Artificial intelligence solutions are now essential weapons in the insurers' battle against fraud. FREMONT, CA: The insurance industry is held responsible for a mass of sensitive data concerning both its customers and employees. Any data breach in an insurance firm could compromise the personal information of multiple users in no time. But insurers now have the option of attaining better cybersecurity posture by utilizing groundbreaking technologies available to them. Artificial Intelligence (AI) among those, is truly reforming insurance systems by making it more secure and enhancing the interaction between humans and machines.

Machine learning has made tremendous progress in recent years and received large amounts of public attention. Though we are still far from designing a full artificially intelligent agent, machine learning has brought us many applications in which computers solve human learning tasks remarkably well. Much of this progress comes from a recent trend within machine learning, called deep learning. Deep learning models are responsible for many state-of-the-art applications of machine learning. Despite their success, deep learning models are hard to train, very difficult to understand, and often times so complex that training is only possible on very large GPU clusters. Lots of work has been done on enabling neural networks to learn efficiently. However, the design and architecture of such neural networks is often done manually through trial and error and expert knowledge. This thesis inspects different approaches, existing and novel, to automate the design of deep feedforward neural networks in an attempt to create less complex models with good performance that take away the burden of deciding on an architecture and make it more efficient to design and train such deep networks.

Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.

ABOUT: You want to include a machine learning component in your IT systems? The process is a little more involved than clicking through an AI tutorial on your laptop. It's not just the first working model you run that you need to consider; you also need to think about things like integration, scaling, and testing. What's more, postlaunch, you'll want to continuously adapt your model to respond to the changing environment. Christoph and Arif will give an introduction into Continuous Delivery for Machine Learning (CD4ML) - a set of tools and processes that ensure that software under development in Machine Learning can be reliably released to production at any time and with high frequency.

While the United States currently has an edge in the race to develop artificial intelligence, China is rapidly gaining ground as Europe falls behind, according to a report released today by the Center for Data Innovation. The study arrives amid a wide-ranging debate about which region has gained AI leadership, and the implications that holds for dominating cutting-edge technologies such as autonomous vehicles and other forms of automation. The winners of an AI arms race could hold a significant economic advantage in the decades to come. There has been growing concern among U.S. tech companies and policymakers that China's initiative to make it dominant in AI by 2030 is allowing it to dictate this critical field. The ability of its central government to allow sweeping data gathering and determine official champions to lead this charge seems to have given its efforts significant momentum.

Detecting abrupt changes in time-series data has attracted rese archers in the statistics and data mining communities for decades Basseville and Nikiforov ( 1993). Based on the instantaneousness of detection, changepoint detection algorithm s can be classified into two categories: online changepoint detection and offline changepoint de tection. While the online change detection targets on data that requires instantaneous r esponses, the offline detection algorithm often triggers delay, which leads to more accurate result s. This literature review mainly focuses on the online changepoint detection algorithms. There are plenty of changepoint detection algorithms that have be en proposed and proved pragmatic. The pioneering works Basseville and Nikiforov ( 1993) compared the probability distributions of time-series samples over the past and pr esent intervals. The algorithm demonstrates an abrupt change when two distributions a re significantly different.

Proposed by Freund and Schapire ( 1997), boosting is a general issue of constructing an extremely accurate prediction with numerous roughly accurate pred ictions. Addressed by Friedman ( 2001, 2002) and Natekin and Knoll ( 2013), the Gradient Boosting Machines (GBM) seeks to build predictive models through back-fittings and no n-parametric regressions. Instead of building a single model, the GBM starts by generatin g an initial model and constantly fits new models through loss function minimization to prod uce the most precise model ( Natekin and Knoll, 2013). This survey concentrates on the mathematical derivations of the gradient boosting algorithms. In Section 2, we analyze the optimization methods for par ametric and nonparametric models. Section 3 covers the definitions of different typ es of loss functions. In Section 4, we present different types of boosting algorithms, while in Section 5, we explore the combination of boosting algorithms and ranking algorithms to ran k the real-world data.

Despite numerous research work in reinforcement learning (RL) and the recent successes obtained by combining it with deep learning, deep reinforcement learning (DRL) is still facing many challenges. Some of them, like the ability to abstract actions or the difficulty to explore the environment with sparse rewards, can be addressed by the use of intrinsic motivation. In this article, we provide a survey on the role of intrinsic motivation in DRL. We categorize the different kinds of intrinsic motivations and detail their interests and limitations. Our investigation shows that the combination of DRL and intrinsic motivation enables to learn more complicated and more generalisable behaviours than standard DRL. We provide an in-depth analysis describing learning modules through an unifying scheme composed of information theory, compression theory and reinforcement learning. We then explain how these modules could serve as building blocks over a complete developmental architecture, highlighting the numerous outlooks of the domain.

Rather than relying on exit interviews and their comparisons to occasional employee surveys to determine engagement, organizations can turn instead to big data and advanced analytics to identify those workers at greatest risk of quitting. A new Harvard Business Review article outlines how applying machine learning algorithms to turnover data and employee information can provide a much more accurate picture of workplace satisfaction. This measure of "turnover propensity" comprised two main indicators: turnover shocks, which are organizational and personal events that cause workers to reconsider their jobs, and job embeddedness, which describes an employee's social ties in their workplace and interest in the work they do. Though achieving this kind of "proactive anticipation" will require a sizable investment of time and effort to develop the necessary data and algorithms, the payoff will likely be worth it: "Leaders can proactively engage valued employees at risk of leaving through interviews, to better understand how the firm can increase the odds that they stay," per HBR. More articles on leadership and management: Can your anesthesia department handle NORA?

This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in vision and speech applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent vision and speech systems. With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation intelligent systems are poised to revolutionize personal and commercial computing. This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent vision and speech systems to date. An overview of large-scale industrial research and development efforts is provided to emphasize future trends and prospects of intelligent vision and speech systems. Robust and efficient intelligent systems demand low-latency and high fidelity in resource constrained hardware platforms such as mobile devices, robots, and automobiles. Therefore, this survey also provides a summary of key challenges and recent successes in running deep neural networks on hardware-restricted platforms, i.e. within limited memory, battery life, and processing capabilities. Finally, emerging applications of vision and speech across disciplines such as affective computing, intelligent transportation, and precision medicine are discussed. To our knowledge, this paper provides one of the most comprehensive surveys on the latest developments in intelligent vision and speech applications from the perspectives of both software and hardware systems. Many of these emerging technologies using deep neural networks show tremendous promise to revolutionize research and development for future vision and speech systems.