India and the United States are reportedly pushing forward a deal for the supply of Predator drone aircraft in order to complete the project before U.S. President Barack Obama leaves office early next year. The aim is to complete the main process in the next few months," Reuters quoted a government official in New Delhi -- speaking on condition of anonymity -- as saying. He was referring to the country's request for 22 Predator Guardian drones -- to be used for military surveillance -- that was made in June this year. India is apparently rushing to conclude the deal under the Obama administration as Indian Prime Minister Narendra Modi shares cordial relations with the American president after several personal interactions since the former took office in 2014. India serves as a strategic partner in the U.S. mission to tackle terrorism in the region while Washington has replaced Moscow as the top arms supplier to New Delhi.

KDnuggets recently ran an Automated Data Science and Machine Learning blog contest, which garnered numerous entries and lots of appreciation for the winning posts and a pair of honorable mentions. The winning post, titled Contest Winner: Winning the AutoML Challenge with Auto-sklearn, written by Matthias Feurer, Aaron Klein, and Frank Hutten, all of the University of Freiburg, provides an overview of Auto-sklearn, an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. The project is built around the successful scikit-learn library and won the recent AutoML challenge. Given the popularity of the post, we asked the authors if they would be interested in answering a few followup questions on themselves, their project, and automated data science in general. What follows is the result of this conversation. What if we start by having you introduce the members of the team and provide a little information on each of your backgrounds?

"It was customary in old times for all Authors to enter the world of letters on their knees, and with uncovered head, and a bow of charming meekness write themselves some brainless dolt's "most humble and obedient servant." In later days, the same feigned subserviency has shown itself in other forms. One desires that some will kindly pardon the weakness and imbecility of his production; for, although these faults may exist in his book, he wrote under "most adverse circumstances," as the crying of a hopeful child, the quarrels of his poultry, and other disasters of the season. Another, clothed with the mantle of the sweetest self-complacency, looks out from his Preface, like a sun-dog on the morning sky, and merely shines out the query, "Am I not a Sun?" while he secures a retreat for his self-love, in case any body should suppose he ever indulged such a singular sentiment. They hold out to the world no need of aid in laying the foundations of their fame; and, however adverse the opinions of the times may be to their claims to renownThe Oregon Territory forms the terminus of these Travels; and, as that country is an object of much interest on both sides of the Atlantic, I have thought proper to preface my wanderings there by a brief discussion of the question as to whom it belongs.

Brilliant minds like Isaac Newton, Leonardo da Vinci, Stephen Hawking, Neil deGrasse Tyson, Nicola Tesla, and of course Albert Einstein are equally known for the application of knowledge to problem solving. While we doubt we'll be winning a Nobel Prize any time soon, we're pleased to announce that Email Studio now includes artificial intelligence (AI) powered by Salesforce Einstein. Einstein combines machine learning, deep learning, natural language processing, smart discovery, and predictive analytics to help our customers get smarter and more predictive about their customers. Admittedly, the idea of adding AI to your email program may seem a little like science fiction, but in reality it's something you can do today. Machine learning is a lot more common than we may realize.

What are the best practices for creating artificial intelligence? It's a question posed by the "partnership on AI" formed by major American technology firms. The goal of the partnership, which includes Google, IBM, Microsoft and Facebook, is to "conduct research, recommend best practices, and publish research under an open license (sic) in areas such as ethics, fairness and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology." Now, a clarification of terms: AI and robots are different. I should know: I wrote a series of novels about self-replicating humanoid robots.

Golden parachutes can't seem to stay out of the news. Last year, Jeff Smisek, the former CEO of United Airlines, received a separation payment of 4.875 million in cash along with additional equity awards and other benefits for a total of close to 37 million after being ousted from his company. Can it also transform the nation? Hillary Clinton was campaigning for her husband in January 1992 when she learned of the race's newest flare-up: Gennifer Flowers had just released tapes of phone calls with Bill Clinton to back up her claim they had had an affair. We tend to associate salads most closely with spring and summer, when fresh produce is at its peak and when we're all in the mood for lighter, fresher-tasting meals.

Next time you try to stifle a yawn, it might be worth discarding polite etiquette by letting your mouth gape for as long as you need because it could help to reveal how smart you are. Biologists have discovered a surprising relationship between the length of time mammals yawn for and how big and complex their brains are. They believe it may also be a sign of greater cognitive ability. Yawning may give an indication of your cognitive abilities, according to a new study. The exact physiological function of yawning is still largely a mystery.

Unification in Description Logics has been introduced as a means to detect redundancies in ontologies. We try to extend the known decidability results for unification in the Description Logic $\mathcal{EL}$ to disunification since negative constraints can be used to avoid unwanted unifiers. While decidability of the solvability of general $\mathcal{EL}$-disunification problems remains an open problem, we obtain NP-completeness results for two interesting special cases: dismatching problems, where one side of each negative constraint must be ground, and local solvability of disunification problems, where we consider only solutions that are constructed from terms occurring in the input problem. More precisely, we first show that dismatching can be reduced to local disunification, and then provide two complementary NP-algorithms for finding local solutions of disunification problems.

This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a \emph{social RADAR}, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings.

Cluster analysis is an unsupervised learning strategy that can be employed to identify subgroups of observations in data sets of unknown structure. This strategy is particularly useful for analyzing high-dimensional data such as microarray gene expression data. Many clustering methods are available, but it is challenging to determine if the identified clusters represent distinct subgroups. We propose a novel strategy to investigate the significance of identified clusters by comparing the within- cluster sum of squares from the original data to that produced by clustering an appropriate unimodal null distribution. The null distribution we present for this problem uses kernel density estimation and thus does not require that the data follow any particular distribution. We find that our method can accurately test for the presence of clustering even when the number of features is high.