Oxygen on-board future space missions will be made from the recycled breath of astronauts. The Advanced Closed Loop System (ACLS) has been built by the European Space Agency (ESA) and is now being installed on-board the orbiting spacecraft. The apparatus recycles half the carbon dioxide (CO2) exhaled by astronauts and converts it into oxygen. Scientists have heralded the invention as an important step towards long-term missions to mars and beyond. ESA astronaut Alexander Gerst poses with the ACLS life-support rack, newly installed on the International Space Station.
In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation. This enables us to study the relative importance of each interaction block for property prediction. We demonstrate on both the QM9 and MD17 dataset that their relative weighting depends strongly on the chemical composition and configurational degrees of freedom of the molecules which opens the path towards a more detailed understanding of machine learning models for molecules.
Body odor is a stubborn problem. Sensors and the computing attached to them struggle to perceive armpit odors in the way humans do, because B.O. is really a complex mix of dozens of gaseous chemicals. The UK's PlasticArmPit project is designing the first machine learning–enabled flexible plastic sensor chip. Its target audience: those who think they might stink. The prototype chip will be manufactured and tested in 2019.
Science hasn't been giving us a tremendous amount of good news these days. We've screwed up the environment so badly, it's hard to even call it an environment anymore. And that's coming back to bite (or sting) us: Bee populations, which we rely on to pollinate our crops, are plummeting. But science is also coming to the rescue, by gluing QR codes to bumblebees' backs and tracking their movements with a robotic camera. Researchers have created a system that tracks individual bees as well as the dynamics of whole colonies exposed to imidacloprid, a neurotoxin that belongs to the infamous neonicotinoid group of pesticides.
Your 5G iPhone is unlikely to appear until 2020, an asteroid mining company gets some help from a new Blockchain owner, and drones get smarter at search and rescue. It's a match made in 2018. Planetary Resources just took an unusual turn on its path to asteroid mining -- selling itself to a blockchain company founded by Ethereum's Joe Lubin. Planetary Resources' Brian Israel said that Blockchain was a "natural solution" for commerce in space and an ideal way for people from various countries to coordinate efforts. It also adds some crucial funding to the space mining company, which had recently laid off employees.
A drone company spun out of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) has collected AU$3.5 million in a funding round led by the CSIRO Innovation Fund, and joined by mining executive Andy Greig. Emesent will use the cash to commercialise its Hovermap product, which uses a drone and lidar to autonomously create 3D maps for underground areas, and grow its staff to 25 people. "Hovermap enables the mining industry to safely inspect inaccessible areas of underground mines while improving the type and quality of data collected to unlock new insights," Dr Stefan Hrabar, co-founder and CEO of Emesent, said. "The data we gather improves a mine's productivity and provides a better understanding of conditions underground, all without sending surveyors and miners into potentially hazardous areas." Hovermap is already used in Australia, the United States, Canada, China, and Japan, and last year completed a beyond line-of-sight drone flight in a mine 600 metres below the surface in Western Australia.
Since the concept of "machines learning" was introduced in the 1950s, the field has gone from a cryptic domain understood by a few (Turing, Markov, Legendre, Laplace or Bayes) to a technology that every company must deploy. Every day we hear how data and automation improve our shopping experiences, our online searches and enables fraud prevention and cybersecurity routines to do more, faster and better for us. Now, the amalgamates created around Artificial Intelligence, Machine Learning and Big Data are bound to confuse industry observers or investors who aren't familiar with the technical details. If you're asking yourself: "What's the difference between Big Data and Machine Learning?", then for the sake of my piece, simply think about it this way: "Big Data is Machine Learning's great uncle". Machine Learning doesn't need Big Data to exist.
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.