Global reinsurance giant Munich Re is working with Starmind to transform and improve the reinsurer's knowledge management through advanced technology and artificial intelligence (AI). Starmind's technology is designed to expand human ingenuity by applying neuroscientific principles to AI, and reinsurer Munich Re is looking to transform its knowledge management via a new application called OneMind. OneMind aims to enable Munich Re to engage and cultivate the expertise of its employees, improving the company's knowledge management via the leverage of internal knowledge and expertise as a competitive advantage. The application of self-learning algorithms based on neuroscientific principles enables Starmind to build a firm's'corporate brain,' which in turn enables employees to better identify and connect with the most appropriate internal expert for almost any query, as well as having access to internal knowledge quickly and efficiently. "Many of our clients face increasingly complex and ever evolving new risks. To provide them with best in class solutions in a swift and efficient way we need to have a profound understanding of how trends and developments drive clients' needs and interests. "We have implemented OneMind because it enables us to easily use our worldwide know-how for either quickly identifying possible solutions that matched similar client needs or for designing new tailor made solutions.

A television ad for Amazon's Echo Dot smart speaker that caused a viewer's device to try to order cat food has been cleared by a UK regulator. The advert, which aired in October, featured a man asking Amazon's voice assistant Alexa to order Purina cat food. A viewer said the ad caused their Echo Dot device to respond after hearing the ad on the television. The viewer complained that the ad was "socially irresponsible". The Advertising Standards Authority (ASA) announced that it would not uphold the consumer's complaint because it did not find the advert to be in breach of the UK Code of Broadcast Advertising.

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Wiraya Activation Intelligence will use machine learning to help mobile operators serve millions of customers per day across Europe.

Today, top individuals from around the world convened at the World Government Summit to discuss the agenda that should govern the next generation of governments. Yesterday, a select few of these leaders gathered in a closed-door meeting to discuss the guidelines that nations should use as they help ...

A machine learning technology developed to spot dangerous medical conditions before they worsen has shown its value in a trial. Based in Kfar Malal, Israel, Medial EarlySign is one of dozens of companies now developing Artificial Intelligence algorithms which can scan large volumes of medical records to pick out warning signs buried in patient data. Medial's latest algorithm looks to identify diabetes patients who are at highest risk of having renal dysfunction within the next 12 months. Medial EarlySign's machine learning-based model analysed dozens of factors contained in Electronic Health Records (EHRs), including laboratory test results, demographics, medication, diagnostic codes and others, to predict who might be at high risk for having renal dysfunction within the period. The company chose to isolate less than 5% of the 400,000 diabetic population selected from its database of 15 million patients, and the algorithm was able to identify 45% of patients who would progress to significant kidney damage within a year, prior to becoming symptomatic.

New insight has emerged on the contents of Elon Musk's Falcon Heavy, which blasted off from Cape Canaveral earlier this month. The Tesla Roadster is carrying a small disc developed by researchers at the University of Southampton in England. The disc, which looks like a shrunken DVD, is storing information on human knowledge and was designed by a group aiming to preserve history called the Arch Mission Foundation. This includes Isaac Asimov's Foundation trilogy, a series of science fiction books. A disc made by the Arch Mission Foundation is on board Elon Musk's orbiting Tesla Roadster.

The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters. Additionally, due to changes in the system in time and in order to increase the accuracy and robustness of the prediction, the Ensemble Kalman Filter (EnKF) is used to update the flow rate predictions based on new observations. The developed approach was tested on the data from two mature gas production wells in which their production is highly dynamic and suffering from salt deposition. The results show that the flow predictions using the EnKF updated model leads to better Jeffreys' J-divergences than the predictions without the EnKF model updating scheme.

Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. Most popular ICA methods use kurtosis as a metric of non-Gaussianity to maximize, such as FastICA and JADE. However, their assumption of fourth-order moment (kurtosis) may not always be satisfied in practice. One of the possible solution is to use third-order moment (skewness) instead of kurtosis, which was applied in $ICA_{SG}$ and EcoICA. In this paper we present a competitive approach to ICA based on the Split Generalized Gaussian distribution (SGGD), which is well adapted to heavy-tailed as well as asymmetric data. Consequently, we obtain a method which works better than the classical approaches, in both cases: heavy tails and non-symmetric data. \end{abstract}

Deep learning methods are useful for high-dimensional data and are becoming widely used in many areas of software engineering. Deep learners utilizes extensive computational power and can take a long time to train-- making it difficult to widely validate and repeat and improve their results. Further, they are not the best solution in all domains. For example, recent results show that for finding related Stack Overflow posts, a tuned SVM performs similarly to a deep learner, but is significantly faster to train. This paper extends that recent result by clustering the dataset, then tuning very learners within each cluster. This approach is over 500 times faster than deep learning (and over 900 times faster if we use all the cores on a standard laptop computer). Significantly, this faster approach generates classifiers nearly as good (within 2\% F1 Score) as the much slower deep learning method. Hence we recommend this faster methods since it is much easier to reproduce and utilizes far fewer CPU resources. More generally, we recommend that before researchers release research results, that they compare their supposedly sophisticated methods against simpler alternatives (e.g applying simpler learners to build local models).