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.

Before Alphabet's Wing drones can start dropping off deliveries all over the globe, the company has to remedy one of the biggest complaints about it first. According to a Wall Street Journal report, the drones are extremely noisy -- someone who lives directly under the drones' path in rural Australia where they're current being tested described the sound they make as "chainsaw gone ballistic." Since the tech will not widely adopted if it's that noisy (especially in dense urban areas) Wing is now developing a quieter drone. The Alphabet subsidiary, which used to be an X Lab project until it became a standalone company in mid-2018, has been testing its drones in Australia since 2014. Apparently, the machines create so much noise that people don't even use their yards anymore.

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy, including estimation of relevant parameters can be time consuming and costly. In this paper we address this issue by proposing generic adaptation strategies based on approaches from earlier works. Experimental results after using the proposed strategies with three adaptive algorithms on 36 datasets confirm their viability. These strategies often achieve better or comparable performance with custom adaptation strategies and naive methods such as repeatedly using only one adaptive mechanism.

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatiotemporal inputs. This paper presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutilized crossbar regions and supports rapid on-chip training within 2 clock cycles. This research also leverages plasticity mechanisms such as neurogenesis and homeostatic intrinsic plasticity to strengthen the robustness and performance of the SP. The proposed design is benchmarked for image recognition tasks using MNIST and Yale faces datasets, and is evaluated using different metrics including entropy, sparseness, and noise robustness. Detailed power analysis at different stages of the SP operations is performed to demonstrate the suitability for mobile platforms.

Drone operators are being warned about rules for flying after a drone forced the grounding of firefighting aircraft battling a blaze on Tasmania's Bruny Island. Tasmania police said the aircraft had to be grounded because firefighting efforts at Conleys Point, south Bruny, were being hampered by a drone flown in the area, putting community safety at risk. "This is a reminder to all operators of drones to not fly near aircraft at any time," police said. "This puts the safety of people in aircraft at risk and also impacts on the safety of the community." Flying drones near public safety or emergency operations, such as bushfires, can be an offence under Civil Aviation Safety Authority rules.

The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management. Project management has a large socio-technical element with many uncertainties arising from variability in human aspects e.g., customers' needs, developers' performance and team dynamics. AI can assist project managers and team members by automating repetitive, high-volume tasks to enable project analytics for estimation and risk prediction, providing actionable recommendations, and even making decisions. AI is potentially a game changer for project management in helping to accelerate productivity and increase project success rates. In this paper, we propose a framework where AI technologies can be leveraged to offer support for managing agile projects, which have become increasingly popular in the industry.

The high-dimensionality and volume of large scale multistream data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. This data, also categorized as big data, is becoming common in manufacturing plants. In this paper, we propose an integrated M\&D approach for large scale streaming data. We developed a novel monitoring method named Adaptive Principal Component monitoring (APC) which adaptively chooses PCs that are most likely to vary due to the change for early detection. Importantly, we integrate a novel diagnostic approach, Principal Component Signal Recovery (PCSR), to enable a streamlined SPC. This diagnostics approach draws inspiration from Compressed Sensing and uses Adaptive Lasso for identifying the sparse change in the process. We theoretically motivate our approaches and do a performance evaluation of our integrated M&D method through simulations and case studies.

We consider the torpedo scheduling problem in steel production, which is concerned with the transport of hot metal from a blast furnace to an oxygen converter. A schedule must satisfy, amongst other considerations, resource capacity constraints along the path and the locations traversed as well as the sulfur level of the hot metal. The goal is first to minimize the number of torpedo cars used during the planning horizon and second to minimize the time spent desulfurizing the hot metal. We propose an exact solution method based on Logic based Benders Decomposition using Mixed-Integer and Constraint Programming, which optimally solves and proves, for the first time, the optimality of all instances from the ACP Challenge 2016 within 10 minutes. In addition, we adapted our method to handle large-scale instances and instances with a more general rail network. This adaptation optimally solved all challenge instances within one minute and was able to solve instances of up to 100,000 hot metal pickups.