Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top $K$ items from noisy pairwise comparisons. In our setting, we are non-actively given $r$ pairwise comparisons between each pair of $n$ items, where each comparison has noise constrained by a very general noise model called the strong stochastic transitivity (SST) model. We analyze the competitive ratio of algorithms for the top-$K$ problem. In particular, we present a linear time algorithm for the top-$K$ problem which has a competitive ratio of $\tilde{O}(\sqrt{n})$; i.e. to solve any instance of top-$K$, our algorithm needs at most $\tilde{O}(\sqrt{n})$ times as many samples needed as the best possible algorithm for that instance (in contrast, all previous known algorithms for the top-$K$ problem have competitive ratios of $\tilde{\Omega}(n)$ or worse). We further show that this is tight: any algorithm for the top-$K$ problem has competitive ratio at least $\tilde{\Omega}(\sqrt{n})$.

In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be relevant only in some specific circumstances. In this setting, the contribution of this paper is to extend the random forest variable importances framework in order (i) to identify variables whose relevance is context-dependent and (ii) to characterize as precisely as possible the effect of contextual information on these variables. The usage and the relevance of our framework for highlighting context-dependent variables is illustrated on both artificial and real datasets.

A SpaceX capsule returned to Earth on Wednesday with precious science samples from NASA's one-year space station resident. SpaceX reported a good splashdown, with three red-and-white striped parachutes slowing the final descent. The Dragon had been at the station for a month, dropping off supplies as well as an experimental, inflatable room that will pop open in two weeks. It was set free by the station's big robot arm. "Dragon spacecraft has served us well, and it's good to see it departing full of science," Peake radioed from 250 miles up.

Elon Musk's much-vaunted Hyperloop supersonic train system took a tentative step towards reality with its first public test of a track run of its propulsion system prototype in the Nevada desert. The company, which changed its name to Hyperloop One on Wednesday to coincide with the open-air propulsion test, has also closed an 80m series B funding round which included investment from the French national rail company, SNCF. When – and very much if – completed, the Hyperloop train would work by propelling a sled at high speeds through a vacuum-sealed tunnel, which its founder claims would be able make the journey from San Francisco to Los Angeles in just 30 minutes. The Nevada test represents a very early proof of concept; there are a vast number of hurdles that the developers of Hyperloop still have to clear if the technology is to become a reality. Hyperloop One also announced a series of other partnerships, including Deutsche Bahn Engineering & Consulting and the British engineering consultancy group Arup, who are currently working on London's Crossrail.

While the human brain is often seen as the ultimate model for robotic intelligence, scientists are also learning plenty from the neurobiological structures and processes of more humble creatures, from fruit flies to rodents. Take the fruit fly – or rather, the maggot that grows up to be a fruit fly. Drosophila fruit fly larvae have fewer than 10 000 neurons – compared to about 100 billion in the human brain. But they display a range of complex orientation and learning behaviours that computational theory does not adequately explain at present. By studying how the larvae change their response to stimuli such as smells when these are associated with reward or punishment, the EU-funded MINIMAL project aims to unpick the exact mechanism underlying learning processes.

Artificial intelligence, or AI, no longer simply exists in science fiction movies and books. Scientists warn AI has and will continue to change almost every aspect of how people conduct business and live. Researchers say artificial intelligence can be a threat, as well as helpful, to humans. From the iPhone personal assistant Siri, to doing searches on the Internet, to the autopilot function, simple artificial intelligence has been around for some time, but is quickly getting more complex and more intelligent. "If we are going to make systems that are going to be more intelligent than us, it's absolutely essential for us to understand how to absolutely guarantee that they only do things that we are happy with," said Stuart Russell, computer science professor at the University of California Berkeley.

Visit us in Chicago on Thursday, June 2nd at 6:30pm to see a Machine Learning presentation by Jeremy Watt, instructor of the upcoming Metis course titled Machine Learning: Algorithms & Applications and author of Machine Learning Refined. This is an Open House for Jeremy's upcoming 6-week evening course at Metis, which starts on July 11th and will be held on Monday and Wednesday evenings from 6:30 - 9:30pm through August 17th . Please RSVP if you'd like to see a demonstration from Jeremy, and to learn more about the course structure and outcomes. Pizza and drinks will be served. Jeremy holds a PhD in Computer Science and Electrical Engineering from Northwestern University where he conducted research in machine learning and computer vision while actively consulting with partners in finance and insurance, as well as startups in the e-commerce and healthcare space.

Well rounded, visionary data scientist with broad spectrum of domain expertise, technical knowledge, and proven success in bringing measurable added value to companies ranging from startups to fortune 100, across multiple industries (finance, Internet, media, IT, security) and domains (data science, operations research, machine learning, computer science, business intelligence, statistics, applied mathematics, growth hacking, IoT). Vincent developed and deployed new techniques such as hidden decision trees (for scoring and fraud detection), automated tagging, indexing and clustering of large document repositories, black-box, scalable, simple, noise-resistant regression known as the Jackknife Regression (fit for black-box, real-time or automated data processing), model-free confidence intervals, bucketisation, combinatorial feature selection algorithms, detecting causation not correlations, and generally speaking, the invention of a set of consistent robust statistical / machine learning techniques that can be understood, implemented, interpreted, leveraged and fine-tuned by the non-expert. Vincent also invented many synthetic metrics (for instance, predictive power and L1 goodness-of-fit) that work better than old-fashioned stats, especially on badly-behaved sparse big data. Some of these techniques have been implemented in a Map-Reduce Hadoop-like environment. Some are concerned with identifying true signal in an ocean of noisy data.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Researchers from Stanford University have created a humanoid robot or robot mermaid to explore sunken treasures and relics. Tagged as OceanOne, the robo-mermaid uses artificial intelligence and virtual reality technology to allow human beings to operate it remotely, as per Stanford News. The robot mermaid looks like a human with hands that are installed with sensors to enable OceanOne to discern if an item is fragile or not. It also has two cameras as its eyes and an artificial human brain for navigating the deep sea and analyzing data. According to CNN, OceanOne's first journey to the deep water was to retrieve a vase from the ruins of Louis XIV's ship La Lune.