Cognovi Labs is a new analytics startup that relies on Twitris, a Wright State University-developed tool that claims to be able to take a sample of social media chatter about a specific topic and deduce real-time, large-scale, automated sentiment about the specific topic they are researching. As a real-world example of the tool's capability, the Cognovi Labs research team -- led by Wright State University researcher (and Cognovi Labs inventor) Dr. Amit Sheth -- analyzed Twitter chatter leading up to the Great Britain/European Union Membership Referendum (Brexit) on June 23. The team was able to predict some six hours before the news broke that the polls leaning toward the "remain" camp were incorrect. This was predicted by running Twitter chatter through the Cognovi Labs Twitris tool. The machine learning tool leverages Cognovi Labs' semantic intellectual property to be able to automate and extract aggregate meaning from social media chatter (including slang) in new, more precise ways.

Picture the scenario: you've been asked to prepare an analysis on whether you have the best people in the right roles in your company and identify where there may be knowledge gaps within the organisation. If your company has offices in New York, London, Berlin and Singapore, that's a huge HR challenge. But what if an artificial intelligence tool can produce in minutes a detailed "knowledge map" based on analysis of employee skills and interests to pinpoint gaps where new hires are needed to fill those holes. British companies are now being offered such "brain technology". Computer software, called Starmind, uses machine learning to understand queries – even anonymously – then source answers from previous staff conversations on a subject or track down experts within the company who are able to help.

WASHINGTON/SAN FRANCISCO – Tesla Motors Inc. says the self-driving feature suspected of being involved in a fatal crash on May 7 is experimental, yet it's been installed on all 70,000 of its cars since October 2014. For groups that have lobbied for stronger safety rules, that's precisely what's wrong with U.S. regulators' increasingly anything-goes approach. "Allowing automakers to do their own testing, with no specific guidelines, means consumers are going to be the guinea pigs in this experiment," said Jackie Gillan, president for Advocates for Highway and Auto Safety, a longtime Washington consumer lobbyist who has helped shape numerous auto-technology mandates. "This is going to happen again and again and again." Tesla's use of a technology still in development, while common in its Silicon Valley home, contrasts with the cautious method of General Motors Co. and other automakers that have restricted their semi-autonomous cars to test tracks and professional drivers.

Fintech (financial technology) has arguably become the poster child for the UK technology sector over the last couple of years but the spectacular collapse of Powa Technologies did nothing for the sector's reputation. When asked whether Powa cast a shadow on the UK's thriving fintech industry, Tom Hulme, a partner at Google Ventures in London, said: "It would break my heart if Powa Technologies corrupted the fintech marketplace." Powa raised at least 175 million ( 122 million) but it had only 250,000 ( 175,200) in the bank at the start of February and debts of 16.4 million ( 11.5 million). Here's how it spent all of its investor's money. Hulme was quick to add that Powa was not the type of company that Google Ventures would look at.

This year 200 million viewers cheered to to the dance of three robots to Michael Jackson s Thriller and Beyonce s Single Ladies during the Eurovision Song Contest in Stockholm. In the past few years we have seen them entering new areas of life, such as logistics or agriculture. While some of that has been greeted with excitement, there is a lot of skepticism and fear associated with the advent of Advanced Robotics (I will go into a definition in a second). This post is an attempt to provide an accurate representation of the current state of Advanced Robotics and how the space is likely to develop. As a venture capitalist I usually look at tech trends through the prism of young, fast-growing startups.

The World Economic Forum has put together a list of the top 10 emerging technologies that will change our lives. The list includes nanosensors that will circulate through the human body, a battery that will be able to power an entire town and socially aware artificial intelligence that will track our finances and health. These are not far-flung visions, according to the forum. They are technologies that are on the cusp of having a meaningful impact. "Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors," said Bernard Meyerson, chairman of the World Economic Forum council that compiled the list of the top 10 emerging technologies in 2016.

We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the estimation of the joint eigenstructure of a set of matrices, with applications in signal processing, linear algebra, and tensor decomposition. By assuming the input matrices to be perturbations of noise-free, simultaneously diagonalizable ground-truth matrices, the approximate joint triangularizers are expected to be perturbations of the exact joint triangularizers of the ground-truth matrices. We provide a priori and a posteriori perturbation bounds on the `distance' between an approximate joint triangularizer and its exact counterpart. The a priori bounds are theoretical inequalities that involve functions of the ground-truth matrices and noise matrices, whereas the a posteriori bounds are given in terms of observable quantities that can be computed from the input matrices. From a practical perspective, the problem of finding the best approximate joint triangularizer of a set of noisy matrices amounts to solving a nonconvex optimization problem. We show that, under a condition on the noise level of the input matrices, it is possible to find a good initial triangularizer such that the solution obtained by any local descent-type algorithm has certain global guarantees. Finally, we discuss the application of approximate joint matrix triangularization to canonical tensor decomposition and we derive novel estimation error bounds.

The Budapest BI Forum is the leading vendor-independent business intelligence and analytics conference in Hungary. One of the main feature of the event is the multi-subject approach: we have Pydata, Rstats, Data visualization, machine learning and BI talk in the 2 day s of the conference. This way speaking at the Budapest BI Forum in any of the tracks also provides a special chance to learn about other interesting fields of BI and analytics. This year we will have the following track, with a separate CFP for each. All speakers are welcome to submit more than one talks to the same or to different tracks.

It's your nighttime routine: You drop your phone onto the nightstand charging pad, and it asks about your day. You tell it, talking to the virtual personal assistant just like you'd talk to a friend. Your phone's artificial intelligence knows you almost as well as you know yourself (maybe even better). So when it suggests ways to get through tomorrow's calendar, you trust its advice. AI is practically everywhere, and getting smarter all the time.

Industrial robots used to be dumb, somewhat inflexible, and mostly blind - but also fast, precise and very efficient. As the cost of components, sensors and vision systems has been dropping, vision-enabled robots are becoming more prevalent and capable, and the industry is dramatically changing. Those changes can be seen in recent trends in China, investments in and acquisitions of robotic companies, by an analysis of recent startup companies, new and widening application areas for robot use, and technological developments. For the past 50 years industrial robots have picked the low-hanging fruit of manufacturing by handling the dull, dirty and dangerous tasks. But today, as consumers want more personalized products, and want them faster, and as costs have dropped and executives have pushed for greater productivity through automation, mobile and vision-enabled robots are emerging and being deployed in many new application areas, particularly for SMEs and in logistics, but also in government, agriculture, surveying, construction and healthcare.