For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally finds an Approximate NewTon (ANT) direction, which is sent to the main driver. The main driver, then, averages all the ANT directions received from workers to form a Globally Improved ANT (GIANT) direction. GIANT is highly communication efficient and naturally exploits the trade-offs between local computations and global communications in that more local computations result in fewer overall rounds of communications. Theoretically, we show that GIANT enjoys an improved convergence rate as compared with first-order methods and existing distributed Newton-type methods. Further, and in sharp contrast with many existing distributed Newton-type methods, as well as popular first-order methods, a highly advantageous practical feature of GIANT is that it only involves one tuning parameter. We conduct large-scale experiments on a computer cluster and, empirically, demonstrate the superior performance of GIANT.

For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally finds an Approximate NewTon (ANT) direction, which is sent to the main driver. The main driver, then, averages all the ANT directions received from workers to form a Globally Improved ANT (GIANT) direction. GIANT is highly communication efficient and naturally exploits the trade-offs between local computations and global communications in that more local computations result in fewer overall rounds of communications. Theoretically, we show that GIANT enjoys an improved convergence rate as compared with first-order methods and existing distributed Newton-type methods. Further, and in sharp contrast with many existing distributed Newton-type methods, as well as popular first-order methods, a highly advantageous practical feature of GIANT is that it only involves one tuning parameter. We conduct large-scale experiments on a computer cluster and, empirically, demonstrate the superior performance of GIANT.

New Scientist Live, the world's greatest festival of science, finished yesterday after three days of mind-expanding talks and exhilarating experiences. Thousands of people attended each day, meeting robots, trying cutting-edge virtual reality set-ups and learning everything from whether science can save humanity to the design flaws in the human body. Most importantly, we had an amazing time. Here are nine incredible things we learned there. We heard Gillian Forrester explain that we may be able to shed light on the longstanding mystery of how humans evolved the ability to speak by studying these great apes.

For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally finds an Approximate NewTon (ANT) direction, which is sent to the main driver. The main driver, then, averages all the ANT directions received from workers to form a Globally Improved ANT (GIANT) direction. GIANT is highly communication efficient and naturally exploits the trade-offs between local computations and global communications in that more local computations result in fewer overall rounds of communications. Theoretically, we show that GIANT enjoys an improved convergence rate as compared with first-order methods and existing distributed Newton-type methods.

Unmanned aerial vehicles (UAVs), more commonly known as drones, are growing at a rapid rate for both consumer and professional markets. Market research firm IHS Markit forecasts the professional drone market will manage a compound annual growth rate (CAGR) of 77.1% through 2020 driven by industries such as agriculture, energy and construction using the technology for surveying, mapping, planning and more. Meanwhile, the consumer drone market will maintain a CAGR of 22.1% through 2020 with companies such as DJI, Parrot and 3D Robotics driving the market with a wide range of devices for photography, recreational use and racing. While these markets will be the main drivers for the next few years, one industry that isn't discussed often as a main driver is the insurance market. However, according to professional services company PwC, the addressable market of drone powered solutions in the insurance industry is valued at $6.8 billion.

Retail companies will invest about 2.76 billion euro in artificial intelligence this year. According to research firm IDC, they are the main driver for AI worldwide, ahead of the banking sector. An estimated 19.1 billion dollar (15.5 billion euro) will be spent on cognitive and AI systems in 2018. That is more than 50 % (54.2 % to be exact) more than last year, according to research firm IDC. It says that 40 % of every digital transformation initiative will use AI in 2019 and that number will grow to 75 % in 2021. Remarkable:the retail sector will surpass the banking industry and spend the most on artificial intelligence in 2018.

The market for AI has seen a significant rise in the last few years with the advent of blockchain and the evolution of ever more sophisticated constructs aimed at solving complicated business tasks and optimizing costs. GraphGrail Ai means to storm the market, inducing it to grow even further with the arrival of healthy competition to challenge the industry giants. According to IDC analysts, for a third of Fortune 500 companies, revenues from information products will grow by 50% by the end of 2017 compared to the rest of the range of products and services. An important source of revenue will be the monetization of data. The amount of data created in the world (10 zettabytes in 2015) will grow to 163 zettabytes by 2025.

Financial services jobs go in and out of fashion. In 2001 equity research for internet companies was all the rage. In 2006, structuring collateralised debt obligations (CDOs) was the thing. In 2010, credit traders were popular. In 2014, compliance professionals were it.

KPMG recently conducted its annual survey of 800 global technology industry leaders, from startups to Fortune 500. The results are the top 3 emerging technology trends that are expected to disrupt business significantly in the next three years. Some 41 percent of respondents identified these three below. Take a closer look at and follow any new developments there, as your business will be disrupted by these technologies sooner than you think. This is the leading trend, with 20 percent of respondents identifying it as driving business transformation.

Financial services jobs go in and out of fashion. In 2001 equity research for internet companies was all the rage. In 2006, structuring collateralised debt obligations (CDOs) was the thing. In 2010, credit traders were popular. In 2014, compliance professionals were it.