canst not touch the freedom of my mind," wrote the playwright John Milton in 1634. But, nearly 400 years later, technological advances in machines that can read our thoughts mean the privacy of our brain is under threat. Now two biomedical ethicists are calling for the creation of new human rights laws to ensure people are protected, including "the right to cognitive liberty" and "the right to mental integrity". Scientists have already developed devices capable of telling whether people are politically right-wing or left-wing. In one experiment, researchers were able to read people's minds to tell with 70 per cent accuracy whether they planned to add or subtract two numbers.

Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.

We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model's ability to selectively focus on the relevant parts of an input sequence.

Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior. Decision trees characterize the input-output relationship through a series of nested $if-then-else$ questions, the testing nodes, leading to a set of predictions, the leaf nodes. Several of such trees are often combined together for state-of-the-art performance: random forest ensembles average the predictions of randomized decision trees trained independently in parallel, while tree boosting ensembles train decision trees sequentially to refine the predictions made by the previous ones. The emergence of new applications requires scalable supervised learning algorithms in terms of computational power and memory space with respect to the number of inputs, outputs, and observations without sacrificing accuracy. In this thesis, we identify three main areas where decision tree methods could be improved for which we provide and evaluate original algorithmic solutions: (i) learning over high dimensional output spaces, (ii) learning with large sample datasets and stringent memory constraints at prediction time and (iii) learning over high dimensional sparse input spaces.

If you've ever bought a pair of new, unlaced sneakers you know what it's like to lace them yourself. It requires carefully wriggling the plastic-cased end of the lace up and through the tiny holes in the shoe's upper from the inside. Sometimes there are two layers to navigate: the cushioned textile interior and maybe a hard plastic overlay used to tighten the shoe around your foot when you tie it up. Of the approximately 120 steps involved in manufacturing an Adidas sneaker, that seemingly simple task is among those robots have not yet been able to master, at least not on an industrial scale, according to Adidas CEO Kasper Rorsted. "The biggest challenge the shoe industry has is how do you create a robot that puts the lace into the shoe," he said.

Robotic limbs aren't a new technology, though the range of motion and strength of such limbs continue to improve. Controlling prosthetics with your mind is another area of refinement, but they're typically connected directly to a patient's brain. A new technique where the robotic arm clicks directly to the bone, however, is showing promise. Johan Baggerman is the first patient in the Netherlands to get a click-on prosthetic arm that he can control with his mind. With a direct connection to the bone, click-on appliances like this don't need a prosthesis socket, making it easy to take on and off and avoiding chafing and other skin problems. The "mind control" is enabled by connecting a patient's nerves to the socket, with a special Bluetooth bracelet to receive the signals.

With nearly 400 breeds spanning almost every corner of the planet, dogs have long followed man on his travels. In a bid to piece together the complex evolution of dogs, researchers have looked at the genetic sequences of 161 modern breeds. The resulting map unearths new evidence that dogs travelled with humans across the Bering land bridge 15,000 years ago, and will likely help researchers identify disease-causing genes in both dogs and humans. In a bid to piece together the complex evolution of dogs, researchers have looked at the genetic sequences of 161 modern breeds. Researchers from the National Institutes of Health (NIH) in Maryland say their findings highlight how the oldest dog breeds evolved, or were bred to fill certain roles.

Steel production is an area that has been studied for decades, and as such the industry has remained very conservative. Despite the big data revolution beginning in the early 2000s, "old-school" industries like steel-making have largely shunned any form of data-driven applications. Fortunately, things change, and here's an example of how data analytics technologies, born within the internet industry, can be applied to an offline practice like turning pig iron into steel. When we began work with Magnitogorsk Iron and Steel Works (MMK), one of the world's largest steel producers and a leading steel company in Russia, a lot of time was spent looking for a challenge that if solved, could (a) positively impact business revenues, and (b) be completed in reasonable time.The challenge that was eventually uncovered and able to meet these criteria, is one well-known to all metallurgists: how much of each ferroalloy to add during steel-making process in order to ensure the required chemistry of the steel at the lowest possible cost. This chemistry is dictated by the international standards for steel – a list of required ranges for the amounts of each element in the final mix.

What are tech titans Google, Amazon and Uber agitating for to further the march of machine learning technology and ultimately inject more fuel in the engines of their own dominant platforms? Specifically, they're pushing for free and liberal access to publicly funded data -- urging that this type of data continue to be "open by default," and structured in a way that supports "wider use of research data." After all, why pay to acquire data when there are vast troves of publicly funded information ripe to be squeezed for fresh economic gain? Other items on this machine learning advancement wish-list include new open standards for data (including metadata); research study design that has the "broadest consents that are ethically possible," and a stated desire to rethink the notion of "consent" as a core plank of good data governance -- to grease the pipe in favor of data access and make data holdings "fit for purpose" in the AI age. These suggestions come in a 125-page report published today by the Royal Society, aka the U.K.'s national academy of science, ostensibly aimed at fostering an environment where machine learning technology can flourish in order to unlock mooted productivity gains and economic benefits -- albeit the question of who, ultimately, benefits as more and more data gets squeezed to give up its precious insights is the overarching theme and unanswered question here.

A group of prominent academics and tech executives fear that the U.K.'s exit from the European Union could jeopardize the U.K.'s lead in the development of machine learning technologies. British researchers have played a critical role in advances in machine learning -– a kind of artificial intelligence in which software learns from experience or data. But as demand for related expertise proliferates across industries, the country faces a "substantial skill shortage in this area," concluded a report published by Tuesday by The Royal Society, one of the world's oldest and most well-known scientific organizations. Although the report doesn't mention Brexit specifically, it implies that the U.K.'s decision to leave the European Union could exacerbate this skills gap. "As it considers its future approach to immigration policy, the U.K. must ensure that research and innovation systems continue to be able to access the skills they need," the report said.