A few years ago I saw this headline news flashing all over the internet. Our dealers are missing up to $18 billion in easy sales. The Chairman and CEO of Caterpillar suggested that the company and its dealers were losing $9 - 18 billion in easy sales revenue as their sales, both internal and dealer networks, weren't monetising the real value of data. They are not tapping into the wealth of real-time customer data now at their fingertips; they are not communicating with each other; and they are not providing customers across the globe with a consistent experience when it comes to everything from e-commerce to parts and services pricing. Long story short, the whole idea was to convert the company's mentality from dumb iron sales to data-driven, machine learning-driven sales.

The mining industry continues to face volatile commodity prices, safety and environmental concerns, and decreasing productivity savings amongst others. Artificial intelligence (AI) related technologies have the potential to bring tangible benefits for mining organizations like enhanced operational efficiency and improved safety and health conditions. What are the key challenges that arise when deploying AI within the mining industry and how can mining companies prepare to adopt AI? Read the report to understand how mining companies are already using AI‑related technologies. There are also many useful lessons for other asset intensive organisations.

Perfumers look out: IBM Research partnered up with one of the top producers of flavors and fragrances, Symrise, to create an perfume-concocting AI. Named Philyra, after the Greed goddess of fragrance, it uses machine learning to sift through thousands of ingredients, formulas and industry trends to derive what IBM considers to be unique combinations. IBM is leveraging the AI to help perfumers design the next great scent rather than a machine that will replace experts of the human nose. Philyra looks at thousands of formulas and raw materials to identify patterns and new combinations to find a potential gap in the market and fill it with a new scent. It finds alternative raw materials, deduces the dosage based on human usage patterns and how humans tend to respond before comparing it to existing fragrances.

Skilled perfumers bring art and science together to design new fragrances, a talent that takes ten or more years to develop. Crafting a fragrance that leaves an impression is one of the most important components a consumer considers when forming a positive or negative opinion about everyday products like laundry detergent, deodorant, air freshener and, of course, cologne and perfume. What if artificial intelligence (AI) could learn from these professionals to augment the process of developing new fragrances or identify completely novel creative pathways? With this in mind, my team at IBM Research, together with Symrise, one of the top global producers of flavors and fragrances, created an AI system that can learn about formulas, raw materials, historical success data and industry trends. Building on previous IBM research using AI to pair flavors and for recipe creation, as well as our new IBM Research AI for Product Composition, we created Philyra.

Nowadays, the contemporary manufactured and small unmanned aerial vehicles (UAVs) known as drones are mostly electric-based, using electric engines for their flight power. The application of such propulsion systems need proper elaboration of efficient and light electric energy sources. The paper tends to shift our approach to drones towards one that will see efficient energy storage through the use of hydrogen – which is outlined in the following sections of this article. Speaking of, there are primarily two methods of on-board energy storing in today's drone system: The second method is one on which we are focusing in this article – mostly because of the complexity of the fuel cells and their constant need for the supply of hydrogen. Currently, hydrogen can be stored in compressed state in pressure bottles or in its liquid state (in cryogenic tanks).

This example of programmable architecture uses lightweight materials and drones to help it adapt to environmental changes. Digital fabrication and automation is changing the way we build, allowing for cutting-edge concepts to take form through computer-aided design tools and integrating robotics into building techniques. Three graduate students over at University of Stuttgart's Institute for Computational Design and Construction (ICD) and Institute of Building Structures and Structural Design (ITKE) recently unveiled a modular architectural canopy that can be reconfigured in real-time, using drones. Dubbed Cyber Physical Macro Material, the 2.5-metre (8.2-foot) high canopy is designed as a "new dynamic (and intelligent) agile architecture for public spaces," which can respond to weather conditions. Built with lightweight carbon fibre filament, magnets and a variety of sensors and processors, the canopy demonstrates the possibility of'live' construction processes, facilitated by unmanned aerial vehicles (UAVs).

They then synthesized and tested one of the compounds predicted computationally -- sodium-barium-borate -- and determined it offers 95 percent efficiency and outstanding thermal stability. Jakoah Brgoch, assistant professor of chemistry, and members of his lab describe the work a paper published Oct. 22 in Nature Communications. The researchers used machine learning to quickly scan huge numbers of compounds for key attributes, including Debye temperature and chemical compatibility. Brgoch previously demonstrated that Debye temperature is correlated with efficiency. LED, or light-emitting diode, based bulbs work by using small amounts of rare earth elements, usually europium or cerium, substituted within a ceramic or oxide host -- the interaction between the two materials determines the performance.

A few years ago I saw this headline news flashing all over the internet. Our dealers are missing up to $18 billion in easy sales. The Chairman and CEO of Caterpillar suggested that the company and its dealers were losing $9 - 18 billion in easy sales revenue as their sales, both internal and dealer networks, weren't monetising the real value of data. They are not tapping into the wealth of real-time customer data now at their fingertips; they are not communicating with each other; and they are not providing customers across the globe with a consistent experience when it comes to everything from e-commerce to parts and services pricing. Long story short, the whole idea was to convert the company's mentality from dumb iron sales to data-driven, machine learning-driven sales.

Researchers from the University of Houston have devised a new machine learning algorithm that is efficient enough to run on a personal computer and predict the properties of more than 100,000 compounds in search of those most likely to be efficient phosphors for LED lighting. Jakoah Brgoch, assistant professor of chemistry, and members of his lab describe the work a paper published Oct. 22 in Nature Communications. The researchers used machine learning to quickly scan huge numbers of compounds for key attributes, including Debye temperature and chemical compatibility. Brgoch previously demonstrated that Debye temperature is correlated with efficiency. LED, or light-emitting diode, based bulbs work by using small amounts of rare earth elements, usually europium or cerium, substituted within a ceramic or oxide host--the interaction between the two materials determines the performance.

The UCR Time Series Archive - introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one dataset from the archive. The original incarnation of the archive had sixteen datasets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 datasets to 85 datasets. This paper introduces and will focus on the new data expansion from 85 to 128 datasets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline (1-Nearest Neighbor classification), a large fraction may be misattributing the reasons for their improvement. Moreover, they may have been able to achieve the same improvement with a much simpler modification, requiring just a single line of code.