We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure.

Performing statistical inference in high-dimension is an outstanding challenge. A major source of difficulty is the absence of precise information on the distribution of high-dimensional estimators. Here, we consider linear regression in the high-dimensional regime $p\gg n$. In this context, we would like to perform inference on a high-dimensional parameters vector $\theta^*\in{\mathbb R}^p$. Important progress has been achieved in computing confidence intervals for single coordinates $\theta^*_i$. A key role in these new methods is played by a certain debiased estimator $\hat{\theta}^{\rm d}$ that is constructed from the Lasso. Earlier work establishes that, under suitable assumptions on the design matrix, the coordinates of $\hat{\theta}^{\rm d}$ are asymptotically Gaussian provided $\theta^*$ is $s_0$-sparse with $s_0 = o(\sqrt{n}/\log p )$. The condition $s_0 = o(\sqrt{n}/ \log p )$ is stronger than the one for consistent estimation, namely $s_0 = o(n/ \log p)$. We study Gaussian designs with known or unknown population covariance. When the covariance is known, we prove that the debiased estimator is asymptotically Gaussian under the nearly optimal condition $s_0 = o(n/ (\log p)^2)$. Note that earlier work was limited to $s_0 = o(\sqrt{n}/\log p)$ even for perfectly known covariance. The same conclusion holds if the population covariance is unknown but can be estimated sufficiently well, e.g. under the same sparsity conditions on the inverse covariance as assumed by earlier work. For intermediate regimes, we describe the trade-off between sparsity in the coefficients and in the inverse covariance of the design. We further discuss several applications of our results to high-dimensional inference. In particular, we propose a new estimator that is minimax optimal up to a factor $1+o_n(1)$ for i.i.d. Gaussian designs.

In this paper, we describe a case study in a big metropolis, in which from data collected by digital sensors, we tried to understand mobility patterns of persons using buses and how this can generate knowledge to suggest interventions that are applied incrementally into the transportation network in use. We have first estimated an Origin-Destination matrix of buses users from datasets about the ticket validation and GPS positioning of buses. Then we represent the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, applying community discovery techniques, to identify clusters that the service supply infrastructure has. Finally, from the superimposing of the flow of people represented in the OriginDestination matrix in the supply network, we exemplify how micro-interventions can be prospected by means of an example of the introduction of express routes.

Human interactions are characterized by explicit as well as implicit channels of communication. While the explicit channel transmits overt messages, the implicit ones transmit hidden messages about the communicator (e.g., his/her intentions and attitudes). There is a growing consensus that providing a computer with the ability to manipulate implicit affective cues should allow for a more meaningful and natural way of studying particular non-verbal signals of human-human communications by human-computer interactions. In this pilot study, we created a non-dynamic human-computer interaction while manipulating three specific non-verbal channels of communication: gaze pattern, facial expression, and gesture. Participants rated the virtual agent on affective dimensional scales (pleasure, arousal, and dominance) while their physiological signal (electrodermal activity, EDA) was captured during the interaction. Assessment of the behavioral data revealed a significant and complex three-way interaction between gaze, gesture, and facial configuration on the dimension of pleasure, as well as a main effect of gesture on the dimension of dominance. These results suggest a complex relationship between different non-verbal cues and the social context in which they are interpreted. Qualifying considerations as well as possible next steps are further discussed in light of these exploratory findings.

The results are a little different to those obtained using RMSE as the objective function. The focus is still well and truly on the volatility indicators, but in this case the best cross validated performance occurred when selecting only 2 out of the 15 candidate variables. Here's a plot of the cross validated performance of the best feature set for various numbers of features: The model clearly performs better in terms of absolute return for a smaller number of predictors. Performance bottoms at 8 predictors and then improves, but never again achieves the performance obtained with 2-4 predictors. This is consistent with Aronson's assertion that we should stick with at most 3-4 variables otherwise overfitting is almost unavoidable.

