As the thirst for technology increases and the demand for smartphones, electric cars and other complex technological devices grows, the amount of resources, minerals and metals we need will only increase, but can we meet this demand? There are few sectors and industries that have not been impacted by technological advancement. Whether it's improving efficiency, enhancing transparency or transforming the supply chain, big data, machine learning and AI are poised to reshape the mining sector as we know it; and the timing couldn't be any better. At the recent Big Data and AI conference held in Toronto, the topic of mining disruption through technology was front and center. Speaker Denis Laviolette, president and CEO of GoldSpot Discovery, highlighted the need for the mining sector to not only embrace the recent advancements, but to also quickly look for ways to integrate these innovations into its business model.

The supply chain is the heart of a company's operations. To make the best decisions, managers need access to real-time data about their supply chain, but the limitations of legacy technologies can thwart the goal of end-to-end transparency. However, those days may soon be behind us. New digital technologies that have the potential to take over supply chain management entirely are disrupting traditional ways of working. Within 5-10 years, the supply chain function may be obsolete, replaced by a smoothly running, self-regulating utility that optimally manages end-to-end work flows and requires very little human intervention.

The US dollar had an event-heavy week to start off June. The US dollar surged early last week as the uncertainty about the Euro arose due to political events happened in Europe and the volatility in Asian markets driven by threats of an immediate trade war between the US and China. On Thursday (May 31), the Euro rebounded as Italy's politicians seemed to have found a resolution to their struggles in forming a new government. In the same day, the Trump administration announced it was putting tariffs on steel and aluminum imports from Canada, Mexico and Europe, strengthening fears over the trade war and making the US dollar suffer a slump. The US labor indicators highlighted the fundamental strength of the country's economy and made the US dollar extend gains amid the Europe geopolitical turmoil.

This dissertation investigates the use of one-sided classification algorithms in the application of separating hazardous chlorinated solvents from other materials, based on their Raman spectra. The experimentation is carried out using a new one-sided classification toolkit that was designed and developed from the ground up. In the one-sided classification paradigm, the objective is to separate elements of the target class from all outliers. These one-sided classifiers are generally chosen, in practice, when there is a deficiency of some sort in the training examples. Sometimes outlier examples can be rare, expensive to label, or even entirely absent. However, this author would like to note that they can be equally applicable when outlier examples are plentiful but nonetheless not statistically representative of the complete outlier concept. It is this scenario that is explicitly dealt with in this research work. In these circumstances, one-sided classifiers have been found to be more robust that conventional multi-class classifiers. The term "unexpected" outliers is introduced to represent outlier examples, encountered in the test set, that have been taken from a different distribution to the training set examples. These are examples that are a result of an inadequate representation of all possible outliers in the training set. It can often be impossible to fully characterise outlier examples given the fact that they can represent the immeasurable quantity of "everything else" that is not a target. The findings from this research have shown the potential drawbacks of using conventional multi-class classification algorithms when the test data come from a completely different distribution to that of the training samples.

India's Mahindra & Mahindra, one of the biggest suppliers of smaller tractors to the U.S., and other manufacturers are racing to develop what they see as the future of farming: robo-tractors and other farming equipment to help produce more food, more sustainably at a lower cost. John Deere has tractors and combines on the market that free the driver in the cabin from the actual driving so he or she can monitor the crops and adjust pesticide, water and soil levels. Technology from Agco Corp.'s Fendt lets several driverless tractors follow a lead tractor driven by a human. Japanese firms Kubota and Yanmar are planning to launch driverless tractors that they expect to be popular with elderly farmers. The next generation is tractors that can drive entirely by themselves.

We revisit the importance of the individual units in Convolutional Neural Networks (CNNs) for visual recognition. By conducting unit ablation experiments on CNNs trained on large scale image datasets, we demonstrate that, though ablating any individual unit does not hurt overall classification accuracy, it does lead to significant damage on the accuracy of specific classes. This result shows that an individual unit is specialized to encode information relevant to a subset of classes. We compute the correlation between the accuracy drop under unit ablation and various attributes of an individual unit such as class selectivity and weight L1 norm. We confirm that unit attributes such as class selectivity are a poor predictor for impact on overall accuracy as found previously in recent work \cite{morcos2018importance}. However, our results show that class selectivity along with other attributes are good predictors of the importance of one unit to individual classes. We evaluate the impact of random rotation, batch normalization, and dropout to the importance of units to specific classes. Our results show that units with high selectivity play an important role in network classification power at the individual class level. Understanding and interpreting the behavior of these units is necessary and meaningful.

In a paper from California Institute of Technology's Jet Propulsion Laboratory, the author laments the loss of connection between machine learning and solving real-world problems. The main question that seems to come up is: what good can machine learning or artificial intelligence (AI) do? A lot, as it turns out. AI could play a big role in aiding and even solving the issues of world hunger and poverty in ways that are surprisingly simple, but increasingly necessary. When you think of AI, it's possible that fixing world hunger isn't the first application that comes to mind.

Mapping a city's buildings might seem like a simple task, one that could be easily automated by training a computer to read satellite photos. Because buildings are physically obvious facts out in the open that do not move around, they can be recorded by the satellites circling our planet. Computers can then "read" these satellite photographs, which are pixelated images like everyday photographs except that they carry more information about the light waves being reflected from various surfaces. That information can help determine the kind of building material and even plant species that appears in an image. Other patterns match up with predictable objects, like the straight lines of roads or the bends of rivers.

A slew of AI weed killers are on the horizon and have the potential to disrupt the multibillion dollar pesticides business. Among them is Swiss-company ecoRobotix and its weed-killing robot. It's solar-powered and can kill weeds for 12 hours straight without an operator at the helm. EcoRobotix uses 20 times less herbicide than traditional methods that spray entire fields. Founded in 2011, ecoRobotix develops autonomous weeding robots, which help farmers to produce healthier food with a more efficient and sustainable use of herbicides.

When the 300-foot Maersk Launcher docked in San Diego early Monday morning, it unloaded a cargo of hardened black blobs scooped from the bottom of the sea. The blobs are not rocks, but naturally-occurring metallic nodules that could one day yield metal deposits of cobalt, manganese, and nickel--not to mention scarce rare earth minerals. As worldwide demand rises for electric vehicle batteries and wind turbines, along with next generation technologies and weapon systems, demand for these metals has taken off. And the seabed is a prime target for those mining operations. Of course, it's no small feat to bring these potato-sized nodules from the bottom of the remote Pacific Ocean, and then sail them to a processing plant where the metals can be extracted.