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."

Agents trained in simulation may make errors when performing actions in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult for the agent to discover because the agent is unable to predict them a priori. In this work, we propose the use of oracle feedback to learn a predictive model of these blind spots in order to reduce costly errors in real-world applications. We focus on blind spots in reinforcement learning (RL) that occur due to incomplete state representation: when the agent lacks necessary features to represent the true state of the world, and thus cannot distinguish between numerous states. We formalize the problem of discovering blind spots in RL as a noisy supervised learning problem with class imbalance. Our system learns models for predicting blind spots within unseen regions of the state space by combining techniques for label aggregation, calibration, and supervised learning. These models take into consideration noise emerging from different forms of oracle feedback, including demonstrations and corrections. We evaluate our approach across two domains and demonstrate that it achieves higher predictive performance than baseline methods, and also that the learned model can be used to selectively query an oracle at execution time to prevent errors. We also empirically analyze the biases of various feedback types and how these biases influence the discovery of blind spots. Further, we include analyses of our approach that incorporate relaxed initial optimality assumptions. (Interestingly, relaxing the assumptions of an optimal oracle and an optimal simulator policy helped our models to perform better.) We also propose extensions to our method that are intended to improve performance when using corrections and demonstrations data.

The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we present an approach for inferring the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task exhibits two challenges: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors. This structure provides outstanding effectiveness and efficiency for small scale upsampling. However, the single-pass upsampling used by UrbanFM is insufficient at higher upscaling rates. Therefore, we further present UrbanPy, a cascading model for progressive inference of fine-grained urban flows by decomposing the original tasks into multiple subtasks. Compared to UrbanFM, such an enhanced structure demonstrates favorable performance for larger-scale inference tasks.

Massive Open Online Courses (MOOCs) have become popular platforms for online learning. While MOOCs enable students to study at their own pace, this flexibility makes it easy for students to drop out of class. In this paper, our goal is to predict if a learner is going to drop out within the next week, given clickstream data for the current week. To this end, we present a multi-layer representation learning solution based on branch and bound (BB) algorithm, which learns from low-level clickstreams in an unsupervised manner, produces interpretable results, and avoids manual feature engineering. In experiments on Coursera data, we show that our model learns a representation that allows a simple model to perform similarly well to more complex, task-specific models, and how the BB algorithm enables interpretable results. In our analysis of the observed limitations, we discuss promising future directions.

-- Predicting s ales opportunities outcome is a core to successful business management and revenue forecasting . Conventionally, this prediction has relied mostly on subjective human evaluations in the process of business to business (B2B) sales decision making. Here, we proposed a practical Machine Learning (ML) workflow to empower B2B sales outcome (win/lose) pre diction within a cloud - based computing platform: Microsoft Azure Machine Learning Service (Azure ML). This workflow consists of two pipelines: 1) a n ML pipeline that trains probabilistic predictive models in parallel on the closed sales opportunities data enhanced with an extensive feature engineering procedure for automated selection and parameterization of an optimal ML model and 2) a Prediction pipeline that uses the optimal ML model to estimate the likelihood of win n ing new sales opportunities as well a s predicting their outcome using optimized decision boundaries. The p erformance of the proposed workflow was evaluated on a real sales dataset of a B2B consulting firm. In the Business to Business (B2B) commerce, companies compete to win high - valued sales opportunities to maximize their profitability. In this regard, a key factor for maintain ing a successful B2B business is the task of determining the outcome of sales opportunities.

UNIVERSITY PARK, Pa.-- Enrique Del Castillo, distinguished professor of industrial engineering and professor of statistics at Penn State, has returned from his Fulbright U.S. Scholar Program, where he conducted research at the University of Coimbra in Coimbra, Portugal. Awarded by the J. William Fulbright Foreign Scholarship Board, Del Castillo was one of approximately 800 U.S. citizens selected to take their expertise abroad for the 2019-20 academic year through the program. Recipients of Fulbright Awards are selected on the basis of academic and professional achievement, as well as record of service and demonstrated leadership in their respective fields. The research project for which he was granted the Fulbright Fellowship, titled "Optimization and control of industrial production processes by active learning methods based on'big' and complex data," sought collaborative research between Penn State's Engineering Statistics and Machine Learning Laboratory and the University of Coimbra's chemometrics group in the Department of Chemical Engineering. Del Castillo worked on the optimization of production processes via machine learning for various industries with the chemometrics group; in particular, they focused on wine, paper and pharmaceuticals.

Big data has become an integral part of our lives and careers. Analyzing data in order to extract patterns is now an essential skill in the fields of technology, health, finance and research. The Coding Hive is a Toronto based training program that offers professional training in coding, statistics, data analytics, machine learning, and artificial intelligence (AI).

First, AI will continue to help students personalize their learning experience and ultimately, see better academic outcomes. Software like Office365 is already using AI to help improve students' learning experience, but we're only at the beginning of what's possible. Today, tools like the editor in Microsoft Word use AI to scan students' papers and make suggestions for more inclusive language and redundant words. PowerPoint is also now using an AI-powered tool called Presentation Coach that records a student as he or she presenting slides and offers a dashboard with feedback on things like word choice, pacing and filler words. These types of naturally integrated AI tools will offer students more control over their learning experience, which boosts their confidence and overall love of learning.

What story will people tell about your organization over the next ten years? Will they celebrate an enthusiastic innovator that thrived by adapting workforce skills and ways of working to the demands of the new economy? Or will they blame poor financial or operational results, unhappy employees, and community disruption on a short-sighted or delayed talent strategy? Our modeling shows that by 2030, up to 30 to 40 percent of all workers in developed countries may need to move into new occupations or at least upgrade their skill sets significantly. Research further suggests that skilled workers in short supply will become even scarcer. Some major organizations are already out front on this issue. Amazon recently pledged $700 million to retrain 100,000 employees for higher-skilled jobs in technology (for example, training warehouse employees to become basic data analysts).