This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.

The construction of New York's Empire State Building is often seen as the figurative and literal pinnacle of construction efficiency, rising 1,250 feet and 102 stories from the ground to its rooftop spire in just over 13 months' time, at a human cost of just five lives. Indeed, most of today's construction projects would be lucky to come close to that level of speed, regardless of the building's size. While the construction industry traditionally has been slow to change the way it operates, several new technologies are poised to usher in a new era of faster and more automated construction practices. Three-dimensional (3D) printing is among the key technologies that are expected to change the way structures are built in the future, as construction engineers and contractors seek methods for completing buildings more quickly, more efficiently, and, in many cases, with a greater attention paid to sustainability. Large printers that can print construction materials such as foam or concrete into specific shapes can drastically speed up the creation of walls, decorative or ornamental pieces, and even certain structural elements.

The Y2K bug provided a windfall for Indian IT because Western companies had ignored an obvious problem, which if not addressed could have led to catastrophe. Now another catastrophe is looming, one that few people understand, but one that could provide even greater opportunities: quantum computing. Quantum computers could literally upset the global balance of power and pose a greater burden on businesses than the Y2K computer bug did toward the end of the '90s. The Y2K bug took years to remediate and created fear and havoc in the technology sector. And for solving that, we knew what the deadline was.

Drones are finding increasing use in Japanese agriculture as farmers start to use the unmanned aerial vehicles for crop inspection and other purposes. Drones "are effective in promoting data-based agriculture and reducing agricultural work" at a time when many aged farmers are struggling to find successors, says an official at the farm ministry's Technology Policy Office. In Japan, it is necessary for unmanned helicopters that spray pesticide, fertilizer or seeds to be registered with a special organization. Registration became necessary for drones in 2015. As of January, 673 drones had been registered, up about three times since last March.

From the BBC World Service … The world's biggest mining companies are seeing a boost to their bottom lines thanks to rising global commodity prices. We'll tell you how the battery revolution is helping shape the overall market. Then, India is opening up its coal industry, allowing foreign companies to bid for coal mines in the country. But will more investment from some of the world's biggest companies translate into better quality of life for residents there? Afterward, a conversation about whether growing use of artificial intelligence presents a looming danger.

According to the report, Apple is seeking to lock down a long-term deal, securing several thousand metric tons a year, for a last five years. The move puts Apple in direct competition with other big players who are also looking for a similar agreement, and advantage. BMW, Volkswagen and Samsung's own battery division are thought to be engaged in similar negotiations for their own EV projects. It's clear from the piece that Apple is only seeking to secure material for batteries that go inside its consumer hardware. CEO Tim Cook has been open about his company's interest in the "autonomous systems" market, but wouldn't be drawn on what exactly was being worked on.

Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that instance. For low class-label noise levels, especially when simple base learners are used, emphasis should be made on instances for which the disagreement rate is high. When more flexible classifiers are used and as the noise level increases, the emphasis on these uncertain instances should be reduced. In fact, at sufficiently high levels of class-label noise, the focus should be on instances on which the ensemble classifiers agree. The optimal type of emphasis can be automatically determined using cross-validation. An extensive empirical analysis using the beta distribution as emphasis function illustrates that vote-boosting is an effective method to generate ensembles that are both accurate and robust.

Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited resources to the top-ranked items. This work exposes a serious problem with the state of learning-to-rank algorithms, which is that they are based on convex proxies that lead to poor approximations. We then discuss the possibility of "exact" reranking algorithms based on mathematical programming. We prove that a relaxed version of the "exact" problem has the same optimal solution, and provide an empirical analysis.

To manufacture medicines, chemists must find the right combinations of chemicals to make the necessary chemical structures. This is more complicated than it sounds, as typical chemical reactions employ several different components, and each chemical involved in a reaction adds another dimension to the calculations. In an ideal world, chemists would like to predict which combination of chemicals would deliver the highest yield of product and avoid unintended by-products or other losses, but predicting the outcome of these multi-dimensional reactions has proven challenging. A group of researchers led by Abigail Doyle, the A. Barton Hepburn Professor of Chemistry at Princeton University, and Dr. Spencer Dreher of Merck Research Laboratories, has found a way to accurately predict reaction yields while varying up to four reaction components, using an application of artificial intelligence known as machine learning. They have turned their method into software that they have made available to other chemists.

In order to design and operate the process it is important to know which variables have the strongest influence on performance. To estimate this we compared each state with the length and diameter of the resulting fibers, performing second order polynomial fit using samples over all 9 experiments, and noting the value of the resulting correlation coefficients R. The correlations between process parameters and the Length and Diameter is contained in Supplementary Table S.1. The angle and position have very little influence on the characteristics of the produced fibers. Polymer flow has a moderate influence (0.37) on length, but only a weak influence on diameter. Thus the most significant influence on overall performance is solvent speed followed by channel width.