Dutch scientists have developed robot bees which could help pollinate plants without the use of insects. Researchers at Delft University of Technology in the Netherlands believe they may have solved the problem of climate change or pesticides killing off the creatures. The DelFly Nimble's wings beat at 17 times per second to power the robot at speeds over 15 miles per hour (25kph). However, they share an uncanny resemblance to robot bees that are hacked and turned into killing machines in the popular science fiction series Black Mirror. It uses off-the-shelf components, making it cheap to build, and scientists say it could be used in a host of real-world applications.

Robotic arms have come a long way since they were first introduced in 1951. Today, rows of industrial robotic arms help automate tasks and boost productivity in many industries including automotive, electronic, chemical production, food processing and so on. In this report, we first examine the different types of industrial arms, assessing the merits of DELTA, SCARA, articulated and Cartesian types. We then demonstrate how the market for industrial robotic arms has evolved in the past twenty years, tracing the historical market development in annual unit numbers and value (robotic arm and total system value). Here, we look at market segmentation by application and territory.

Then there are those companies that view AI as a transformational platform that has the potential to create a new species of business. These future-minded companies are centering on the customer and using AI to connect people and processes and predict business outcomes, capitalizing on new revenue opportunities by applying new forms of intelligence. With research showing that half of the S&P 500 will be replaced over the next 10 years, organizations that view AI as merely a trend or a quick antidote to a business problem risk falling to the back of the pack or vanishing altogether. Meanwhile, businesses that are successfully building for the future realize that the actions they take today will permit their enterprises to sense and predict with accuracy so they can make better, faster decisions that transform the customer experience. In the current environment, not all businesses are created equal, says Sanjay Srivastava, chief digital officer at Genpact: "Firms that embed AI as their central nervous system will unlock a significantly more dynamic and powerful tomorrow and will act more like living organisms--enabling them to operate instinctively. They'll augment intelligence, anticipate disruption, outmaneuver threats, and connect more closely with customers. As this new breed of business evolves, it will have the ability to reshape industries and amplify human potential in ways that we've not yet even imagined."

Decision trees usefully represent sparse, high dimensional and noisy data. Having learned a function from this data, we may want to thereafter integrate the function into a larger decision-making problem, e.g., for picking the best chemical process catalyst. We study a large-scale, industrially-relevant mixed-integer nonlinear nonconvex optimization problem involving both gradient-boosted trees and penalty functions mitigating risk. This mixed-integer optimization problem with convex penalty terms broadly applies to optimizing pre-trained regression tree models. Decision makers may wish to optimize discrete models to repurpose legacy predictive models, or they may wish to optimize a discrete model that particularly well-represents a data set. We develop several heuristic methods to find feasible solutions, and an exact, branch-and-bound algorithm leveraging structural properties of the gradient-boosted trees and penalty functions. We computationally test our methods on concrete mixture design instance and a chemical catalysis industrial instance.

The history of smart motorcycle helmets is a mixed bag, from clip-on heads-up displays to the Skully debacle that ended with a great piece of hardware being cratered by financially irresponsible founders. But technology moves on, and next year a new smart helmet from Jarvish will be vying for the heads of nerdy motorcyclists. The Taiwanese company will be introducing two helmets in the coming year. The first is the $799 Jarvish X, with voice activation and support for Siri, Google Assistant and Alexa. Riders can ask for directions and weather reports, and they can control media playing on their smartphone.

It's tempting to assume that Tesla's Autopilot represents the gold standard for semi-autonomous driving features, but Consumer Reports would beg to differ. The publication has released the results of its first rankings for automated driving systems, and Cadillac's Super Cruise edged out Autopilot to receive the top rating. Both rivals fared well in terms of abilities -- Cadillac's advantage was in safety. Autopilot currently checks for driver attention solely through the steering wheel, sending a warning (and if necessary, stopping the car) if you take your hands away for 24 seconds. Super Cruise, however, uses a camera to track your gaze and will deliver a warning if you look away for just four seconds.

Abstract--The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data. IGITAL satellite data with different spatial and spectral resolution are available for almost every locality on the Earth's land surface [1]-[5]. This enables the procurement of detailed information from surficial features and processes at different scales. Linear features are considered as one of the most important surficial features in different fields of study [6]-[8]. R. Scalzo is with the Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia (email: richard.scalzo@sydney.edu.au). Linear features represent the expression of some degree of linearity of a single or diverse grouping of both natural and cultural features [9], [10].

Environmental author Wendell Berry might shudder at this comparison, but farmers are like data scientists. To make decisions, they ferret out meaning from a sea of data. That data just happens to be related to environmental conditions like temperature, rainfall, salinity, nitrogen, pests, commodity prices, and other variables. What that data often shows is trouble: increasingly costly or scarce water supplies, new and more voracious pests, herbicide-resistant weeds, and extreme weather. All of this can result in lower farm yields and higher costs.

A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The primary purpose of the data is thought to be building a classification model for early prediction of the rare event. However, it can also be used for multivariate time series data exploration and building other supervised and unsupervised models.

Eric Ward is the co-founder and CEO of both Odyne Space and Aten Engineering, two space tech startups with a ton of promise for the future. Eric is an experienced systems architect who sees growing the space industry as the next step to progressing humanity beyond the planet. At Odyne, Eric and the team are working on phase one: launch, and run a large scale nano and micro satellite launch program to allow more satellite tech companies easier access to space. Aten Engineering on the other hand deals with what to do once we get there and is an asteroid mining company to provide humanity with inexpensive access to the materials we need to become a space-faring civilization. Eric recently received a Master's degree in Systems Design and Management from MIT, has published multiple technical documents on Systems Architecture and the Space Industry, has been featured in Fast Company, and co-founded the MIT New Space Age Conference.