Man-made brainpower (AI) will soon be at the core of each major technological framework on the planet to manage and get to your strategic information. Only a couple of uses are cyber and homeland security, anti-money laundering, payments, financial markets, biotech, healthcare, marketing, natural language processing (NLP), computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). Artificial Intelligence is turning into a significant staple of innovation, scarcely any individuals comprehend the advantages and weaknesses of AI and Machine Learning innovations. While machine intelligence is sure to assume a key role in the making of cutting edge frameworks in a wide assortment of industry areas sooner rather than later, it is especially applicable in quickly developing businesses, for example, ICT, manufacturing and transportation. Over the globe, mobile operators are preparing to deploy the fifth era of 3GPP mobile wireless networks (5G).

Artificial Intelligence in Practice is a practical resource that demystifies how Artificial Intelligence (AI) and machine learning can be used to solve common business challenges and open the door to opportunities that often exceed expectations. The book is filled with insights from some of the most important AI giants including Google, Microsoft, Amazon, Alibaba, and other forward thinking industry leaders. It also presents compelling case studies from traditional businesses and startups, that detail how AI is being applied in the real world of business. Bestselling author and AI expert Bernard Marr offers detailed examinations of 50 companies that have successfully integrated AI into their business practices. He provides an overview of each company, describes the specific problem AI addressed and explains how AI offered a workable solution.

Our society relies heavily on digital devices and channels, and with that the concept of identity has quickly become the foundation of every customer interaction--particularly within the digital advertising ecosystem. In response to the emerging strategic importance of identity, Experian today announced a new innovative solution that uses the fusion of data and artificial intelligence, to help marketers connect Mobile Ad IDs (MAIDs) with digital and offline identity attributes to better understand their target audiences. Powered by Experian's vast data assets and identity platform, the new solution incorporates machine-learning algorithms, as well as deterministic and probabilistic techniques, to sift and connect billions of advanced identity signals and data elements, including MAIDs, from a wide variety of internal and external sources. The outcome of this process allows brand marketers to implement more effective analytics, audience segmentation and activation, and measurement capabilities. "Experian has always been a leader in identity resolution, helping brand marketers more accurately identify and understand customers, while also keeping customers at the heart of every marketing strategy," said Kevin Dean, Experian's president and general manager of Marketing Services, North America.

Human emotions unfold over time, and more affective computing research has to prioritize capturing this crucial component of real-world affect. Modeling dynamic emotional stimuli requires solving the twin challenges of time-series modeling and of collecting high-quality time-series datasets. We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches in affective computing, including discriminative and generative models. We then introduce the first version of the Stanford Emotional Narratives Dataset (SENDv1): a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models. We demonstrate several baseline and state-of-the-art modeling approaches on the SEND, including a Long Short-Term Memory model and a multimodal Variational Recurrent Neural Network, which perform comparably to the human-benchmark. We end by discussing the implications for future research in time-series affective computing.

Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in practice, labeled data may be collected from multiple sources, while naive application of the single-source DA algorithms may lead to suboptimal solutions. In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones. Specifically, the proposed MDDA includes four stages: (1) pre-train the source classifiers separately using the training data from each source; (2) adversarially map the target into the feature space of each source respectively by minimizing the empirical Wasserstein distance between source and target; (3) select the source training samples that are closer to the target to fine-tune the source classifiers; and (4) classify each encoded target feature by corresponding source classifier, and aggregate different predictions using respective domain weight, which corresponds to the discrepancy between each source and target. Extensive experiments are conducted on public DA benchmarks, and the results demonstrate that the proposed MDDA significantly outperforms the state-of-the-art approaches. Our source code is released at: https://github.com/daoyuan98/MDDA.

SW AG: Item Recommendations using Convolutions on Weighted Graphs Amit Pande, Kai Ni and V enkataramani Kini Data Sciences, Target Corporation Abstract --Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SW AG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weighted graphs to generate embed-dings for nodes (items) that incorporate both graph structure as well as node feature information such as item-descriptions and item-images. The three important SWAG operations that enable us to efficiently generate node embeddings based on graph structures are (a) Sampling of graph to homogeneous structure, (b) W eighting the sampling, walks and convolution operations, and (c) using AGgregation functions for generating convolutions. The work is an adaptation of graphSAGE over weighted graphs. We deploy SW AG at T arget and train it on a graph of more than 500K products sold online with over 50M edges. Offline and online evaluations reveal the benefit of using a graph-based approach and the benefits of weighing to produce high quality embeddings and product recommendations. I NTRODUCTION Convolutional Neural Networks (CNNs) are used to establish state-of-the-art performance on many Computer Vision applications [2]. CNNs consist of a series of parameterized convolutional layers operating locally (around neighboring pixels of an image) to obtain hierarchy of features about an image. The first layer learns simple edge-oriented detectors. Higher layers build up on the learning of lower layers to learn more complex features and objects. The success of CNNs in Computer Vision has inspired efforts to extend the convolu-tional operation from regular grids (2D images), to graph-structured data [9]. Graphs, such as social networks, word co-occurrence networks, guest purchasing behavior, protein-protein interactions and communication networks, occur naturally in various real-world applications. Analyzing them yields insights into the structure of society, language, and different patterns of communication.

Recently, it has been shown that spiking neural networks (SNNs) can be trained efficiently, in a supervised manner, using backpropagation through time. Indeed, the most commonly used spiking neuron model, the leaky integrate-and-fire neuron, obeys a differential equation which can be approximated using discrete time steps, leading to a recurrent relation for the potential. The firing threshold causes optimization issues, but they can be overcome using a surrogate gradient. Here, we extend previous approaches in two ways. Firstly, we show that the approach can be used to train convolutional layers. Convolutions can be done in space, time (which simulates conduction delays), or both. Secondly, we include fast horizontal connections \`a la Den\`eve: when a neuron N fires, we subtract to the potentials of all the neurons with the same receptive the dot product between their weight vectors and the one of neuron N. As Den\`eve et al. showed, this is useful to represent a dynamic multidimensional analog signal in a population of spiking neurons. Here we demonstrate that, in addition, such connections also allow implementing a multidimensional send-on-delta coding scheme. We validate our approach on one speech classification benchmarks: the Google speech command dataset. We managed to reach nearly state-of-the-art accuracy (94%) while maintaining low firing rates (about 5Hz). Our code is based on PyTorch and is available in open source at http://github.com/romainzimmer/s2net

In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of well-known BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999-2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages.

Artificial Intelligence (AI): The fundamental change in our daily work routine, the way we live and interact with others, is all going to be represented by the fourth industrial revolution. Moving from the third to the fourth industrial revolution is going to open a new chapter in human development – incorporating the extraordinary technological advances. These advanced technologies are emerging and will continue to merge in the business world. We see the fourth industrial revolution changing the digital, physical, and biological worlds. It is creating novel opportunities and promises of a better future. On the other hand, the evolution of technology will become the reason for potential risks and dangers.

Besides being a hot topic these days, emerging technologies such as artificial intelligence and blockchain have received a reputation for being especially male-dominated in an already bro-saturated tech world. However, the buzz around artificial intelligence and cryptography isn't without merit, as these technologies are much more than just one more thing to be mansplained. "It's expected that soon, artificial intelligence will combine the intricacy and pattern recognition strength of human intelligence with the speed, memory and knowledge sharing of machine intelligence." Similarly, decentralized blockchain systems have the potential to change our lives from the way we do business, to the way we drive, vote, make purchases and even prove our identity. With such diverse and far-reaching applications, it is clear that a diversity of perspectives will be necessary to create effective and sustainable solutions.