Sudbury's Centre for Excellence in Mining Innovation (CEMI) has gone international in signing a memorandum of understanding (MOU) with an industry technology centre in Australia. CEMI and METS Ignited of Brisbane signed an agreement to establish a vehicle for each organization to collaborate and accelerate the commercialization of mining innovations in Canada and Australia. "We have boots on the ground in Australia now," said Charles Nyabeze, CEMI's vice-president of business development and commercialization, in a Sept. 13 phone interview. "We will have access to game-changing solutions not only for our Canadian mines, but also for the mines we work with globally." The two organizations intend to cross-promote each other in their respective countries when it comes to mining-related exploration, extraction, transportation; tailings, waste and water management technologies; and digitalization of mine operations using analytics, artificial intelligence, automation and robotics.

Fox News Flash top headlines for Sept. 12 are here. Check out what's clicking on Foxnews.com A bio-inspired robot can use water from the environment to launch itself into the air, British researchers revealed. The robot can travel 85 feet through the air after taking off and researchers believe it could be used to collect samples in hazardous or otherwise cluttered environments, such as during a major flood. Researchers from the Aerial Robotics Laboratory at Imperial College London devised a system that requires only 0.2 grams of calcium carbide powder in a combusion chamber, with the only moving part being a small pump that delivers water from the environment where the robot sits.

The robot is no stranger to heavy industry, but its virtual co-worker, robotic process automation (RPA), is only just beginning to find a place within the industrial sector. The technology's clever software robots can fulfil repetitive and time-consuming tasks, and offer a multitude of benefits from improved accuracy to cost-savings. However, with RPA failures a common occurrence in its early adoption, it's clear that initial implementation of the technology has not proved to be smooth sailing for many businesses. For multinational consultancy EY, RPA failures are all too familiar, having witnessed 30 to 50 per cent of initial projects fail. Companies developing the technology claim it can transform operations, but if it's as favourable as they say, why are there so many RPA failures?

Customer reviews are a gold mine, and artificial intelligence is a fast and cost-effective way to turn them into essential insight. Learn how AI can help you turn good feedback into great product, uncover what really matters to customers, and more, in this VB Live event. Customers have higher-than-ever expectations, and 89% of consumers are more likely than ever to share positive or negative experiences. More importantly, behind every customer review is an important and personal story. Good or bad, there's a reason they took the time to search out your feedback form or Facebook page, compose a message, choose a rating, and share their thoughts with the world.

Researchers from Penn State and Carnegie Mellon University (CMU) have received a $1.2 million grant from the United States Department of Energy (DOE) to use machine learning -- a form of artificial intelligence -- and data science to design more effective catalysts for chemical processing. The grant is part of a new initiative by the DOE to provide $27.6 million in grants for data science research in chemical and materials sciences. A catalyst is a stable chemical substance that, when added to a chemical reaction, increases the rate of reaction without becoming part of the reaction. "It is important to recognize how widespread the use of catalysts is," said Michael Janik, Penn State professor of chemical engineering and principal investigator for the study. "About 90% of chemical products people are using every day, like gasoline and fine chemicals that are in shampoo, are going through some sort of catalytic process before they're used."

A nature-inspired robot using water and combustible powder can launch itself from water like a flying fish. The device, which can travel 26 metres through the air after take-off, could potentially be used to collect water samples in hazardous environments, such as floods. Researchers at Imperial College London created the system, which weighs just 160 grams and can'jump' multiple times after refilling its water tank. Furthermore, while similar robots often require calm conditions to leap from the water, the team's invention generates a force 25 times the robot's weight, giving it a greater chance of overcoming choppy waves. The water and the calcium-carbide powder combine in a reaction chamber, producing a burnable acetylene gas.

Machine learning (ML) techniques are increasingly applied to decision-making and control problems in Cyber-Physical Systems among which many are safety-critical, e.g., chemical plants, robotics, autonomous vehicles. Despite the significant benefits brought by ML techniques, they also raise additional safety issues because 1) most expressive and powerful ML models are not transparent and behave as a black box and 2) the training data which plays a crucial role in ML safety is usually incomplete. An important technique to achieve safety for ML models is "Safe Fail", i.e., a model selects a reject option and applies the backup solution, a traditional controller or a human operator for example, when it has low confidence in a prediction. Data-driven models produced by ML algorithms learn from training data, and hence they are only as good as the examples they have learnt. As pointed in [17], ML models work well in the "training space" (i.e., feature space with sufficient training data), but they could not extrapolate beyond the training space. As observed in many previous studies, a feature space that lacks training data generally has a much higher error rate than the one that contains sufficient training samples [31]. Therefore, it is essential to identify the training space and avoid extrapolating beyond the training space. In this paper, we propose an efficient Feature Space Partitioning Tree (FSPT) to address this problem. Using experiments, we also show that, a strong relationship exists between model performance and FSPT score.

Experimental effort and animal welfare are concerns when exploring the effects a compound has on an organism. Appropriate methods for extrapolating chemical effects can further mitigate these challenges. In this paper we present the efforts to (i) (pre)process and gather data from public and private sources, varying from tabular files to SPARQL endpoints, (ii) integrate the data and represent them as a knowledge graph with richer semantics. This knowledge graph is further applied to facilitate the retrieval of the relevant data for a ecological risk assessment task, extrapolation of effect data, where two prediction techniques are developed.

In this article, we list down 6 trending jobs in machine learning one can apply. Responsibilities: The responsibilities include developing highly scalable classifiers and tools leveraging machine learning, data regression and rule-based models, deep learning, create language models from petabytes of text data in different languages, suggest, collect and synthesize requirements and innovate to create next-generation feature sets. The candidate will work as part of the product team to implement algorithms that power user and developer-facing products reaching out to millions of users, adapt standard machine learning methods to best exploit modern parallel environments. Prerequisites: The candidate must have strong background in one or more of Machine Learning, Artificial Intelligence, Pattern Recognition, Natural Language, Deep Learning, DNNs, large scale Data Mining, experience with scripting languages such as Perl, Python, PHP, and shell scripts, experience with recommendation systems, targeting systems, ranking systems or similar systems, experience with any of Hadoop/Hbase/Pig or MapReduce/Bigtable or R/Matlab/AzureML or similar technologies. Responsibilities: The responsibilities for a Machine Learning Engineer – Lead include building common ML capabilities used across Corporate based on machine learning models, automate and streamline existing processes, procedures, and toolsets.