Welcome to the second installment of my mini-series, "Digital Health Around the World." As illustrated in the recent StartUp Health funding report, global digital health innovation and investment activity is accelerating, with many significant deals involving non-U.S.-based companies. The innovation trade winds are clearly blowing toward Asia, Europe and other parts of the world where difficult health infrastructure, outcomes, and demographic challenges are looming. Our second stop on this digital health world tour is Singapore. Singapore has a range of infrastructure and demographic challenges and opportunities that are attracting innovation activity (and investment).

If you like being on the bleeding edge and aren't afraid of a few bugs, the Google Home Preview Program is now available to anyone who wants to sign up, as 9 to 5 Google noticed recently. If you'd rather not be an AI assistant guinea pig, fear not. When Google first announced the program, it assured users that "this is not beta software." Rather, it is production quality, and users are "simply getting earlier access to new features before they're released broadly." If that's so, it seems like every Home owner would sign up, so what's the catch?

Andrew Ng is one of the foremost thinkers on the topic of artificial intelligence. He founded and led the "Google Brain" project which developed massive-scale deep learning algorithms. In 2011, he led the development of Stanford University's main Massive Open Online Course (MOOC) platform. His course on Machine Learning would eventually reach an "enrollment" of over 100,000 students. That experience led Ng to co-found Coursera, a MOOC that partners with some of the top universities in the world to offer high quality online courses. Today, Coursera is the largest MOOC platform in the world.

Dan Hunter is a prison guard's worst nightmare. But he's not a hardened crim. As dean of Swinburne University's Law School, he's working to have most wardens replaced by a system of advanced artificial intelligence connected to a network of high-tech sensors. Called the Technological Incarceration Project, the idea is to make not so much an internet of things as an internet of incarceration. Professor Hunter's team is researching an advanced form of home detention, using artificial intelligence, machine-learning algorithms and lightweight electronic sensors to monitor convicted offenders on a 24-hour basis.

Messenging app'Messenger' launched a range of new artificial intelligence (AI) features in Australia on Wednesday. The AI, called'M', works almost like a prompting service, where it recognises words and phrases used in a conversation and then suggests relevant content and actions based on the chat between the two users. For example, if you're speaking to someone on their birthday, 'M' will recognise either through a phrase used or their Messenger profile when their birthday is and then prompt you to send a birthday message. Similarly, if you are chatting about making plans or struggling to come to a group decision about something, the AI will suggest you make a plan or start a group poll respectively. If you are chatting in a one-on-one conversation, and one person rises the idea of making a call, 'M' will prompt you to start a video or voice chat.

Scientists have solved the puzzle of the so-called "Frankenstein dinosaur", which seems to consist of body parts from unrelated species. A new study suggests that it is in fact the missing link between plant-eating dinosaurs, such as Stegosaurus, and carnivorous dinosaurs, like T. rex. The finding provides fresh insight on the evolution of the group of dinos known as the ornithischians. The study is published in the Royal Society journal Biology Letters. Matthew Baron, a PhD student at Cambridge University, told BBC News that his assessment indicated that the Frankenstein dinosaur was one of the very first ornithischians, a group that included familiar beasts such as the horned Triceratops, and Stegosaurus which sported an array of bony plates along its back.

Probabilistic inversion within a multiple-point statistics framework is still computationally prohibitive for large-scale problems. To partly address this, we introduce and evaluate a new training-image based simulation and inversion approach for complex geologic media. Our approach relies on a deep neural network of the spatial generative adversarial network (SGAN) type. After training using a training image (TI), our proposed SGAN can quickly generate 2D and 3D unconditional realizations. A key feature of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic (or deterministic) inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. A series of 2D and 3D categorical TIs is first used to analyze the performance of our SGAN for unconditional simulation. The speed at which realizations are generated makes it especially useful for simulating over large grids and/or from a complex multi-categorical TI. Subsequently, synthetic inversion case studies involving 2D steady-state flow and 3D transient hydraulic tomography are used to illustrate the effectiveness of our proposed SGAN-based probabilistic inversion. For the 2D case, the inversion rapidly explores the posterior model distribution. For the 3D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well. Future work will focus on the inclusion of direct conditioning data and application to continuous TIs.

During reactive transport modeling, the computational cost associated with chemical reaction calculations is often 10-100 times higher than that of transport calculations. Most of these costs results from chemical equilibrium calculations that are performed at least once in every mesh cell and at every time step of the simulation. Calculating chemical equilibrium is an iterative process, where each iteration is in general so computationally expensive that even if every calculation converged in a single iteration, the resulting speedup would not be significant. Thus, rather than proposing a fast-converging numerical method for solving chemical equilibrium equations, we present a machine learning method that enables new equilibrium states to be quickly and accurately estimated, whenever a previous equilibrium calculation with similar input conditions has been performed. We demonstrate the use of this smart chemical equilibrium method in a reactive transport modeling example and show that, even at early simulation times, the majority of all equilibrium calculations are quickly predicted and, after some time steps, the machine-learning-accelerated chemical solver has been fully trained to rapidly perform all subsequent equilibrium calculations, resulting in speedups of almost two orders of magnitude. We remark that our new on-demand machine learning method can be applied to any case in which a massive number of sequential/parallel evaluations of a computationally expensive function $f$ needs to be done, $y=f(x)$. We remark, that, in contrast to traditional machine learning algorithms, our on-demand training approach does not require a statistics-based training phase before the actual simulation of interest commences. The introduced on-demand training scheme requires, however, the first-order derivatives $\partial f/\partial x$ for later smart predictions.

Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines. These processes are often characterized by nonlinear time dynamics that include interactions across multiple scales of spatial and temporal variability. The data sets associated with many of these processes are increasing in size due to advances in automated data measurement, management, and numerical simulator output. Non- linear spatio-temporal models have only recently seen interest in statistics, but there are many classes of such models in the engineering and geophysical sciences. Tradi- tionally, these models are more heuristic than those that have been presented in the statistics literature, but are often intuitive and quite efficient computationally. We show here that with fairly simple, but important, enhancements, the echo state net- work (ESN) machine learning approach can be used to generate long-lead forecasts of nonlinear spatio-temporal processes, with reasonable uncertainty quantification, and at only a fraction of the computational expense of a traditional parametric nonlinear spatio-temporal models.

There's a popular perception out there that most people, particularly office workers, are concerned that artificial intelligence (AI) is coming for their jobs. After all, more and more simple tasks in the workplace have become automated -- from daily reminders to finding and editing electronic documents. But are the office workers themselves truly worried that robots will take away jobs? In our new report The Future of Work: More than a Machine, we surveyed more 4,000 office workers across the U.S., U.K., and Germany and asked them about how technology is changing their jobs, especially advanced technology like AI, and how confident they feel about keeping their jobs in the future. The study suggests that instead of being anxious about technology taking over their jobs, office workers are optimistic about how it can boost productivity and work for them.