Amy from x.ai Melody Medical Bot from Baidu Travel, Shopping and Customer Service Bots: Claire, Clara, Julie, Ann, Messenger Bot from Kayak, KLM, Expedia … 10. Machine Learning – Need to mitigate Business concerns 1 • Data without biases • Transfer tribal Knowledge to Data • Compliances • Social nuances • Voice to Speech challenges IoTDisruptions.com Amy from x.ai Melody Medical Bot from Baidu Travel, Shopping and Customer Service Bots: Claire, Clara, Julie, Ann, Messenger Bot from Kayak, KLM, Expedia …

The stochastic block model (SBM) is a random graph model with planted clusters. It is widely employed as a canonical model to study clustering and community detection, and provides generally a fertile ground to study the statistical and computational tradeoffs that arise in network and data sciences. This note surveys the recent developments that establish the fundamental limits for community detection in the SBM, both with respect to information-theoretic and computational thresholds, and for various recovery requirements such as exact, partial and weak recovery (a.k.a., detection). The main results discussed are the phase transitions for exact recovery at the Chernoff-Hellinger threshold, the phase transition for weak recovery at the Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial recovery, the learning of the SBM parameters and the gap between information-theoretic and computational thresholds. The note also covers some of the algorithms developed in the quest of achieving the limits, in particular two-round algorithms via graph-splitting, semi-definite programming, linearized belief propagation, classical and nonbacktracking spectral methods. A few open problems are also discussed.

The global energy industry is facing disruption as it transitions from fossils to renewables (and occasionally back again). Its challenges include balancing growing demand in developing nations with the need for sustainability, and predicting the effect of extreme weather conditions on supply and demand. Against this backdrop, GE Power – whose turbines and generators supply 30 per cent of the world's electricity – has been working on applying Big Data, machine learning and Internet of Things (IoT) technology to build an "internet of power" to replace the linear, one-way traditional model of energy delivery. Ganesh Bell – first and current Chief Data Officer at GE Power, tells me "The biggest opportunity is that, if you think about it, the electricity industry is still following a one-hundred-year-old model which our founder, Edison, helped to proliferate. "It's the generation of electrons in one source which are then transmitted in a one-way linear model.

The global energy industry is facing disruption as it transitions from fossils to renewables (and occasionally back again). Its challenges include balancing growing demand in developing nations with the need for sustainability, and predicting the effect of extreme weather conditions on supply and demand. Against this backdrop, GE Power - whose turbines and generators supply 30 per cent of the world's electricity - has been working on applying Big Data, machine learning and Internet of Things (IoT) technology to build an "internet of power" to replace the linear, one-way traditional model of energy delivery. Ganesh Bell – first and current Chief Data Officer at GE Power, tells me "The biggest opportunity is that, if you think about it, the electricity industry is still following a one-hundred-year-old model which our founder, Edison, helped to proliferate. "It's the generation of electrons in one source which are then transmitted in a one-way linear model.

This article presents a logical formalization of the emotional appraisal theory, i.e., it formalizes the cognitive process of evaluation that elicits an emotion. This formalization is psychologically grounded on the OCC cognitive model of emotions. More specifically, we are interested in event-based emotions, i.e., emotions that are elicited by the evaluation of the consequences of an event that either happened or will happen. The formal modelling presented here is based on the AfPL Probabilistic Logic, a BDI-like probabilistic modal logic, which allows our model to verify whether the variables that determine the elicitation of emotions achieved the necessary threshold or not. The proposed logical formalization aims at addressing how the emotions are elicited by the agent cognitive mental states (desires, beliefs and intentions), and how to represent the intensity of the emotions. These are important initial points in the investigation of the dynamic interaction among emotions and other mental states.

