Geologic Carbon Storage (GCS) is an important technology that aims to reduce the amount of carbon dioxide in the atmosphere. Multiphase flow in porous media is essential to understand CO2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (< 15%) and GPU memory (< 35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time.

On December 11th 2017, President Trump signed Space Policy Directive 1, refocusing NASA's efforts in human space exploration. In March 2019, Vice President Mike Pence made the surprising announcement that America would be returning astronauts to the Moon in just 5 years time. This Lunar mission, known as Artemis which stands for "Acceleration, Reconnection, Turbulence and Electrodynamics of Moon's Interaction with the Sun", is hoping to leave more than just a flag and footprints this time. NASA aims to establish a base on the Moon and in orbit by 2028, providing a foundation for where scientists will eventually power a mission to Mars and beyond. The Artemis mission has left many questioning whether it is possible to send human's back to the Moon in just five years time, with a report claiming that NASA would need an annual increase of $4-$5 billion on top of its $22.6 billion 2020 budget.

At its crudest, most reductive, we could sum up the future of artificial intelligence as being about robot butlers v killer robots. We have to get there eventually, so we might as well start with the killer robots. If we were to jump forward 50 years to see what artificial intelligence might bring us, would we – Terminator-style – step into a world of human skulls being crushed under the feet of our metal and microchip overlords? No, we're told by experts. It might be much worse.

Brian Wong - Kiip, CEO & Co-founder: So in 50 years I think the world will, in terms of technology, really change and really this concept I call proximity of tech - which is how close the technology is to your body. And right now where it's kind of silly, we hold things and we wear things and it's kind of at our hands, right? And we might put it on over our faces but the whole point is just getting it into our eyeballs, into our ears, into our stomachs. Ingestibles are already a thing, it's crazy, you literally ingest these pills these robot pills that can obviously see what you're eating and all these things and all your caloric intake, it's amazing. And then you know you've heard of startups like Magic Leap where they're doing AR for your world; I should just be able to load my emails then like do this, then do this, that's literally what will happen I think in 50 years, everything will just come inside and that to me is really, really exciting.

How could the NHS look in another 30 years time? Will new technology and medical advances transform how we're looked after? We asked nine different corners of the health service - and the health secretary - what innovations we might see. Innovations that were unthinkable only a few decades or years ago are now common practice. Advances in medicine and technology, such as robot-assisted surgery and artificial intelligence, will have a significant impact on the delivery of surgical care in the future.

In just ten years time your dog could talk to you instead of barking, according to leading experts. Advances in artificial intelligence and machine learning mean the Dr Dolittle dream of communicating with animals could soon be a reality. One researcher is currently collecting thousands of videos of dogs barking, growling and moving around, and is using them to teach an algorithm to understand canine communication. Professor Con Slobodchikoff from Northern Arizona University is developing new technology that interprets the calls of the prairie dog and says it could eventually be used to interpret other animals. North American rodents prairie dogs have a sophisticated ways of calling group members and alerting them to danger.

We have been in the market of analytics for roughly 10 years now. The product we have to today is no older than 2,5 years. Along the journey we were blessed to work with many of the brightest and most experienced people in the field of actually applying analytics in the domain of marketing. These professionals were able to lever the value in the data of their companies way before many of their competitors could even grasp that there was something happening. The difference was that they could build their analytics stack with the software that was already available.

The power of Algorithms to calculate, contemplate and anticipate the needs of patients is improving rapidly and still has no sign of slowing down. Everything from patient diagnosis to therapy selection will soon be moving at exponential rates. Does that mean the end of doctors? To better understand technology's ever-growing role in healthcare, we first have to better examine the potential of tools and timelines that we are working with. A recent study done at Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School, showed that AI isn't about Humans versus Machines. They trained a Deep Learning Algorithm for identifying Metastatic Breast Cancer, interpreting pathology images.

Egencia has been taking a look at how technology such as artificial intelligence will effect the corporate travel world in the near, medium and long-term. Alongside Phocuswright, the Expedia-owned travel management company has put together a white paper entitled the "Emerging Tech-Driven Corporate Travel Revolution." The study provides a current state of the nation look as regards technologies such as AI, machine learning, Natural Language Processing and chatbots in business travel as well as looking further out. In three years time, for example, the research predicts AI will be embedded in corporate travel in terms of how data is used to make for smarter, more efficient travel and improved compliance. Natural language processing and chatbots are not expected to be fully embedded at this early stage.

Over 1,000 attendees, 50 speakers and 30 exhibitors; this is a brief summary of what I was lucky enough to take part in during the first AI Europe 2016 conference held in London on the 5 and 6 December. The attendee list boasted the biggest names from the world of artificial intelligence such as Microsoft, Dell, Uber, Samsung and Nvidia, as well as several innovative start-ups, the likes of Blippar and DreamQuark whose innovations are based on machine or deep learning models. Even if we can say with a degree of certainty that further advances in artificial intelligence are yet to come, leading players are in agreement that most AI techniques and technologies are now well-advanced. Therefore, their major preoccupation today is more about the quality of the data sets being used to train and validate their machine and deep learning models. Whether it's Dell or Uber, Microsoft or Blippar, they all have one thing in common: they all agree on the fact that as of now, the quality of the data used in AI for machine learning is of the utmost importance.