Dialogue State Tracking (DST) is a core component of virtual assistants such as Alexa or Siri. To accomplish various tasks, these assistants need to support an increasing number of services and APIs. The Schema-Guided State Tracking track of the 8th Dialogue System Technology Challenge highlighted the DST problem for unseen services. The organizers introduced the Schema-Guided Dialogue (SGD) dataset with multi-domain conversations and released a zero-shot dialogue state tracking model. In this work, we propose a GOaL-Oriented Multi-task BERT-based dialogue state tracker (GOLOMB) inspired by architectures for reading comprehension question answering systems. The model "queries" dialogue history with descriptions of slots and services as well as possible values of slots. This allows to transfer slot values in multi-domain dialogues and have a capability to scale to unseen slot types. Our model achieves a joint goal accuracy of 53.97% on the SGD dataset, outperforming the baseline model.

Regret minimization is treated as the golden rule in the traditional study of online learning. However, regret minimization algorithms tend to converge to the static optimum, thus being suboptimal for changing environments. To address this limitation, new performance measures, including dynamic regret and adaptive regret have been proposed to guide the design of online algorithms. The former one aims to minimize the global regret with respect to a sequence of changing comparators, and the latter one attempts to minimize every local regret with respect to a fixed comparator. Existing algorithms for dynamic regret and adaptive regret are developed independently, and only target one performance measure. In this paper, we bridge this gap by proposing novel online algorithms that are able to minimize the dynamic regret and adaptive regret simultaneously. In fact, our theoretical guarantee is even stronger in the sense that one algorithm is able to minimize the dynamic regret over any interval.

We provide a new online learning algorithm which utilizes online passive-aggressive learning (PA) and total-error-rate minimization (TER) for binary classification. The PA learning establishes not only large margin training but also the capacity to handle non-separable data. The TER learning on the other hand minimizes an approximated classification error based objective function. We propose an online PATER algorithm which combines those useful properties. In addition, we also present a weighted PATER algorithm to improve the ability to cope with data imbalance problems. Experimental results demonstrate that the proposed PATER algorithms achieves better performances in terms of efficiency and effectiveness than the existing state-of-the-art online learning algorithms in real-world data sets.

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well known causal inference framework. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills - the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.

Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. Thus in this paper, we propose an online knowledge distillation method that transfers not only the knowledge of the class probabilities but also that of the feature map using the adversarial training framework. We train multiple networks simultaneously by employing discriminators to distinguish the feature map distributions of different networks. Each network has its corresponding discriminator which discriminates the feature map from its own as fake while classifying that of the other network as real. By training a network to fool the corresponding discriminator, it can learn the other network's feature map distribution. We show that our method performs better than the conventional direct alignment method such as L1 and is more suitable for online distillation. Also, we propose a novel cyclic learning scheme for training more than two networks together. We have applied our method to various network architectures on the classification task and discovered a significant improvement of performance especially in the case of training a pair of a small network and a large one.

One of the largest economic revolutions of our time is unfolding around us. Technology, innovation and automation are redrawing the career paths of millions of people. Most headlines focus on the negative, i.e. machines taking our jobs. But in reality, these developments are opening up a world of opportunity for people who can make the move to a STEM career or upskill in their current job. There's also another part to this story: How AI can help boost the economy by improving how we learn.

The past two years have seen a record number of women elected to board positions. According to a report on U.S. Board Diversity Trends posted by Harvard Law School, 46% of newly elected directors in 2019 were female and women now hold 27% of directorships across the S&P 500 companies. One of those newly elected members is Wendy Pfeiffer (pictured), chief information officer of Nutanix Inc. and board director with Qualys Inc. and Girls in Tech Inc. "When I was recruited for the board [of Qualys] … we didn't talk about the fact that I am female at all. We talked about the fact that I'm an operator, that I'm a technologist," Pfeiffer told Jeff Frick (@JeffFrick), host of theCUBE, SiliconANGLE Media's mobile livestreaming studio during the Qualys Security Conference in Las Vegas. During the interview, Pfeiffer and Frick discussed how the growth of artificial intelligence is helping data security, making having a diverse workforce more critical than ever.

Sharing AI related news has become crucial in this era of digital transformation. Given the current wave, many AI researchers have turned into AI influencers to drive value and success to their respective field. These are the people who are driving conversations about AI across social media and other platforms. Please note: This is not a ranking article. Gregory Piatetsky is a well-known expert in Big Data, Business Analytics, Data Mining, Data Science, and Machine Learning and is among top influencers in those fields.

Artificial Intelligence (AI) is allowing researchers to address some of the biggest and most urgent questions facing the world. For example, how to tackle climate change or find effective treatments for degenerative diseases. While many organizations are interested in AI, few have the data science expertise and tools to use deep learning solutions to solve their business challenges. Enter Multitel--an ICT innovation center and a leader in machine learning research committed to helping companies across sectors reap the rewards of AI. Jean-Yves Parfait, AI Team Leader at Multitel comments: "There is a lot of hype surrounding AI, which is making it more difficult for business leaders to have realistic expectations about how it could drive their business forward. We've made it our mission to help organizations realize the true potential of this emerging technology. We've been so successful that we've seen a steady increase in the number of companies commissioning AI research projects with us in recent years."