One of Bill Gates' Arizona-based investment firms, Belmont Partners, just purchased close to 25 thousand acres of land in Tonoph, Arizona for $80 million dollars to develop a "smart city!" Veuer's Chandra Lanier has the story. Bill Gates visiting an agricultural facility in Adama, Ethiopia, that processes, cleans, bags and ships white pea beans, red kidney beans, and chickpeas grown in Ethiopia to European markets. Tech pioneer Bill Gates thinks the U.S. can keep its historically influential role as a global leader. But for a second year in a row, he cautioned that the nation risks losing its geopolitical clout if the Trump administration succeeds in slashing foreign aid, as proposed Monday in a new federal budget that prioritizes a jump in military spending. Last year, the White House tried to reduce foreign aid by one-third, but Congress did not approve the cuts.

Tech giants have made some major strides in advancing facial recognition technology. But a new study, called'Gender Shades,' has found that it may not be working for all users, especially those who aren't white males. A researcher from the MIT Media Lab discovered that popular facial recognition services from Microsoft, IBM and Face vary in accuracy based on gender and race. A researcher from MIT tested popular facial recognition services and found that they experienced more errors when the used was a dark-skinned female. To illustrate this, researcher Joy Buolamwini created a data set using 1,270 photos of parliamentarians from three African nations and three Nordic countries. The faces were selected to represent a broad range of human skin tones, using a labeling system developed by dermatologists, called the Fitzpatrick scale.

Matrix and tensor decomposition is a fundamental technique in machine learning to analyze data represented in the form of multidimensional arrays, which is used in a wide range of applications such as computer vision (Vasilescu and Terzopoulos, 2002, 2007), recommender systems (Symeonidis, 2016), signal processing (Cichocki et al., 2015), and neuroscience (Beckmann and Smith, 2005). The current standard approaches include NMF (nonnegative matrix factorization) (Lee and Seung, 1999, 2001) for matrices and CANDE-COMP/PARAFAC (CP) decomposition (Harshman, 1970) or Tucker decomposition (Tucker, 1966) for tensors. CP decomposition compresses an input tensor into a sum of rank-one components and Tucker decomposition approximates an input tensor by a core tensor multiplied by matrices. To date, matrix and tensor decomposition has been extensively analyzed and there are a number of variations of such decompositions (Kolda and Bader, 2009), where the common goal is to approximate an given tensor by a smaller number of components, or parameters, in an efficient manner. Despite the recent advances of decomposition techniques, a statistical theory that can systematically define decompositions for any order tensors including vectors and matrices is still under development. Moreover, it is well known that CP and Tucker tensor decompositions include non-convex optimization and the global convergence is not guaranteed. Although there are a number of extensions to transform the problem into convex (Liu et al., 2013; Tomioka and Suzuki, 2013), one needs additional assumptions on data, such as a bounded variance. Here we present a new paradigm of matrix and tensor decomposition, called Legendre decomposition, based on the information geometry (Amari, 2016), which solves the above open problems of matrix and tensor decompositions.

LONDON, Feb 12 (Reuters) - "Chocfinger" made his name and his money by taking bold bets on cocoa markets. But after nearly four decades of trading, sometimes winning, sometimes losing, Anthony Ward threw in the towel. Ward blames the rise of computer-driven funds and high-frequency trading for forcing him and some other well-known commodities investors to close their hedge funds and look for opportunities where machines can't make a difference. It was in January 2016, after a slide in cocoa prices, that Ward decided the days of traditional commodity investors doing well from taking positions based on fundamentals such as supply and demand may be numbered. "It was just too big, too quick, too dramatic. And completely against the fundamentals," Ward told Reuters.

Artificial intelligence is set to swell the GCC and Egypt's economies to the tune of $320 billion by 2030, according to a report. Globally, the economic uplift could be to the magnitude of $15.7 trillion, more than the current output of China and India combined, according to a report by professional services firm PwC. Within that increase, $6.6 trillion is likely to come from increased productivity, while $9.1 trillion is likely to come from benefits to consumers. Artificial intelligence (AI) is a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they are sensing and their objectives. AI is rapidly evolving, with current technology including autopilots, digital assistants and chatbots.

A significant number of South African startups have revolutionized in 2017 and embraced the Artificial Intelligence (AI) related technologies in their software. The list below contains some South African startups that have either potentially disruptive technologies using AI or developed cutting AI solutions. The Cape Town-based startup was established in 2013 by Daniel Schwartzkopff and Francis Cronje. The startup gives consulting and product development services to some industries from law to finance. The startup was founded in 2011 by Dayne, Ryan Falkenberg, and Mark Pederson.

LONDON: Artificial intelligence is set to swell the GCC and Egypt's economies to the tune of $320 billion by 2030, according to a report.Globally, the economic uplift could be to the magnitude of $15.7 trillion, more than the current output of China and India combined, according to a report by professional services firm PwC. Within that increase, $6.6 trillion is likely to come from increased productivity, while $9.1 trillion is likely to come from benefits to consumers....

LONDON: Artificial intelligence is set to swell the GCC and Egypt's economies to the tune of $320 billion by 2030, according to a report. Globally, the economic uplift could be to the magnitude of $15.7 trillion, more than the current output of China and India combined, according to a report by professional services firm PwC. Within that increase, $6.6 trillion is likely to come from increased productivity, while $9.1 trillion is likely to come from benefits to consumers. Artificial intelligence (AI) is a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they are sensing and their objectives. AI is rapidly evolving, with current technology including autopilots, digital assistants and chatbots.

In a time when data has been labelled'the new oil', businesses are scrambling to implement effective forward-thinking data management strategies that can deliver real-time insights and business value to decision-makers to drive business strategy, increase revenue, and grow profits. Data modellers have become indispensable assets to enterprises wishing to leverage their data to drive competitive advantage. However, there is often a disconnect between the data modellers analysing and extracting value from data, and the business decision-makers who need to utilise data as a strategic asset to drive business outcomes. Historically, businesses owned vast amounts of structured and unstructured data in their ERP, transactional, and other business systems, which was brought together in a data warehouse. Here, a range of different data modelling tools, from the conceptual (showing relationships between different entities) to the logical (looking at certain attributes within the data) and physical (referring to how data is represented and stored using a database management system) were applied to create a framework within which analysts could extract business value.

The United Nations Development Programme (UNDP) helps countries implement the United Nations (UN) Sustainable Development Goals (SDGs), an agenda for tackling major societal issues such as poverty, hunger, and environmental degradation by the year 2030. A key service provided by UNDP to countries that seek it is a review of national development plans and sector strategies by policy experts to assess alignment of national targets with one or more of the 169 targets of the 17 SDGs. Known as the Rapid Integrated Assessment (RIA), this process involves manual review of hundreds, if not thousands, of pages of documents and takes weeks to complete. In this work, we develop a natural language processing-based methodology to accelerate the workflow of policy experts. Specifically we use paragraph embedding techniques to find paragraphs in the documents that match the semantic concepts of each of the SDG targets. One novel technical contribution of our work is in our use of historical RIAs from other countries as a form of neighborhood-based supervision for matches in the country under study. We have successfully piloted the algorithm to perform the RIA for Papua New Guinea’s national plan, with the UNDP estimating it will help reduce their completion time from an estimated 3-4 weeks to 3 days.