We consider linear models where $d$ potential causes $X_1,...,X_d$ are correlated with one target quantity $Y$ and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression coefficients has 'generic' orientation relative to the covariance matrix $\Sigma_{XX}$ of $X$. Using an ICA based model for confounding, we show that both confounding and overfitting yield regression vectors that concentrate mainly in the space of low eigenvalues of $\Sigma_{XX}$.

Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can be used for a variety of machine learning tasks. However, learning structural representations of nodes is a challenging problem, and it has typically involved manually specifying and tailoring topological features for each node. In this paper, we develop GraphWave, a method that represents each node's network neighborhood via a low-dimensional embedding by leveraging heat wavelet diffusion patterns. Instead of training on hand-selected features, GraphWave learns these embeddings in an unsupervised way. We mathematically prove that nodes with similar network neighborhoods will have similar GraphWave embeddings even though these nodes may reside in very different parts of the network, and our method scales linearly with the number of edges. Experiments in a variety of different settings demonstrate GraphWave's real-world potential for capturing structural roles in networks, and our approach outperforms existing state-of-the-art baselines in every experiment, by as much as 137%.

UK companies are setting the global standard for the next wave of breakthroughs, particularly in technology.In 2016, AlphaGo, a computer programme created by London-based engineers, beat Lee Sedol, a top player of the game. The next wave of research for DeepMind will focus on developing advanced algorithms that can help scientists tackle challenges like curing disease, reducing energy consumption, or building revolutionary materials.

Researchers have broken speed records for the building blocks of quantum computing, getting them ready to actually build the computers that could revolutionise the world. Quantum computers could come to dwarf the processing power of today's conventional computers, by harnessing the strange effects of quantum physics. They could eventually allow for new techniques in everything from healthcare to physics, allowing work to be done at a speed almost inconceivable today. But scientists are still working at the most foundational level of such technology, attempting to put together the building blocks. The new research drastically improves those building blocks: making the "logic gates" that will be used in those computers faster than they've ever been before.

Researchers from the University of Freiburg in Germany recently stumbled upon an?interesting solution?

North Korea poses a more considerable cyber-attack threat to the international community than Russia, according to cyber-security firm CrowdStrike. Kim Jong-un's regime has been connected to a number of major hacks in recent years, with its attention primarily focused on neighbour and rival South Korea. Most famously, a hacker collective known as the "Lazarus Group", allegedly backed by Pyongyang, was blamed for the WannaCry ransomware attack last summer, which locked out IT systems across the world, hitting the NHS in the UK and such major international businesses as Russia's Sberbank and carmakers Honda and Renault. Speaking ahead of the launch of the 2018 edition of CrowdStrike's annual threat report, co-founder Dmitri Alperovitch warned that cyber-attacks had grown in intensity and sophistication over the last year. "In 2018, my biggest worry is actually about North Korea. I worry a great deal that they may do a destructive attack, perhaps against our financial sector, in an attempt to deter a potential US strike against either their nuclear facilities or even the regime itself," he told The Guardian.

Netflix's huge selection of films and TV is finally coming to Sky. The Sky Q box will soon be able to show the various video offered by Netflix, the two companies have said. New and existing customers will be able to watch Netflix through their box and by paying through their subscription, for the first time. That will allow the Netflix shows like Stranger Things or Orange Is The New Black on the Sky Q box. They will be able to make full use of Sky Q functionality, including its menus, voice search and other tools. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

It depends who you ask. AI might be a hot topic but you'll still need to justify those projects. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. At a very high level artificial intelligence can be split into two broad types: narrow AI and general AI.

When we look at a stack of blocks or a stack of Oreos, we intuitively have a sense of how stable it is, whether it might fall over, and in what direction it may fall. That's a fairly sophisticated calculation involving the mass, texture, size, shape, and orientation of the objects in the stack. Researchers at MIT led by Josh Tenenbaum hypothesize that our brains have what you might call an intuitive physics engine: The information that we are able to gather through our senses is imprecise and noisy, but we nonetheless make an inference about what we think will probably happen, so we can get out of the way or rush to keep a bag of rice from falling over or cover our ears. Such a "noisy Newtonian" system involves probabilistic understandings and can fail. Consider this image of rocks stacked in precarious formations.

Here are four projects led by 5th-year engineering students majoring in Computer Science, Big Data and Connected Objects and Financial Engineering which resorted to Machine Learning. "Weather Learning" is a project which aims at developing a tool allowing to predict the weather report, anywhere in France. It is based on modern methods of Machine learning and Deep learning. This project does not use physico-chemical modellings but the history of the weather data. After identifying meteorological sources from which data from more than 10 years ago could be extracted, students developed algorithms allowing to extract the meteorological data. MongoDB was chosen as a database for its capacity to manage geographical coordinates.