Intelligent Personal Assistant, Automated Personal Assistant or Automated Virtual Personal Assistant can perform tasks, or services, on behalf of an individual based on a combination of user input, and location awareness. It has the ability to access information from a variety of online sources such as weather conditions, traffic congestion, news, stock prices, user schedules, and retail prices. Intelligent Personal Assistant has the ability to organize and maintain information and includes the management of emails, calendar events, files, and to do lists. Some automated personal assistants can perform concierge type tasks or provide information based on voice input or commands and some smart personal agents, which can automatically perform management or data handling tasks based on online information without user initiation or interaction. Intelligent Agents can be classified based on their degree of perceived intelligence and capability such as simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents and learning agents.

In his new book, The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling, Adam Kucharski details how trying to understand dice games led one mathematician to develop probability theory, how one of the first wearable computers was designed to covertly predict the fall of a roulette ball, and how poker-playing bots are advancing more quickly than we think. As he shows, science, mathematics, and gambling have long been intertwined, and thanks to advances in big data and machine learning, our sense of what's predictable is growing, crowding out the spaces formerly ruled by chance. At the same time, though, we're letting more of our lives be influenced by algorithms, bits of code whose effects are beyond our full understanding. As in so many other areas, the creations are outpacing their creators. In the lightly edited interview below, Kucharski explains how we got here, what poker-playing bots can show us about being human, and what comes next. In the book you call gamblers the godfathers of probability theory, noting that it's a newer area of mathematics than we might expect.

As much as we tend to cringe when referring to the term' hipster culture' – we think there's some practical value to scoring suburbs on their (dare I say it) trendiness. At the end of the day, living somewhere that has character and access to good food and coffee trumps the alternative. Microburbs, an online property tool that launched last year, uses an algorithm to give suburbs – and even smaller pockets of land within them (thus the'micro') – a rating based on their cultural vibe. According to the algorithm, Sydney has the most'hipster' suburbs with Darlinghurst rating 9.9/10 and Surry Hills 9.9/10. Meanwhile, Melbourne hosts 21 suburbs with a score above 9, while Sydney has 20. So judging by this, it's still a little unclear which city comes up on top.

IBM has opened The Watson Centre at Marina Bay in Singapore – an incubator designed to bring together organisations of all sizes, business partners and IBM experts to co-create business solutions that leverage IBM's cognitive, Blockchain and design capabilities. IBM's new Asia Pacific headquarters is based in the same location, in the heart of Singapore's financial district. IBM Garage Singapore With the advent of Blockchain, and the increasing demand from clients across Asia to explore the possibilities of this transformative technology, IBM will help accelerate the design, development and commercialization of Singapore Blockchain applications through the IBM Garage and the IBM Global Entrepreneur program. At the IBM Garage, experts collaborate with clients, developers and entrepreneurs to test-drive tools, processes, and procedures to make Blockchain real. The garage creates a bridge between the scale of enterprise and culture of startups and supports the development of an Open Standards based Blockchain ecosystem and creates new work opportunities in Singapore.

The NHS could be applying machine learning-style processing to its patient, doctor and hospital data in an effort to improve efficiency within five years if plans by Google/DeepMind to push into the healthcare sector are approved. According to New Scientist, which has obtained a Memorandum of Understanding drawn up between DeepMind and the Royal Free NHS Trust in London, the two organisations are attempting to form a "broad ranging, mutually beneficial partnership, engaging in high levels of collaborative activity and maximising the potential to work on genuinely innovative and transformative projects." Among the areas the project aims to touch on are making improvements in clinical outcomes and patient safety, and reducing costs throughout the organisation. The memo also sets out a long list of "areas of mutual interest" where the two organisations could work together over the next five years, including bed and demand management software, financial control products, private messaging and task management for junior doctors, and even real-time health prediction. In fact, health prediction has formed the basis of the first project between the two partners, with Google/DeepMind creating an app called Streams that aims to study healthcare data to try to identify patients at risk of deterioration, readmission or even death.