A year ago, we were so impressed with what was then called the Google Home Hub, we called it one of the top 10 best tech products of 2018, with one caveat. We wished it could be bigger. Now, the all-new, and yes, larger version is in stores, with a bigger 10-inch screen (up from 7 inches) higher price tag ($229, vs. $129) and a confusing new name. The new Google Nest Hub Max, with a ten-inch screen, back to back with the old Nest Hub, with a 7-inch screen. The old Home Hub is now called the Nest Hub, and the larger, new version is the Nest Hub Max.

Today's modern enterprises are collecting data at exponential rates, and it's no mystery that effectively making use of that data has become a top priority for many. According to a recent survey of 2000 global enterprises by McKinsey & Company, 47% of organizations have embedded at least one AI capability in their standard business processes. This is up from 20% in 2017 and it's clear that this growth has created a global race to enabling the next important evolution of business as we know it: The AI-first enterprise. But what does this actually mean? With investment in AI technologies poised to reach $9.5 billion over the next three years, the imminent opportunity involves embedding data and machine learning intelligence across the business at scale -- predicting the next best move for growth, making every product a data product, or creating entirely new data-driven revenue streams.

In machine learning, we often need to train a model with a very large dataset of thousands or even millions of records. The higher the size of a dataset, the higher its statistical significance and the information it carries, but we rarely ask ourselves: is such a huge dataset really useful? Or we could reach a satisfying result with a smaller, much more manageable one? Selecting a reasonably small dataset carrying the good amount of information can really make us save time and money. Let's make a simple mental experiment.

This paper focuses on reducing the computational cost of the Monte Carlo method for uncertainty propagation. Recently, Multi-Fidelity Monte Carlo (MFMC) method (Ng, 2013; Peherstorfer et al., 2016) and Multi-Level Monte Carlo (MLMC) method (Müller et al., 2013; Giles, 2015) were introduced to reduce the computational cost of Monte Carlo method by making use of low-fidelity models that are cheap to an evaluation in addition to the high-fidelity models. In this paper, we use machine learning techniques to combine the features of both the MFMC method and the MLMC method into a single framework called Multi-Fidelity-Multi-Level Monte Carlo (MFML-MC) method. In MFML-MC method, we use a hierarchy of proper orthogonal decomposition (POD) based approximations of high-fidelity outputs to formulate a MLMC framework. Next, we utilize Gradient Boosted Tree Regressor (GBTR) to evolve the dynamics of POD based reduced order model (ROM) (Xiao et al., 2017) on every level of the MLMC framework.

This article is sponsored by Oracle – redefining data management with the world's first autonomous database. In the coming years, the amount of data we create worldwide will grow to 175 zettabytes of data per year by 2025, up from 33 zettabytes in 2018. Over half of this data will be created by the Internet of Things devices and over 60% of it will be enterprise data. By 2025, 30% of all the data created will be in real-time, offering organisations great opportunities to constantly optimise their business. Clearly, the organisation of tomorrow is a data organisation.

In the age of Artificial Intelligence (AI) and Key Performance Indicators (KPI's) only creativity will open doors to new opportunities and long-term success. And when others think that this soon will be under the all-powerful paw of technologies, some believe that creativity will help humans thrive and monetize on their talents, knowledge and unusual approaches to problem-solving. Buckle up, because this is my first article of the Creativity series. And it seems there is quite a journey ahead! Always surrounded by scientists I got used to hearing about miracles being born within the walls of laboratories.

Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field.

Pharmaceutical giant Pfizer today announced plans to launch a one-year pilot program with robotics company Catalia Health, maker of Mabu, a home robot that coaches patients on health and prescription drugs. Mabu uses voice interactions powered by conversational AI to assess a user's mood, record data, manage symptoms, and provide helpful information. The robot then supplies information back to medical professionals -- like caregivers or clinicians -- such as the frequency of medication usage or questions the robot was unable to answer. Mabu is also able to supply personalized responses and deploy affective computing to predict a user's emotional state. The robot is designed to help ensure patients take their medication and adjust to any drastic lifestyle changes resulting from an affliction, CEO Cory Kidd told VentureBeat in an interview.

US artificial intelligence firm Digital Reasoning has received investment from Standard Chartered Bank's innovation, investment and ventures arm, SC Ventures, that brings its Series D-1 funding round total to US$40 million. The artificial intelligence (AI) and machine-learning company will partner with Standard Chartered to expand its financial services product offerings in communications surveillance across Asia Pacific, the Middle East and other international markets. The fresh funds will help it to expand into use cases within financial institutions that are adjacent to its core business of e-communications and voice surveillance for compliance, the company said. It will also support the company's efforts to broaden its pre-trained model catalog, supporting growth in financial markets across Asia, the Middle East and Africa. Digital Reasoning's Series D-1 funding round had a first close last year, led by BNP Paribas with participation from Barclays, Square Capital, Goldman Sachs, Nasdaq, Lemhi Ventures, HCA, and the Partnership Fund for New York City.

Think getting into the expanding #AI economy requires an advanced degree in data science or fluency in the latest programming languages? The truth is artificial intelligence needs subject matter experts, and lots of them. Many of today's industrial workers already have invaluable subject matter expertise that will be essential to helping AI become more sophisticated, efficient, and useful in the years to come.