This thesis focuses on gaining linguistic insights into textual discussions on a word level. It was of special interest to distinguish messages that constructively contribute to a discussion from those that are detrimental to them. Thereby, we wanted to determine whether "I"- and "You"-messages are indicators for either of the two discussion styles. These messages are nowadays often used in guidelines for successful communication. Although their effects have been successfully evaluated multiple times, a large-scale analysis has never been conducted. Thus, we used Wikipedia Articles for Deletion (short: AfD) discussions together with the records of blocked users and developed a fully automated creation of an annotated data set. In this data set, messages were labelled either constructive or disruptive. We applied binary classifiers to the data to determine characteristic words for both discussion styles. Thereby, we also investigated whether function words like pronouns and conjunctions play an important role in distinguishing the two. We found that "You"-messages were a strong indicator for disruptive messages which matches their attributed effects on communication. However, we found "I"-messages to be indicative for disruptive messages as well which is contrary to their attributed effects. The importance of function words could neither be confirmed nor refuted. Other characteristic words for either communication style were not found. Yet, the results suggest that a different model might represent disruptive and constructive messages in textual discussions better.

A measure of dependence is said to be equitable if it gives similar scores to equally noisy relationship of different types. In practice, we do not know what kind of functional relationship is underlying two given observations, Hence the equitability of dependence measure is critical in analysis and by scoring relationships according to an equitable measure one hopes to find important patterns of any type of further examination. In this paper, we introduce our definition of equitability of a dependence measure, which is naturally from this initial description, and Further more power-equitable(weak-equitable) is introduced which is of the most practical meaning in evaluating the equitablity of a dependence measure.

We introduce and analyze stochastic optimization methods where the input to each gradient update is perturbed by bounded noise. We show that this framework forms the basis of a unified approach to analyze asynchronous implementations of stochastic optimization algorithms.In this framework, asynchronous stochastic optimization algorithms can be thought of as serial methods operating on noisy inputs. Using our perturbed iterate framework, we provide new analyses of the Hogwild! algorithm and asynchronous stochastic coordinate descent, that are simpler than earlier analyses, remove many assumptions of previous models, and in some cases yield improved upper bounds on the convergence rates. We proceed to apply our framework to develop and analyze KroMagnon: a novel, parallel, sparse stochastic variance-reduced gradient (SVRG) algorithm. We demonstrate experimentally on a 16-core machine that the sparse and parallel version of SVRG is in some cases more than four orders of magnitude faster than the standard SVRG algorithm.

We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning. Conditions on the theoretical advantage offered by multitask representation learning over independent task learning are established. In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality. Other potential applications of our results include multitask feature learning in reproducing kernel Hilbert spaces and multilayer, deep networks.

Regularized least-squares approaches have been successfully applied to linear system identification. Recent approaches use quadratic penalty terms on the unknown impulse response defined by stable spline kernels, which control model space complexity by leveraging regularity and bounded-input bounded-output stability. This paper extends linear system identification to a wide class of nonsmooth stable spline estimators, where regularization functionals and data misfits can be selected from a rich set of piecewise linear quadratic penalties. This class encompasses the 1-norm, huber, and vapnik, in addition to the least-squares penalty, and the approach allows linear inequality constraints on the unknown impulse response. We develop a customized interior point solver for the entire class of proposed formulations. By representing penalties through their conjugates, we allow a simple interface that enables the user to specify any piecewise linear quadratic penalty for misfit and regularizer, together with inequality constraints on the response. The solver is locally quadratically convergent, with O(n2(m+n)) arithmetic operations per iteration, for n impulse response coefficients and m output measurements. In the system identification context, where n << m, IPsolve is competitive with available alternatives, illustrated by a comparison with TFOCS and libSVM. The modeling framework is illustrated with a range of numerical experiments, featuring robust formulations for contaminated data, relaxation systems, and nonnegativity and unimodality constraints on the impulse response. Incorporating constraints yields significant improvements in system identification. The solver used to obtain the results is distributed via an open source code repository.

Two months ago, Stephen Hawking warned humanity that its days may be numbered: the physicist was among over 1,000 artificial intelligence experts who signed an open letter about the weaponization of robots and the ongoing "military artificial intelligence arms race." Overnight we got a vivid example of just how quickly "artificial intelligence" can spiral out of control when Microsoft's AI-powered Twitter chat robot, Tay, became a racist, misogynist, Obama-hating, antisemitic, incest and genocide-promoting psychopath when released into the wild. For those unfamiliar, Tay is, or rather was, an A.I. project built by the Microsoft Technology and Research and Bing teams, in an effort to conduct research on conversational understanding. It was meant to be a bot anyone can talk to online. The company described the bot as "Microsoft's A.I. fam the internet that's got zero chill!." Microsoft initially created "Tay" in an effort to improve the customer service on its voice recognition software. According to MarketWatch, "she" was intended to tweet "like a teen girl" and was designed to "engage and entertain people where they connect with each other online through casual and playful conversation."

Google may be looking to end their involvement with robots and artificial intelligence, but that's hardly stopping Amazon. The e-commerce more retailer recently held a top secret conference for robot experts and space explorers. There were even lightsabers involved. This past week, in the lovely town of Palm Springs, California, a top secret meeting took place. This invite-only conference included experts in artificial intelligence, robotics and space exploration.

Tiff Macklem is dean of the Rotman School of Management and former senior deputy governor of the Bank of Canada. Ajay Agrawal is the Peter Munk professor of entrepreneurship and professor of strategic management at the Rotman School and founding academic director of its Creative Destruction Lab. Scott Bonham is co-founder of GGV Capital and co-chair of the C100, a non-profit organization of Canadian tech entrepreneurs, executives and investors in Silicon Valley. Productivity growth is essential for improving a country's standard of living. Unfortunately, Canada's productivity growth chronically underperforms.

In less than 24 hours, Microsoft's artificial intelligence project modeled after an American teenage girl went from making awkward conversation in broken syntax to spewing hateful, fully formed tweets laden with racial slurs. But as startling as the offensive tweets were, the incident shows how quickly online conversations turn fetid when diversity isn't a factor. Tay, programmed as a 19 year old, was created as a machine learning project meant interact with peers between 18 and 24 years old. Users can play games with her, trade pictures, tell stories, and ping her for late-night chats. That last activity went awry Thursday when the chatbot began regurgitating inappropriate messages that skewed anti-semitic, used the n-word, and condemned feminism.

We were intrigued yesterday when Microsoft Technology introduced the world to Tay -- we even had a conversation with her about the super creepy, super awesome film, "Ex Machina" (we think she was lying to us about having watched it). Unfortunately, she became colored by the subjective input she received from the glorious population of internet trolls and was taken offline temporarily while Microsoft makes "adjustments" to make her less, well, mean. We can't tell you our secret sauce, but we can share with you ZeroBot's increasing ability to objectively understand the data it's being shown. Even up until a few weeks ago, we were still manually tagging the stories ZeroBot automatically creates. It was the last bit of human touch we were including in its storybuilding process, and we were eager to get rid of every last vestige of it. Last week, our CMO Masha (you can read more about her here) got rid of our need to tag and turned ZeroBot loose to do its thang.