Autonomous weapons are currently at the core of the debate about the future of artificial intelligence, and some countries are already starting to distance themselves from the ethical dilemma and the political controversy involved in their use. In a recent meeting of the United Nations' Convention on Conventional Weapons (CCW), world leaders have considered a possible ban on these so-called "killer robots" -- technically a misnomer, as these autonomous weapons aren't limited to just the robotic type. Now, at the annual Munich Security Conference (MSC), Germany made it clear they're not interested in developing autonomous weapon systems. "We have a very clear position. We have no intention of procuring […] autonomous systems," said Lieutenant General Ludwig Leinhos, head of Germany's new Cyber and Information Space Command, according to eNCA.

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Recently, I reached out to 17 thought leaders -- AI experts, computer engineers, roboticists, physicists, and social scientists -- with a single question: "How worried should we be about artificial intelligence?" Disagreement about the appropriate level of concern, and even the nature of the problem, is broad. Some experts consider AI an urgent danger; many more believe the fears are either exaggerated or misplaced. Here is what they told me. I am infinitely excited about artificial intelligence and not worried at all.

Last October, Uber had one of its self- driving trucks make a beer run, traveling 200 kilometers down the interstate to deliver a cargo of Budweiser from Fort Collins to Colorado Springs. A person rode in the truck but spent most of the trip in the sleeper berth, monitoring the automated system. The self-driving truck developed by Uber's recently acquired Otto unit reflects remarkable technological achievements. It also provides yet another indicator of a looming shift in the economy that could have deep political consequences. It is uncertain how long it will take for driverless trucks and cars to take over the roads.

WASHINGTON – Artificial intelligence and its impact on weapons of the future has made U.S. Defense Secretary Jim Mattis doubt his own theories on warfare. A question on the subject prompted the retired Marine general to give an impromptu seminar on his theory of war Saturday to reporters returning with him from a week-long tour of Europe. Recalling his own writings, he differentiated between the essential nature of war, which is unchanging because it is human, and war's character, which is changing. "The fundamental nature of war is almost like H2O," he said. An old dead German called it a Chameleon because it changes to adapt to its time, to the technology, to the terrain," he said, referring to the 19th century military strategist Carl von Clausewitz. Mattis explained that today drones are piloted remotely, but tomorrow weapons may be able to learn on their own, adapt and fire themselves. "The most misnamed weapon in our system is the unmanned aerial vehicle.

There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed. However, most recent work focuses on discriminative classifiers which only models the conditional distribution of the labels given the inputs. In this abstract we propose deep Bayes classifier that improves the classical naive Bayes with conditional deep generative models, and verifies its robustness against a number of existing attacks. We further developed a detection method for adversarial examples based on conditional deep generative models. Our initial results on MNIST suggest that deep Bayes classifiers might be more robust when compared with deep discriminative classifiers, and the proposed detection method achieves high detection rates against two commonly used attacks.

Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an anonymous author even when the author tries to remove privacy sensitive content. In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ($A^4NT$), to combat such text-based adversaries. We combine sequence-to-sequence language models used in machine translation and generative adversarial networks to obfuscate author attributes. Unlike machine translation techniques which need paired data, our method can be trained on unpaired corpora of text containing different authors. Importantly, we propose and evaluate techniques to impose constraints on our $A^4NT$ to preserve the semantics of the input text. $A^4NT$ learns to make minimal changes to the input text to successfully fool author attribute classifiers, while aiming to maintain the meaning of the input. We show through experiments on two different datasets and three settings that our proposed method is effective in fooling the author attribute classifiers and thereby improving the anonymity of authors.

Networks are a fundamental model of complex systems throughout the sciences, and network datasets are typically analyzed through lower-order connectivity patterns described at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, also called network motifs, describe the fundamental structures that control and mediate the behavior of many complex systems. We develop three tools for network analysis that use higher-order connectivity patterns to gain new insights into network datasets: (1) a framework to cluster nodes into modules based on joint participation in network motifs; (2) a generalization of the clustering coefficient measurement to investigate higher-order closure patterns; and (3) a definition of network motifs for temporal networks and fast algorithms for counting them. Using these tools, we analyze data from biology, ecology, economics, neuroscience, online social networks, scientific collaborations, telecommunications, transportation, and the World Wide Web.

In an increasingly polarized world, demagogues who reduce complexity down to simple arguments based on emotion are gaining in popularity. Are opinions and online discussions falling into demagoguery? In this work, we aim to provide computational tools to investigate this question and, by doing so, explore the nature and complexity of online discussions and their space of opinions, uncovering where each participant lies. More specifically, we present a modeling framework to construct latent representations of opinions in online discussions which are consistent with human judgements, as measured by online voting. If two opinions are close in the resulting latent space of opinions, it is because humans think they are similar. Our modeling framework is theoretically grounded and establishes a surprising connection between opinion and voting models and the sign-rank of a matrix. Moreover, it also provides a set of practical algorithms to both estimate the dimension of the latent space of opinions and infer where opinions expressed by the participants of an online discussion lie in this space. Experiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports, and the Newsroom app suggest that unidimensional opinion models may be often unable to accurately represent online discussions, provide insights into human judgements and opinions, and show that our framework is able to circumvent language nuances such as sarcasm or humor by relying on human judgements instead of textual analysis.

The rise of artificial intelligence will help improve productivity and lead to equitable development, said Prime Minister Narendra Modi at the University of Mumbai on Sunday. Modi, who inaugurated the Wadhwani Institute for Artificial Intelligence on the Kalina campus of the university, downplayed fears of humans losing jobs to robots. "With each wave of new technology, new opportunities arise. It opens an entirely new paradigm of opportunities. New opportunities have always outnumbered old ones," said Modi. "This optimism spells from my firm faith in the ancient Indian thinking that blended science and spirituality and found harmony between the two for the greater good of mankind," he said.