Representatives from angel investors, venture capital, private equity and corporate venture funds awarded Movus, started by technology strategy consultant Brad Parsons in January 2015, the Investor Award at KPMG Australia's Energise accelerator program last Friday. Having developed an automation monitoring blueprint for BHP Billiton and Aurizon trains and condition monitoring blueprint for Sydney Trains while working for Perth-based Ajilon Australia, Parsons realised the technology was totally applicable for the billions of assets globally that sit below those big heavy assets. Parsons has now developed a condition monitoring sensor that works in the industrial environment picking up the health of machinery using artificial intelligence and a machine learning engine on the backend. It is magnetically attached and can be installed in minutes, the same as a Fitbit for humans. Parsons has already talked about it with Energise sponsors Woodside Petroleum and Chevron Corporation, and will meet Wesfarmers and Western Australia's Water Corporation representatives in Perth in the next fortnight.

We consider the exploration-exploitation tradeoff in linear quadratic (LQ) control problems, where the state dynamics is linear and the cost function is quadratic in states and controls. We analyze the regret of Thompson sampling (TS) (a.k.a. posterior-sampling for reinforcement learning) in the frequentist setting, i.e., when the parameters characterizing the LQ dynamics are fixed. Despite the empirical and theoretical success in a wide range of problems from multi-armed bandit to linear bandit, we show that when studying the frequentist regret TS in control problems, we need to trade-off the frequency of sampling optimistic parameters and the frequency of switches in the control policy. This results in an overall regret of $O(T^{2/3})$, which is significantly worse than the regret $O(\sqrt{T})$ achieved by the optimism-in-face-of-uncertainty algorithm in LQ control problems.

Even six years after a nuclear crisis struck Fukushima in Japan, radiation levels at Fukushima continued to reach the extreme levels. While most of this data is collected through cameras and robots, there is now a shadow of doubt about the future of these robots. Tokyo Electric Power Company Holdings Inc. (TEPCO), the operator of the Fukushima Daiichi plant, failed to get a comprehensive report in its attempt to find nuclear debris in a containment vessel with the help of the PMORPH survey robot – developed by Hitachi-GE Nuclear Energy and the International Research Institute for Nuclear Decommissioning (IRID) – on Thursday. This was the latest in the spate of robot failures in the process of decommissioning the plant. Last month, a Toshiba "scorpion" robot, built to tolerate up to 1,000 sieverts of radiation, was unable to withstand the high levels of nuclear toxicity in nuclear reactor No. 2. There have been a number of other instances, causing authorities to think of alternative approaches to the clean-up.

This paper introduces a method for efficiently inferring a high-dimensional distributed quantity from a few observations. The quantity of interest (QoI) is approximated in a basis (dictionary) learned from a training set. The coefficients associated with the approximation of the QoI in the basis are determined by minimizing the misfit with the observations. To obtain a probabilistic estimate of the quantity of interest, a Bayesian approach is employed. The QoI is treated as a random field endowed with a hierarchical prior distribution so that closed-form expressions can be obtained for the posterior distribution. The main contribution of the present work lies in the derivation of \emph{a representation basis consistent with the observation chain} used to infer the associated coefficients. The resulting dictionary is then tailored to be both observable by the sensors and accurate in approximating the posterior mean. An algorithm for deriving such an observable dictionary is presented. The method is illustrated with the estimation of the velocity field of an open cavity flow from a handful of wall-mounted point sensors. Comparison with standard estimation approaches relying on Principal Component Analysis and K-SVD dictionaries is provided and illustrates the superior performance of the present approach.

March 23, 2017 --If some NASA researchers have their way, Mars exploration technology of the future may rely on an art form from the past. NASA's Jet Propulsion Laboratory (JPL) has developed a Pop-Up Flat Folding Explorer Robot (PUFFER) prototype that could change how we explore Mars. The rugged yet portable machine takes its inspiration from the art of origami, which, despite Americans' association with grade-school arts and crafts, is proving to be a cutting-edge design philosophy. Recent developments in the field have led to an explosion of uses ranging from solar panels to bulletproof barriers. What sets PUFFER apart from other rovers is that it folds flat, making its mini-profile even slimmer.