A famous idiom states that nothing in this life is certain other than death and taxes. Yet, of the many ways that the films and novels of science fiction have imagined the roles of robots in our high-tech future, paying taxes has generally not been one of the functions dreamed up for our android friends. Nonetheless, the futuristic-sounding concept of a "robot tax" is now a real topic in Europe and beyond, if still being quite a distance from becoming a real thing. For many years, issues around the rapid digitalization of the working environment and the increasing use of automation and robotics have energized economic and social debate. A long established argument is that increasingly rapid advances in artificial intelligence (AI) and automation - a so-called "robot revolution" - will ultimately leave huge numbers unemployed, with no sector of the labor market left untouched.

To illustrate his position, he used the example of the MH17 plane crash. "When the Russians shot down the airliner, and we were searching for the smoking gun, we found it a month later -- on Facebook," the general said at an Air Force Association breakfast in Washington Wednesday, according to DefenseTech.com. Goldfein pointed the finger at Russia for the crash, as though the Netherlands have already made a conclusion as to who shot the Buk missile that brought the plane down (which they have not). "We found posted pictures on Russian blog sites that actually showed the activity, but it took us a month to figure that out," Goldfein said, leaning on social media as a source of reliable information, even though investigation of the MH17 catastrophe is still going on and it is hampered by "a great deal of disinformation and attempts to discredit the investigation," Dutch Foreign Minister Bert Koenders said in a statement earlier in July, according to France 24. Discussing a trip to the offices of the Bloomberg news agency, he reportedly asked a technician to perform a Twitter search on violent extremist activity over the last 48 hours, and the system actually mapped the relevant tweets on a map.

Robots posing as people online are "a menace," Tim Wu wrote recently in The New York Times. Bots swarm the Internet pretending to be human, slinging election propaganda and controlling hot Broadway tickets. Robots, some in embodied human form, may take over a startling percentage of U.S. jobs in the next couple of decades. In his book out last month, Will Robots Take Your Job? Nigel M. de S. Cameron notes that the U.S.'s 3.5 million workers in the trucking industry are at risk because of the coming rise of autonomous vehicles, but robots are moving also to "occupy the space of emotional intelligence." Robot health-care companions and virtual psychiatrists may be in the offing. Robots do, of course, offer huge benefits to us.

In his introduction, Jacobs offers a brief history of Canada's pioneering contribution to the field of artificial intelligence (AI), explaining the significance of the shift between rules-based AI and machine learning that originated in Ontario. "Forty years ago, the prevalent form of AI involved programmers using IF/THEN statements to teach machines," Jacobs explains. "Then there were these outliers who believed that, 'no, you're not going to program anything, the machine is going to figure it out itself, and it's going to do this by using artificial neurons that mimic how the brain works.' The leader of that group was someone named Geoffrey Hinton, and for most of his career, people said that he was crazy…They couldn't really get any funding except for a couple small research organizations in Canada, including CIFAR." The Canadian Institute for Advanced Research (CIFAR) that Jacobs refers to, approved its first program, Artificial Intelligence & Robotics in 1982, while operating out of an Ontario government office just a few blocks from where Jacobs is sitting, and later recruited Geoffrey Hinton to Toronto.

"The Emoji Movie" succeeded from its zero percent score on Rotten Tomatoes Friday after one reviewer gave the film a certified-fresh review, bumping its score up to 3 percent. Betsy Bozdech is the first critic to give the movie a positive review. The reviewer responsible for the boosted score hails from Common Sense Media, claiming the 3D computer-animated comedy flick "isn't bad, but it isn't great, either. Common Sense Media aims to educate parents with quality reviews on the latest media and technology arrivals. The review on the Sony-Columbia Pictures collaboration evaluates the movie's educational value, positive messages, positive role models and language, among other factors. Bozdech came to the conclusion that the film's overall message "emphasizes the importance of being true to yourself, as well as the value of honesty and teamwork." Bozdech also cited the movie's protagonist Gene, voiced by "Silicon Valley" alum T.J. Miller, as a good role model for children viewers. "Gene starts out desperate to fit in and do what he's'supposed' to do, but he learns that his ability to be many things is what makes him special -- and he can be useful by being himself," she wrote. "Gene's parents love him, but at first they're also a bit embarrassed by him and his differences.

While Tesla rolls out its Model 3 with autonomous hardware, a new survey found more than half of Americans say they would buy a self-driving vehicle for their next car purchase. The data, provided by Reportlinker, show 70 percent of people love driving themselves. However, 53 percent say they would buy a fully autonomous car for their next purchase, while a third of respondents say they would be interested in buying a partially automated vehicle. A reason why people might be leaning towards self-driving technology is because automation is slowly making its way into American vehicle. More than 50 percent of respondents said their current car has automatic cruise control, 36 percent said it has cameras with rear or side views and 20 percent said it has automatic braking.

In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman correlation coefficient values, our methods perform more than two times better (nearly 0.62) in predicting the borrowing likeliness compared to the best performing baseline (nearly 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88 percent of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.

In this paper, a generalized multivariate Student-t mixture model is developed for classification and clustering of Low Probability of Intercept radar waveforms. A Low Probability of Intercept radar signal is characterized by a pulse compression waveform which is either frequency-modulated or phase-modulated. The proposed model can classify and cluster different modulation types such as linear frequency modulation, non linear frequency modulation, polyphase Barker, polyphase P1, P2, P3, P4, Frank and Zadoff codes. The classification method focuses on the introduction of a new prior distribution for the model hyper-parameters that gives us the possibility to handle sensitivity of mixture models to initialization and to allow a less restrictive modeling of data. Inference is processed through a Variational Bayes method and a Bayesian treatment is adopted for model learning, supervised classification and clustering. Moreover, the novel prior distribution is not a well-known probability distribution and both deterministic and stochastic methods are employed to estimate its expectations. Some numerical experiments show that the proposed method is less sensitive to initialization and provides more accurate results than the previous state of the art mixture models.

Restricted Boltzmann Machines are described by the Gibbs measure of a bipartite spin glass, which in turn corresponds to the one of a generalised Hopfield network. This equivalence allows us to characterise the state of these systems in terms of retrieval capabilities, both at low and high load. We study the paramagnetic-spin glass and the spin glass-retrieval phase transitions, as the pattern (i.e. weight) distribution and spin (i.e. unit) priors vary smoothly from Gaussian real variables to Boolean discrete variables. Our analysis shows that the presence of a retrieval phase is robust and not peculiar to the standard Hopfield model with Boolean patterns. The retrieval region is larger when the pattern entries and retrieval units get more peaked and, conversely, when the hidden units acquire a broader prior and therefore have a stronger response to high fields. Moreover, at low load retrieval always exists below some critical temperature, for every pattern distribution ranging from the Boolean to the Gaussian case.

After spending the past few years in Virginia Beach, 2017 RoboBoat changed the scenery. Now in Daytona Beach, Florida, we are excited for new challenges and possibilities for our Teams.