With conversational commerce mushrooming throughout the retail world, it surely must have the feel of déjà vu for the iconic 1-800-Flowers.com Founded in 1976 and branded with its workhorse 1-800-Flowers phone number in 1986, the company can point to its penchant for speaking directly to its customers as a reason for its longevity -- first through the phone and then across the internet. The process of conversational commerce uses remarkably intuitive technology tools such as voice messaging and chatbot applications to facilitate seamless interactions between brands and shoppers to drive transactions or trigger service. Voice protocols are emerging as the dominant technique fueling conversational commerce, notably voice assistants such as Apple Siri, Google Assistant and Amazon Alexa. In addition, text-enabled chatbots are proliferating to help consumers through human-like conversations.

Gartner, Inc. today revealed its top strategic predictions for 2020 and beyond. Gartner's top predictions examine how the human condition is being challenged as technology creates varied and ever-changing expectations of humans. "Technology is changing the notion of what it means to be human," said Daryl Plummer, distinguished vice president and Gartner Fellow. "As workers and citizens see technology as an enhancement of their abilities, the human condition changes as well. CIOs in end-user organizations must understand the effects of the change and reset expectations for what technology means."

The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the first graduate-level, research-based artificial intelligence (AI) university in the world, has received immediate interest from graduate students across the globe. So far, 3,200 students have started the application process, including 1,681 potential students in the last step of their application process and 234 completing their applications within the first week of the university's launch on October 16. The majority of applications were received from the UAE, Saudi Arabia, Algeria, Egypt, India, and China, a statement said. Dr Sultan Ahmed Al Jaber, UAE Minister of State and chairman of the MBZUAI board of trustees, said: "The level of interest in such a short time is a very encouraging sign. MBZUAI is attracting prospective students from around the world, affirming the UAE leadership's vision of investing in human potential and enabling societies through knowledge and education to find practical solutions to some of the biggest challenges in the world, and further establishing the UAE and Abu Dhabi as a global hub for innovation and higher education."

We consider the case of derivative-free algorithms for non-convex optimization, also known as zero order algorithms, that use only function evaluations rather than gradients. For a wide variety of gradient approximators based on finite differences, we establish asymptotic convergence to second order stationary points using a carefully tailored application of the Stable Manifold Theorem. Regarding efficiency, we introduce a noisy zero-order method that converges to second order stationary points, i.e avoids saddle points. Our algorithm uses only $\tilde{\mathcal{O}}(1 / \epsilon^2)$ approximate gradient calculations and, thus, it matches the converge rate guarantees of their exact gradient counterparts up to constants. In contrast to previous work, our convergence rate analysis avoids imposing additional dimension dependent slowdowns in the number of iterations required for non-convex zero order optimization.

This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and burstiness. The PRGDS is based on a new motif in Bayesian latent variable modeling, an alternating chain of discrete Poisson and continuous gamma latent states that is analytically convenient and computationally tractable. This motif yields closed-form complete conditionals for all variables by way of the Bessel distribution and a novel discrete distribution that we call the shifted confluent hypergeometric distribution. We draw connections to closely related models and compare the PRGDS to these models in studies of real-world count data sets of text, international events, and neural spike trains. We find that a sparse variant of the PRGDS, which allows the continuous gamma latent states to take values of exactly zero, often obtains better predictive performance than other models and is uniquely capable of inferring latent structures that are highly localized in time.

The reports provide market insights into demand drivers, regional outlook, and competitive analysis of the Cognitive Computing market for the Cognitive Computing forecast period. Further, it throws focus on restraints as well discusses future chances at length that are likely to come to the fore over the forecast period. The analysis thus provided helps market stakeholders with business planning and to gauge scope of expansion in the Cognitive Computing market over the forecast period. Moreover, the report has explored changing factors for the market segments. It covers the growth factors of the worldwide market based on end-users. It's a well-crafted Cognitive Computing market research report which has been designed using the primary and secondary sources.

A few years ago, Paul Allen, the co-founder of Microsoft, published the results of something called the Great Elephant Census, which counted all the savanna elephants in Africa. What it found rocked the conservation world: In the seven years between 2007 and 2014, Africa's savanna elephant population decreased by about a third and was on track to disappear completely from some African countries in as few as 10 years. To reverse that trend, researchers landed on a technology that is rewriting the rules for everything from our household appliances to our cars: artificial intelligence. AI's ability to find patterns in enormous volumes of information is demystifying not just elephant behavior but human behavior -- specifically poacher behavior -- too. "AI can process huge amounts of information to tell us where the elephants are, how many there are," said Cornell University researcher Peter Wrege. "And ideally tell us what they are doing."

Expectation propagation (EP) is a powerful approximate inference algorithm. However, a critical barrier in applying EP is that the moment matching in message updates can be intractable. Handcrafting approximations is usually tricky, and lacks generalizability. Importance sampling is very expensive. While Laplace propagation provides a good solution, it has to run numerical optimizations to find Laplace approximations in every update, which is still quite inefficient. To overcome these practical barriers, we propose conditional expectation propagation (CEP) that performs conditional moment matching given the variables outside each message, and then takes expectation w.r.t the approximate posterior of these variables. The conditional moments are often analytical and much easier to derive. In the most general case, we can use (fully) factorized messages to represent the conditional moments by quadrature formulas. We then compute the expectation of the conditional moments via Taylor approximations when necessary. In this way, our algorithm can always conduct efficient, analytical fixed point iterations. Experiments on several popular models for which standard EP is available or unavailable demonstrate the advantages of CEP in both inference quality and computational efficiency.

Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.

The days of construction projects running behind schedule and over budget could soon be over as AI technology tries to solve the U.S. productivity puzzle. Disperse, an AI-powered construction firm, has raised fresh finance to expand into the U.S. in a bid to tackle inefficiencies on building sites. The company's technology uses visual snapshots of construction projects to alert managers about potential problems before they happen. The construction sector has been grappling with low levels of productivity for decades, with underinvestment in technology one of the key factors. Closing the productivity gap in global construction could be worth $1.6 trillion a year, with a third of that coming in the U.S., according to the McKinsey Global Institute.