We introduce a generic scheme for accelerating gradient-based optimization methods in the sense of Nesterov. The approach, called Catalyst, builds upon the inexact acceler- ated proximal point algorithm for minimizing a convex objective function, and consists of approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. One of the key to achieve acceleration in theory and in practice is to solve these sub-problems with appropriate accuracy by using the right stopping criterion and the right warm-start strategy. In this paper, we give practical guidelines to use Catalyst and present a comprehensive theoretical analysis of its global complexity. We show that Catalyst applies to a large class of algorithms, including gradient descent, block coordinate descent, incremental algorithms such as SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these methods, we provide acceleration and explicit sup- port for non-strongly convex objectives. We conclude with extensive experiments showing that acceleration is useful in practice, especially for ill-conditioned problems.

Drones are sweeping over the global mining industry and for good reason. The future of mining will increasingly rely on the use of drones and automated systems, slashing costs while helping mining companies find and dig up more gold, silver and other metals and minerals.

If farmers want to know how healthy crops are, perhaps they shouldn't trust their eyes. Matt Free -- a manager at Evergreen FS, an agriculture company -- learned that lesson this year. His team provides crop protection services such as fertilizers and herbicides to farmers across Illinois. After a year-long test of a variety of new technologies, Evergreen FS found artificial intelligence could identify trouble, such as fungus growth and water shortages, in corn and soybean crops weeks before the naked eye would ever realize it. The tech, which comes from startup Ceres Imaging, offers farmers an AI analysis of photos taken from planes flying several thousand feet above fields. Previously, the technology was only available for orchards and vineyards.

When considering a startup, especially an early-stage startup, investors want to conduct as much due diligence as possible. What little data they can gather is scattered all over different sources including Crunchbase, LinkedIn, Pitchbooks, company websites, etc. Consolidating this data takes a great amount of time and effort. Furthermore, the data sets can be incomplete or biased depending on the search queries -- imagine overlooking a keyword. To make the due diligence process fairer and less cumbersome for investors, various platforms are using machine learning (ML) to pull together information about startups from all available resources to help investors assess companies and investment opportunities. But where machine learning really shines is in the interplay of data-driven insights that are qualified by human intuition and personal experience.

Did you ever use spell check in google? If you have then you used a machine learning algorithm. There are countless instances in an average person's day where he/she uses machine Learning. It has become a vital component in modern men's life. Driver less cars, Rovers in Mars, Weather predictions, Market share predictions, Speech Processing, Internet of things, Healthcare well these are just the tip of the iceberg.

BHP is applying data science to understand how it services machines located across its mines, in the hope of saving $79 million this financial year alone. The miner revealed plans late last year to set up a maintenance centre of excellence (MCoE) based out of Brisbane. The MCoE will standardise maintenance systems and processes for BHP's worldwide operations, replacing the previous model of having 40 different maintenance organisations globally, each with its own way of working. One of the keys to the MCoE model is its reliance on data science techniques, such as machine learning, to understand how maintenance is performed at each site and where improvements can be made. Like other projects since BHP relaunched its technology function at the start of this year, the idea with the MCoE is to create repeatable processes for its business operations across the world.

The digital revolution has brought with it a new way of thinking about manufacturing and operations. Emerging challenges associated with logistics and energy costs are influencing global production and associated distribution decisions. Significant advances in technology, including big data analytics, AI, Internet of Things, robotics and additive manufacturing, are shifting the capabilities and value proposition of global manufacturing. In response, manufacturing and operations require a digital renovation: the value chain must be redesigned and retooled and the workforce retrained. Total delivered cost must be analyzed to determine the best places to locate sources of supply, manufacturing and assembly operations around the world.

Health Catalyst believes machine learning is the life-saving technology that will transform healthcare. Machine learning challenges the traditional, reactive approach to healthcare. In fact, it's the exact opposite: predictive, proactive, and preventative--life-saving qualities that make it a critically essential capability in every health system. Health Catalyst is on a mission to help health systems save lives by making machine learning routine, actionable, and pervasive through catalyst.ai Some may ask whether machine learning is just a technology fad or whether it will provide true value in healthcare.

Data mining isn't a new invention that came with the digital age. The concept has been around for over a century, but came into greater public focus in the 1930s. According to Hacker Bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers. Forbes also reported on Turing's development of the "Turing Test" in 1950 to determine if a computer has real intelligence or not. To pass his test, a computer needed to fool a human into believing it was also human.

'Fear and Loathing In the Age of QE … AI' is a presentation given at Mining Investment London earlier this week. Stephen Flood, CEO of GoldCore presentation (28 minutes) was well received at the conference which is a strategic mining and investment conference for leaders in the mining and investment sectors, bringing together attendees from 20 countries. 'Fear and Loathing In the Age of QE … AI' can be watched on Youtube here Why Silver Bullion Is Set To Soar – GoldCore Interview Gold Bullion Stored In Singapore Is Safest – Marc Faber Russia Seen More Likely to Sell Dollar Rather Than Gold Talking Gold with CNN's Richard Quest Gold holds near one-week low as dollar firms (Reuters.com) Goldman Says the Bitcoin Haters Just Don't Get It (Bloomberg.com) Goldman Warns That Market Valuations Are at Their Highest Since 1900 (Bloomberg.com)