One of Hollywood's oldest tropes is the robotic usurpation of humankind. It makes for compelling thrillers and science fiction. The variety of narrative twists possible are in-numerable. Terminator, a luminary of the genre, implanted this fear as efficiently as Jaws did in cultivating a phobia of sharks into an entire generation. As narratives go, everything else pales in comparison -- a cautionary tale regarding one's lust for power which inevitably capsizes when it surmounts its peak.

Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of current users for information that will lead to better decisions in the future. Recently, concerns have been raised about whether the process of exploration could be viewed as unfair, placing too much burden on certain individuals or groups. Motivated by these concerns, we initiate the study of the externalities of exploration - the undesirable side effects that the presence of one party may impose on another - under the linear contextual bandits model. We introduce the notion of a group externality, measuring the extent to which the presence of one population of users impacts the rewards of another. We show that this impact can in some cases be negative, and that, in a certain sense, no algorithm can avoid it. We then study externalities at the individual level, interpreting the act of exploration as an externality imposed on the current user of a system by future users. This drives us to ask under what conditions inherent diversity in the data makes explicit exploration unnecessary. We build on a recent line of work on the smoothed analysis of the greedy algorithm that always chooses the action that currently looks optimal, improving on prior results to show that a greedy approach almost matches the best possible Bayesian regret rate of any other algorithm on the same problem instance whenever the diversity conditions hold, and that this regret is at most $\tilde{O}(T^{1/3})$. Returning to group-level effects, we show that under the same conditions, negative group externalities essentially vanish under the greedy algorithm. Together, our results uncover a sharp contrast between the high externalities that exist in the worst case, and the ability to remove all externalities if the data is sufficiently diverse.

For some positions that Siemens AG SIEGY 1.43% needs to fill, there may be a universe of fewer than 2,000 qualified people in the U.S., said Michael Brown, vice president of talent acquisition in the Americas for the German industrial conglomerate that makes everything from gas turbines to mammography machines. "The question is how many of those are looking for a job?" Finding the right potential candidates on sites like LinkedIn isn't easy because "they're tired of being found." Siemens has 377,000 employees world-wide and about 50,000 in the U.S. At the moment, it has about 1,500 open jobs across America, most of which require some software or science-related background. Employers are handicapped by several factors, data show and recruiters say: Cutting-edge skills are evolving faster than universities can train people, the supply of talented young workers entering these fields isn't satisfying the huge demand for them, and mobility--a worker's willingness to uproot their life for a job in a new place--has declined. The odds of luring rare, coveted candidates away from their current job or city are long, Mr. Brown said.

Scientists from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel. Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells. The new approach is much faster than the current way to predict the behavior of pathways, and promises to speed up the development of biomolecules for many applications in addition to commercially viable biofuels, such as drugs that fight antibiotic-resistant infections and crops that withstand drought.

Given the value modern analytics can provide, there are going to be many within an industrial organization who will want to create models and consume their output. This includes high-level math experts, such as data scientists, citizen data scientists (such as engineers), as well as field- and back-office workers. Broadening the adoption of analytics across this complex set of users is by no means a simple task. These users often have specific, different, and sometimes competing perspectives on what data is valuable and how it is best used. Adding to this is the vast scale and scope of many industrial value chains, such as oil and gas, where like terms often have different meanings based on process application, multiple languages are used, and many individuals and roles are involved.

I am often asked which disruptive technologies will thrive in the oil and gas sector and what impact they will have on the industry. If I had been asked this question three or four years ago I would have said that it was unlikely that any would make a significant impression given the high oil price and the value being generated all through the supply chain. But how things changed, and quickly. The oil price crashed and the operators were scrambling around in almost unseemly haste to unlock the potential of disruption. The key word became digitisation and the benefits it could bring to a sector that was paying the price for decades of poor cost management, lack of standardization and a risk adverse nature that bordered on technophobia.

When we talk about the environment, it's important to remember the importance of a healthy ecosystem. Climate change is a real phenomenon, despite arguments to the contrary, and protecting our environment is vital for future generations. Google has long understood how important the environment is to its users, and has made a commitment to lowering its environmental footprint. But until now, that commitment has been hard to keep. Google relies on energy-intensive data centers to hold the massive amounts of information that it gathers, and as global demand for its services increases, so does its need for data center storage.

A new approach developed by Zak Costello (left) and Hector Garcia Martin brings the the speed and analytic power of machine learning to bioengineering. Scientists from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel. Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells.

Artificial intelligence and energy production might not be so unrelated, according to a faculty member at the University of Wyoming. At the Wyoming Energy Summit on Wednesday, Nick Cheney -- a visiting assistant professor in UW's computer science department -- discussed some of the ways in which autonomous machines using artificial intelligence might change the energy sector in years to come. "I'm not a futurist," Cheney said. "I don't believe, despite working in AI, that artificial intelligence is a thing that will immediately save us all or doom us all. It comes with its own potential risks and benefits.

The demand of metals by modern technology has been shifting from common base metals to a variety of minor metals, such as cobalt or indium. The industrial importance and limited geological availability of some minor metals have led to them being considered more "critical," and there is a growing interest in such critical metals and their producing companies. In this research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE), to predict the stock trend of major critical metal producers. Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors," each advisor dealing with a particular economic sector. Then through ensembles of weak classifiers, each advisor produces a prediction result, and all the advisors are combined again in a biased online update fashion to dynamically make the final prediction. Based on a misclassification error of 32% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296%, or an excess return of 130% within one year. In addition, the feature set selected by dynABE also suggests potentially influential factors to metal criticality, because stock prices of major producers influence metal production. Therefore, not only does this research propose a novel framework for specialized stock trend prediction, it also provides domain insights into dynamic features that potentially influence metal criticality.