With an eye-watering price tag of £999, these customers will be hoping for some decent bang for their buck. But that didn't put off Apple fans as the much-anticipated iPhone X's with its lush screen and facial-recognition skills tests the patience of consumers and investors with demand outstripping supply around the world. And those who managed to get their hands on one this morning will be pleased to know they are sitting on potential gold mines, with devices already listed for sale on auction website eBay for up to £15,000. The X is Apple's next generation smartphone that uses facial recognition software for the first time and is going on sale in cities worldwide today - with queues building at Apple Stores amid rumours of limited stock. Among the first to get the new device in Britain was reality TV contestant Marco Pierre White Jr, 22, the son of the celebrity chef, who joined the queue last night to pick up a phone for his girlfriend Francesca Suter.

With artificial intelligence set to revolutionize how the military runs surveillance missions around the world, one top Defense Department official hopes to bring intelligent systems to the Pentagon's efforts both on and off the battlefield. As director of defense intelligence for warfighter support, Air Force Lt. Gen Jack Shanahan spearheaded Project Maven, a Pentagon initiative to rapidly turn drone surveillance footage into useful intelligence through machine learning. The tool is scheduled to launch by the end of the year. The Pentagon has long used drones over the Middle East to inform the fight against groups like ISIS. Though drone and camera technology have advanced significantly, the back end looks much the same as it did decades ago, with analysts still spending countless hours manually scrolling through video for points of interest.

Fresh breakthroughs in artificial intelligence come thick and fast these days. Last month, Google's DeepMind revealed its latest Go-playing AI which mastered the ancient game from scratch in a mere 70 hours. AI can spot cancer in medical scans better than humans, meaning radiotherapy can be targeted in minutes, not hours. We may soon use the technology to design new drugs, or repurpose existing ones to treat other, neglected, diseases. But as we begin to realise these opportunities, the potential risks increase: that AI will proliferate, uncontrolled and unregulated, in the hands of a few increasingly powerful technology firms, at the expense of jobs, equality and privacy.

The "sharks" are coming to CCW; Herjavec is first of three to be announced in 2018 New Orleans (October 25, … The "sharks" are coming to CCW; Herjavec is first of three to be announced in 2018 New Orleans (October 25, … Today, and tomorrow we will be addressing the United Nations at ESCAP about the benefits of AI, it's current… Artificial Intelligence is not a concept anymore but a reality. Want to build the most lucrative business? In any business (including staffing), your data repository or database is pretty much like your home.

Machine learning is not a new topic nor is it new to supply chain. However, it has been garnering a lot of attention within supply chain due to its transformational business potential. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range." Machine learning is often categorized as being supervised and unsupervised. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.

Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen real-life event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.

Early approaches to multiple-output Gaussian processes (MOGPs) relied on linear combinations of independent, latent, single-output Gaussian processes (GPs). This resulted in cross-covariance functions with limited parametric interpretation, thus conflicting with the ability of single-output GPs to understand lengthscales, frequencies and magnitudes to name a few. On the contrary, current approaches to MOGP are able to better interpret the relationship between different channels by directly modelling the cross-covariances as a spectral mixture kernel with a phase shift. We extend this rationale and propose a parametric family of complex-valued cross-spectral densities and then build on Cram\'er's Theorem (the multivariate version of Bochner's Theorem) to provide a principled approach to design multivariate covariance functions. The so-constructed kernels are able to model delays among channels in addition to phase differences and are thus more expressive than previous methods, while also providing full parametric interpretation of the relationship across channels. The proposed method is first validated on synthetic data and then compared to existing MOGP methods on two real-world examples.

Sometimes it seems surprising that science functions at all. In 2005, medical science was shaken by a paper with the provocative title "Why most published research findings are false."1 Written by John Ioannidis, a professor of medicine at Stanford University, it didn't actually show that any particular result was wrong. Instead, it showed that the statistics of reported positive findings was not consistent with how often one should expect to find them. As Ioannidis concluded more recently, "many published research findings are false or exaggerated, and an estimated 85 percent of research resources are wasted."2 It's likely that some researchers are consciously cherry-picking data to get their work published.

British universities are being stripped of artificial intelligence (AI) experts in a brain drain to the private sector that is hampering research and disrupting teaching at some of the country's leading institutions. Scores of talented scientists have left or passed up university posts for salaries two to five times higher at major technology firms, where besides getting better pay, new recruits can take on real-world problems with computer power and datasets that academia cannot hope to provide. The impact of the hiring frenzy is revealed in a confidential Guardian survey of the UK's elite Russell Group universities, which found that many top institutions were struggling to keep up with the demand from tech firms that are aggressively expanding their AI research groups. One university executive said AI researchers were courted by industry on a routine basis and that departments regularly missed out on the best talent when companies made better offers. "We need top quality staff to teach and research and the implications of not achieving this don't need to be spelt out," the executive told the Guardian.

Banks and hedge funds that rely on artificial intelligence threaten to inject risks into the financial system that could exacerbate a future crisis, according to global regulators. The financial industry's rush to adopt AI raises the potential that firms will become overly dependent on technologies that herd them toward the same view of risks and could "amplify financial shocks," according to a study published on Wednesday by the Financial Stability Board, a panel of regulators that includes the U.S. Federal Reserve and European Central Bank. "AI and machine learning applications show substantial promise if their specific risks are properly managed," the FSB said in a report that called for additional monitoring and testing of robotic technologies designed to lessen human involvement. "Taken as a group, universal banks' vulnerability to systemic shocks may grow if they increasingly depend on similar algorithms or data streams." The FSB, headed by Bank of England Governor Mark Carney, said that many of the technologies are being designed and tested in a period of low volatility in financial markets, and, as a result, "may not suggest optimal actions in a significant economic downturn or in a financial crisis."