Today, the recycling market is changing rapidly due to global changes where the quality requirements of the incoming and outgoing material are increased. As a result, systems that are separating waste material from the target material need to be improved continuously to cope with this change. Stora Enso's Langerbrugge Mill in northwest Belgium, which is one of the largest paper mills in Europe, required a more effective paper-cardboard sorting solution and technology that can easily be retrained for anomaly detection. This technology was developed by Robovision, a company specializing in deep learning-based machine vision and robot programming, and Imec, which is the world-leading R&D and innovation hub in nanoelectronics and digital technologies within the framework of the AUTOWARE project. A big challenge in paper recycling is the separation of cardboard and waste materials from paper.

Proteins, the fundamental nanomachines of life, have provided scientists like me with many lessons in our own efforts to create nanomachinery. Proteins are large molecules containing hundreds to thousands of atoms and are typically a few nanometers (billionths of a meter) to tens of nanometers across. Our bodies contain at least 20,000 different proteins that, among other things, cause our muscles to contract, digest our food, build our bones, sense our environment and tirelessly recycle hundreds of small molecules within our cells. As a chemistry undergraduate in 1986, I dreamed of the possibility of designing and synthesizing macromolecules (molecules containing more than 100 atoms) that could do the amazing things that proteins do and more. I have programmed computers since the first TRS-80s came out in the late 1970s, and I thought it would be wonderful if I could build complex molecular machines as easily as I could write software. I wanted to create a programming language for matter--a combination of software and chemistry that would enable people to describe a nanomachines shape and would then determine the series of chemical processes that a chemist or a robot should carry out to build the nanodevice. Unfortunately, the idea of inventing nanomachines by designing new proteins runs into a severe obstacle.

On a meetup that I attended a couple of months ago in Sydney, I was introduced to an online machine learning course by fast.ai. I never paid any attention to it then. This week, while working on a Kaggle competition, and looking for ways to improve my score, I came across this course again. I decided to give it a try. Here is what I learned from the first lecture, which is a 1 hour 17 minutes video on INTRODUCTION TO RANDOM FOREST.

A lot of nat gas analysts would at times reference EIA's Nuclear Capacity Outage (NCO henceforth), yet I haven't seen anyone do a detailed explanation of how they apply it toward an objective bias in implied Nat Gas demand, i.e. Fair Value bias going forward expected by traders paying attention to NCO. So I got curious, and first look at NG prices vs. YOY change in NCOs: So it looks like there is likely somewhat of a rough relationship, that some traders are paying attention to it. Then the next step would be an attempt toward precision via Time Series Analysis. So, what I'd do here is a 2 Step Machine Learning process of 1) Forecast expected NCO for the rest of 2019, then apply that to estimate Natural Gas futures fair value bias going forward.

Cologne – LANXESS is broadening its use of artificial intelligence (AI) in product development. The specialty chemicals company has launched a project aimed at expanding its range of prepolymers. The goal is to offer customers tailor-made polyurethane systems with even shorter lead times, including for entirely new applications with different requirements. The Urethane Systems business unit is using the potential of AI and has brought materials AI company Citrine Informatics on board as a project partner. LANXESS data specialists and process experts used the Citrine Platform for artificial intelligence to add further data points to the company's formulation database.

Brazil's mining major Vale is set to start applying machine learning to identify new drilling targets at its Coleman nickel mine. Coleman Mine, which is the flagship asset of Vale in Ontario, Canada, is part of the company's base metals operations. Vale has selected technology company GoldSpot Discoveries to examine and analyse the vast amount of data acquired by it over decades of mining at Coleman. GoldSpot Discoveries' team of geologists and data scientists will also discover previously unrecognised data trends, which may point to unknown areas of in-depth mineralisation. By using its geoscience and machine science expertise, GoldSpot Discoveries' team will clean, unify and analyse exploration data from Vale's Coleman Mine.

In the workplace of the not-too-distant future, employees will need to go beyond being tech-savvy to being able to comfortably work alongside digital colleagues. Artificial intelligence (AI), machine learning and intelligent bots will be automatically making decisions to streamline business processes and empower efficient automation. The widespread adoption of machines to do much of the "heavy lifting" will change some jobs from the inside out, making individual workers far more productive and less bogged down with repetitive tasks. Smart chatbots can already handle first- and even second-level customer service calls, and AI is powering everything from manufacturing lines to automated vehicles. For example, BHP is rolling out automated trucks at its iron ore and coal mines across Australia over the next 5 years, following the success of its Jimblebar mine trial program, which saw a 90 per cent reduction in the number of dangerous incidents.

We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of existing stochastic simulators. Unlike traditional approaches to surrogate modeling, our surrogates retain the interpretable structure of the reference simulators. The particular way we achieve this allows us to replace the reference simulator with the surrogate when undertaking amortized inference in the probabilistic programming sense. The fidelity and speed of our surrogates allow for not only faster "forward" stochastic simulation but also for accurate and substantially faster inference. We support these claims via experiments that involve a commercial composite-materials curing simulator. Employing our surrogate modeling technique makes inference an order of magnitude faster, opening up the possibility of doing simulator-based, non-invasive, just-in-time parts quality testing; in this case inferring safety-critical latent internal temperature profiles of composite materials undergoing curing from surface temperature profile measurements.

Technology skills such as Artificial Intelligence/Machine Learning (AI/ML), digital marketing and design thinking will be important to drive the future growth, finds a survey by ed-tech firm Great Learning. The analysis of 307 corporates (ranging from small and medium enterprises (SMEs) to large corporate) focussed on finding out top skills that organisations will need to drive future performance and how they may plan to bridge the impending skill deficit among their ranks. As per the survey, 25 per cent of all companies believe AI/ML are the most crucial skills needed to ensure an organisation's future growth. Digital marketing emerged second with 19 per cent finding it most crucial. Prime Minister Narendra Modi on Tuesday met with the members of JP Morgan's International Council in New Delhi and discussed his vision for making India a USD 5 trillion economy by 2024.

In a novel approach that could help reduce carbon emissions, a team of scientists led by Stony Brook's Anatoly Frenkel have described a way to use artificial intelligence (AI) to facilitate the conversion of carbon dioxide (CO2) into methane. By using this method to track the size, structure, and chemistry of catalytic particles under real reaction conditions, the scientists can identify which properties correspond to the best catalytic performance, and then use that information to guide the design of more efficient catalysts. "Improving our ability to convert CO2 to methane would'kill two birds with one stone' by making a sustainable non-fossil-fuel energy source that can be easily stored and transported while reducing carbon emissions," said Anatoly Frenkel, a chemist with a joint appointment at the U.S. Department of Energy's Brookhaven National Laboratory (BNL) and Stony Brook University. Frenkel is a professor of Materials Science in the College of Engineering and Applied Sciences. Frenkel's group has been developing a machine-learning approach to extract catalytic properties from x-ray signatures of catalysts collected as chemicals are transformed in reactions.