Mining corporation Rio Tinto says that an autonomous rail system called AutoHaul that it's been developing in the remote Pilbara region of Australia for several years is now entirely operational -- an accomplishment the company says makes the system the "world's largest robot." "It's been a challenging journey to automate a rail network of this size and scale in a remote location like the Pilbara," Rio Tinto's managing director Ivan Vella told the Sidney Morning Herald, "but early results indicate significant potential to improve productivity, providing increased system flexibility and reducing bottlenecks." The ore-hauling train is just one part of an ambitious automation project involving robotics and driverless vehicles that Rio Tinto wants to use to automate its mining operations. The company conducted its first test of the train without a human on board earlier this year, and it now claims that the system has completed more than a million kilometers (620,000 miles) of autonomous travel. In response to concerns from labor unions, Rio Tinto promised that the autonomous rail system will not eliminate any existing jobs in the coming year -- though it's difficult to imagine the project won't cut into human jobs in the long term.

Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the cost of negative items is high for the users. In this work, we describe a new recommender algorithm that explicitly models negative user preferences in order to recommend more positive items at the top of recommendation-lists. We build upon existing machine-learning model to incorporate the contextual information provided by negative user preference. With experimental evaluations on two openly available datasets, we show that our method is able to improve recommendation quality: by improving accuracy and at the same time reducing the number of negative items at the top of recommendation-lists. Our work demonstrates the value of the contextual information provided by negative feedback, and can also be extended to signed social networks and link prediction in other networks.

Data mining is a computational process of finding patterns in large data sets with methods like artificial intelligence, machine learning, statistics, analysis, and systems. With a goal to get information from that data that can later be used. The relationship between customers and companies has changed – companies have become easily accessible through social media and messaging platforms which provide valuable but unstructured data. This is why companies need data mining and tools that come with it. Data mining tools allow businesses to gather information from these platforms and use it for their purposes – namely, marketing evaluation and analysis.

In less than 50 years, Artificial Intelligence (AI) will match humans traits like adaptability, creativity and emotional intelligence, an expert has predicted. Speaking at the "Festival of Dangerous Ideas" at University of New South Wales in Sydney on Sunday, Professor Toby Walsh said AI will match human intelligence by 2062, media reported on Monday "Toby Walsh, Scientia Professor of Artificial Intelligence at UNSW Sydney, has put a date on this looming reality. "He considers 2062 the year that artificial intelligence will match human intelligence, although a fundamental shift has already occurred in the world as we know it," the university said in a statement. Walsh argued that we are already experiencing the risks of AI that seem to be so far in the future. "Even without machines that are very smart, I'm starting to get a little bit nervous about where it's going and the important choices we should be making", said Walsh who has written a book "2062: The World that AI Made".

Drones could someday revolutionize e-commerce by cutting delivery times, reducing energy use and lowering costs. For now, they are dividing neighbors in the suburban neighborhood of Bonython, where one of the world's most advanced drone-delivery tests has taken flight. Tech companies are tinkering with drone deliveries all over the world. Wing is a step ahead of some by routinely bringing everyday items to customers in an entire neighborhood. Residents can use a smartphone app to order food, hardware supplies and over-the-counter medications from half a dozen retailers.

Emergent and unscheduled cardiology admissions from cardiac catheterization laboratory add complexity to the management of Cardiology and in-patient department. In this article, we sought to study the behavior of cardiology admissions from Catheterization laboratory using time series models. Our research involves retrospective cardiology admission data from March 1, 2012, to November 3, 2016, retrieved from a hospital in Iowa. Autoregressive integrated moving average (ARIMA), Holts method, mean method, na\"ive method, seasonal na\"ive, exponential smoothing, and drift method were implemented to forecast weekly cardiology admissions from Catheterization laboratory. ARIMA (2,0,2) (1,1,1) was selected as the best fit model with the minimum sum of error, Akaike information criterion and Schwartz Bayesian criterion. The model failed to reject the null hypothesis of stationarity, it lacked the evidence of independence, and rejected the null hypothesis of normality. The implication of this study will not only improve catheterization laboratory staff schedule, advocate efficient use of imaging equipment and inpatient telemetry beds but also equip management to proactively tackle inpatient overcrowding, plan for physical capacity expansion and so forth.

Honey has been collected and used by humankind as both a food and medicine for thousands of years. However, in the modern economy, honey has become subject to mislabelling and adulteration making it the third most faked food product in the world. The international scale of fraudulent honey has had both economic and environmental ramifications. In this paper, we propose a novel method of identifying fraudulent honey using machine learning augmented microscopy.

Wing, a graduate of Google parent company Alphabet's X R&D lab, aims to develop drones that might one day be used to deliver packages to customers' doorsteps. According to The Wall Street Journal, Wing's parcel-carrying drones, which were deployed in a rural area of southeastern Australia in October 2017 as part of a pilot program, have disrupted the lives of some longtime residents, who say that they don't use their yards as much. And the noise -- which some accounts compared to that of a chainsaw -- tends to spook pups, a local dog club president told the publication. The current-gen Wing drones can fly at speeds of up to 78 miles per hour and take off and land vertically, thanks to a dozen vertical rotors and two propellers. Automated flight-planning software determines their route, while onboard sensors help them to avoid obstacles. Despite the sophisticated onboard tech, though, sounds aren't the only problem Wing's drones have yet to overcome.

Editor's Note: The DigitalGlobe 2018 Australia Sustainability Hackathon aimed to address Australia's most conflicting issues surrounding mining, agriculture and environmental sustainability using machine learning and satellite imagery. This blog post is written by the winning team from the agriculture category. The forestry industry can benefit from multi-spectral, high-resolution satellite imagery in a number of ways, particularly for inventory components, such as tree stocking assessment, Leaf Area Index (LAI) estimation, volume survey and health analysis at stand and individual tree level. These could be measured in direct way through sampling. However, direct methods are very labour intensive, costly and subject to sampling error. Image-based remote sensing and advanced artificial intelligence (AI) technology offer an affordable solution to this problem.

A drone equipped with a thermal camera is seen in this file photo. CANBERRA, Australia--Robyn McIntyre, who lives on the outskirts of Australia's capital, was in her family room a few months ago when she thought she heard a "chain saw gone ballistic." It was actually a drone on its way to deliver a burrito or coffee as part of a test from Wing, which like Google is a subsidiary of Alphabet Inc. One recent day, she said delivery drones flew over her house about 10 times in 2½ hours, making it difficult to focus on working or reading the newspaper. "There's one!" said Ms. McIntyre, 64 years old, drinking tea in her living room on a recent Saturday morning.