Nisha Rataria remembers the moment that she understood the power of technology to significantly improve a child's learning and comprehension. As a teacher at the public Vidhya Nagar Primary School in Ahmedabad, Gujarat, India, Rataria teaches students from across the spectrum – bright, struggling, poor and middle class. A few years ago, her school implemented an artificial-intelligence based education program called EnglishHelper that provides a suite of tools to help children learn to speak, read and write English. Many of her students, who she says could not even recognize the alphabet, could now read English with some confidence. By the end of the 2019-2020 school year, EnglishHelper and ReadToMe could be used by nearly 20 million students worldwide.

There are many never-ending debates between Republicans and Democrats. Impeach vs. don't impeach; capital punishment vs. life in prison; wall vs. no wall; legalizing marijuana vs. not; self-driving cars vs. human drivers; Red Sox vs. Yankees; takeout vs. home-cooked; or Gone With the Wind vs. any other movie. All of these issues are stunningly important, right up to the second where cataclysm falls and creates a nightmare scenario that so many fear. That cataclysm is a complete loss of electricity and every mode of convenience and survival we take for granted. IS NORTH KOREA'S EMP THREAT REAL OR'SOMETHING OUT OF A JAMES BOND MOVIE'?

The greatest impact of intelligent technologies won't be from eliminating jobs but from changing what people do and driving innovation deeper into the business. Thoughtful adoption of intelligent technologies will be essential to survival for many companies. But simply implementing the newest technologies and automation tools won't be enough. Success will depend on whether organizations use them to innovate in their operations and in their products and services -- and whether they acquire and develop the human capital to do so. In a recent Deloitte survey of 250 executives familiar with how their companies are thinking about intelligent technologies, nearly three out of four said that they expected AI to substantially transform their organizations within three years.1

AdMobilize, headquartered in Miami, FL with offices in London, UK, Bogota, Colombia, Sao Paulo, Brazil is a venture-backed AI company with seamless solutions for implementing advanced computer vision technologies in the brick and mortar world. The company has one clear mission; connecting the physical world to the online grid. Our "drop in" solutions yield to each customer's hardware/software needs. AdMobilize's suite of analytics and engagement products are designed to be "Plug and Measure", enabling real-time audience analytics and intelligence to be instantly activated at scale on any software/hardware platform. AdMobilize offers the industry's most complete and accurate analytics/engagement solution for digital signage, OOH, DOOH, retail, live events, small business, malls, restaurants, and beyond.

In a commonly-used version of the Simple Assembly Line Balancing Problem (SALBP-1) tasks are assigned to stations along an assembly line with a fixed cycle time in order to minimize the required number of stations. It has traditionally been assumed that the total work needed for each product unit has been partitioned into economically indivisible tasks. However, in practice, it is sometimes possible to divide particular tasks in limited ways at additional time penalty cost. Despite the penalties, task division where possible, now and then leads to a reduction in the minimum number of stations. Deciding which allowable tasks to divide creates a new assembly line balancing problem, TDALBP (Task Division Assembly Line Balancing Problem). We propose a mathematical model of the TDALBP, an exact solution procedure for it and present promising computational results for the adaptation of some classical SALBP instances from the research literature. The results demonstrate that the TDALBP sometimes has the potential to significantly improve assembly line performance.

Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph or a hypergraph in which edges depict relationships of lexical similarity between sentences. Such approaches fail to capture semantic similarities between sentences when they express a similar information but have few words in common and are thus lexically dissimilar. To overcome this issue, we propose to extract semantic similarities based on topical representations of sentences. Inspired by the Hierarchical Dirichlet Process, we propose a probabilistic topic model in order to infer topic distributions of sentences. As each topic defines a semantic connection among a group of sentences with a certain degree of membership for each sentence, we propose a fuzzy hypergraph model in which nodes are sentences and fuzzy hyperedges are topics. To produce an informative summary, we extract a set of sentences from the corpus by simultaneously maximizing their relevance to a user-defined query, their centrality in the fuzzy hypergraph and their coverage of topics present in the corpus. We formulate a polynomial time algorithm building on the theory of submodular functions to solve the associated optimization problem. A thorough comparative analysis with other graph-based summarization systems is included in the paper. Our obtained results show the superiority of our method in terms of content coverage of the summaries.

Semi-analytic models are best suited to compare galaxy formation and evolution theories with observations. These models rely heavily on halo merger trees, and their realistic features (i.e., no drastic changes on halo mass or jumps on physical locations). Our aim is to provide a new framework for halo merger tree generation that takes advantage of the results of large volume simulations, with a modest computational cost. We treat halo merger tree construction as a matrix generation problem, and propose a Generative Adversarial Network that learns to generate realistic halo merger trees. We evaluate our proposal on merger trees from the EAGLE simulation suite, and show the quality of the generated trees.

Assessment of mental workload in real world conditions is key to ensure the performance of workers executing tasks which demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end. However, EEG correlates of mental workload vary across subjects and physical strain, thus making it difficult to devise models capable of simultaneously presenting reliable performance across users. The field of domain adaptation (DA) aims at developing methods that allow for generalization across different domains by learning domain-invariant representations. Such DA methods, however, rely on the so-called covariate shift assumption, which typically does not hold for EEG-based applications. As such, in this paper we propose a way to measure the statistical (marginal and conditional) shift observed on data obtained from different users and use this measure to quantitatively assess the effectiveness of different adaptation strategies. In particular, we use EEG data collected from individuals performing a mental task while running in a treadmill and explore the effects of different normalization strategies commonly used to mitigate cross-subject variability. We show the effects that different normalization schemes have on statistical shifts and their relationship with the accuracy of mental workload prediction as assessed on unseen participants at train time.

Artificial intelligence is one of the technologies with the most transformative potential in business. According to research by McKinsey, 70 per cent of companies are likely to have adopted at least one form of AI by 2030. This will contribute to an additional $13tr of global economic activity. Machine learning – a subset of artificial intelligence – enables machines to get better at executing tasks without human intervention, by finding patterns in data, and learning from their experience. It's no surprise, therefore, that there has been an explosion in the number of machine learning companies worldwide.

The formalism of anchor words has enabled the development of fast topic modeling algorithms with provable guarantees. In this paper, we introduce a protocol that allows users to interact with anchor words to build customized and interpretable topic models. Experimental evidence validating the usefulness of our approach is also presented.