ZURICH – Zurich Insurance is deploying artificial intelligence (AI) in deciding personal injury claims after test trials cut the processing time from an hour to just seconds, its chairman said. "We recently introduced AI claims handling … and saved 40,000 work hours, while speeding up the claim processing time to five seconds," Tom de Swaan said in an interview after the insurer started using machines in March to review paperwork, such as medical reports. "We absolutely plan to expand the use of this type of AI," he said. Insurers are racing to hone the benefits of technological advancements such as big data and AI as tech-driven startups, like Lemonade Inc., enter the market. Lemonade promises renters and homeowners insurance in as little as 90 seconds and payment of claims in three minutes with the help of artificial intelligence bots that set up policies and process claims.

Incumbents should consider partnerships and find new value-added services. For a long time, insurance proved resistant to digital technology's disruptive power. Complex regulation, the capital reserves required to underwrite insurance, and underwriting skills and proprietary data built on years of experience kept the industry protected. But these barriers are rapidly eroding. For the most part, the main threat is not from insurtechs, the nimble new tech start-ups that have thus far focused on property-and-casualty (P&C) insurance, as well as marketing and distribution, and into which venture capitalists have poured $4.4 billion in the past two years.

An engineer at IBM once told me that the future of information technology could be summed up in a few key terms: mobility, cloud computing, the internet of things, and automation and artificial intelligence. It is worth bearing in mind that automation and artificial intelligence are becoming more and more prominent topics within the public sphere, especially with the emergence of powerful AI like IBM's Watson and DeepMind's AlphaGo. These forms of artificial intelligence, also referred to as robots or bots for short, don't necessarily take up physical space. Instead, they are programs, stored on a desktop or a cloud, that have the ability to learn and adapt to different situations as opposed to earlier programs that were more rigid. Because of their learning capabilities, these robots can perform tasks that were deemed impossible by earlier programmers: Robots can write stories, they can understand human speech, and they can diagnose a patient better than their own doctor can.

A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."

In this paper, we present a novel method for co-clustering, an unsupervised learning approach that aims at discovering homogeneous groups of data instances and features by grouping them simultaneously. The proposed method uses the entropy regularized optimal transport between empirical measures defined on data instances and features in order to obtain an estimated joint probability density function represented by the optimal coupling matrix. This matrix is further factorized to obtain the induced row and columns partitions using multiscale representations approach. To justify our method theoretically, we show how the solution of the regularized optimal transport can be seen from the variational inference perspective thus motivating its use for co-clustering. The algorithm derived for the proposed method and its kernelized version based on the notion of Gromov-Wasserstein distance are fast, accurate and can determine automatically the number of both row and column clusters. These features are vividly demonstrated through extensive experimental evaluations.

This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen as a kernel mixture network with square and non-overlapping kernels. We test the performance of our method on two important applications, namely Bayesian filtering and generative modeling. In the Bayesian filtering example, we show that the method can be used to filter complex nonlinear and non-Gaussian signals defined on manifolds. The resulting kernel mixture network filter outperforms both the quantized softmax filter and the extended Kalman filter in terms of model likelihood. Finally, our experiments on generative models show that, given the same architecture, the kernel mixture network leads to higher test set likelihood, less overfitting and more diversified and realistic generated samples than the quantized softmax approach.

Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated.

Helping the elderly stay in their homes with an assistive robot, giving those with disabilities greater independence by increasing their mobility and improving safety for workers across industries with a single invention sounds ambitious, but that's just Genesis Robotics wants to do with its newly unveiled LiveDrive, a direct-drive robotic actuator. "I think we can even help people walk," Genesis Robotics and LiveDrive President Michael Gibney told International Business Times. "If we can get an exoskeleton, get people out of a wheel chair, we'll be able to really change people's lives." Genesis Robotics hopes to change the way robots are made with its LiveDrive actuator. LiveDrive aims to replace bulky motors, drive belts and gearboxes that limit existing robots in terms of load-bearing, precision, speed and flexibility of use.

Whenever I read articles about data science I feel like there is some important aspect missing: evaluating the performance and quality of a machine learning model. There is always a neat problem at hand that gets solved and the process of data acquisition, handling and model creation is discussed, but the evaluation aspect too often is very brief. But I truly believe it's the most important fact, when building a new model. Consequently, the first post on this blog will deal with a pretty useful evaluation technique: lift analysis. Machine learning covers a wide variety of problems like regression and clustering.

The rise of artificial intelligence and robotics in the workplace is inevitable, but what does that mean for the recruitment industry? With robots building everything from our computers to our cars, there is always the fear that the human workforce will become redundant in the future. Researchers from Oxford University found that over the following 20 years, 35% of current jobs in the UK are at high risk of computerisation. Artificial intelligence offers benefits such as efficient management of time, speed, precision and costs less than hiring an employee to perform the same job. Although this may seem impressive, robots lack human emotion, judgement and the ability to think independently. These traits are important in many industries, one of them being recruitment.