When we hear technology we think of electronic gadgets and a hundred types of software. But the problems of the future are going to be more basic. Food, water, and shelter are important to talk about. They're essential to sustain human life and limited in availability. Moreover, the increasing population and concentration of population in major cities will possibly lead to scarcity unless we take due action.

Paull Allen who is Co-founder of Microsoft published results of experiment with great elephant's census around 2 years ago. This was an effort to count Savannah elephants in Africa. Between 2007 in 2014, Population of elephants was decrease by about the third and was about to vanish completely from some African countries. Researchers were able to use artificial intelligence technology to reverse this trend, which really help them to rewrite the rules for everything which we are doing for household appliances, through AI. Ability of artificial intelligence can help in finding patterns and gathering information which then further can be used to modify behaviour.

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.