Are you ready to say goodbye to 2019? Tucked within that long list is the excitement of what 2020 will bring to the mobile world. Although 2019 wasn't exactly a banner year, it certainly set the stage for a lot of new technology trends to come. And thus, I pull out my 10 Ball of Prognostication and gaze deep into its shadowy realm to see what the upcoming 366 days--2020 is a leap year--have in store. If the Google Pixel 4 proved one thing, it's that Artificial Intelligence (AI) is not only here to stay, it's going to continue to lead the mobility charge.

Gay: As an astronomer, I have to admit, my day-to-day life is sitting at home writing software to help us better understand our universe. Then, as a communicator of science, it just makes me so excited to come out here and tell you about the kind of stuff I get to do. As an astronomer, I use data; images, spectra, photos but taken with cameras that are sometimes orbiting our world and other planets, moons, asteroids. For a lot of my career, everything I wanted to study, everything I wanted to learn, I could do with software, a database, and sometimes some really clumsy-linked lists because that was C in the 90s. Along the way though, I got curious about all these other areas of science that are different from mine. It was from the planetary-science community where I've somehow migrated over the years that I learned there are people - such as the folks who are today mapping out planet classic Pluto - that the way they do their analysis of the geological features on this world are to sit around round tables with a screen and a Wacom tablet. They draw by hand what they perceive to be the boundaries between different kinds of glaciers, different kinds of mountains, different features on this distant world. This is science by hand because humans and software don't know what to make of Pluto but the humans can at least guess. There's a lot of science that works this way. One of the most disturbing things I learned is there is a brilliant scientist Stuart Robbins who, as his PhD work at the University of Colorado, drew three million circles - again, with a Wacom tablet; go Wacom - three million circles on thousands and thousands of images of the surface of Mars. This ended up leading to a catalog of 600,000 craters. The reason he had to draw so many circles is he had to periodically remap regions to make sure that his bias hadn't changed over time. He had to map things at small scales, at big scales, at in-between scales, bridge across all of these, have overlapped between his image. Three million circles got him a PhD.

The Big Data paradigm has gained momentum last decade, because of its promise to deliver valuable insights to many real-world applications. With the advent of this emerging paradigm comes not only an increase in the volume of available data, but also the notion of its arrival velocity, that is, these real-world applications generate data in real-time at rates faster than those that can be handled by traditional systems. This situation leads us to assume that we have to deal with a potentially infinite and ever-growing datasets that may arrive continuously (stream learning) in batches of instances or instance by instance, in contrast to traditional systems where there is free access to all historical data. These traditional processing systems assume that data are at rest and simultaneously accessed. The models based on this traditional processing do not continuously integrate new information into already constructed models but, instead, regularly reconstruct new models from the scratch.

New intelligent applications and smart devices built using GE Healthcare's Edison intelligence platform help improve radiology efficiency and enhance patient care. It's no secret that Artificial Intelligence (AI) is poised to make a powerful impact on patient care and healthcare operations. At the 2019 annual meeting of the Radiological Society of North America (RSNA) in Chicago, AI is again the hottest topic at the show. An entire floor is dedicated to the ever-expanding AI vendors and sessions feature a full slate of speakers showcasing emerging AI technologies and trends. This year, GE Healthcare introduced several new intelligent applications and smart devices built using Edison, our secure intelligence platform unveiled at RSNA 2018.

AI Machine Learning is being debated due to the "update problem" of adaptiveness. Humans typically learn new things on-the-fly. Let's use jigsaw puzzles to explore the learning process. Imagine that you are asked to solve a jigsaw puzzle and you've not previously had the time nor inclination to solve jigsaw puzzles (yes, there are some people that swear they will never do a jigsaw puzzle, as though it is beneath them or otherwise a useless use of their mind). Upon dumping out onto the table all the pieces from the box, you likely turn all the pieces right side up and do a quick visual scan of the pieces and the picture shown on the box of what you are trying to solve for.

It would be easy to wonder what Zachi Attia is doing in the cardiac operating rooms of one of America's most prestigious hospitals. He has no formal medical training or surgical expertise. The first time he watched a live procedure, he worried he might faint. But at Mayo Clinic, the 33-year-old machine learning engineer has become a central figure in one of the nation's most ambitious efforts to revamp heart disease treatment using artificial intelligence. Working side by side with physicians, he has built algorithms that in studies have shown a remarkable ability to unmask heart abnormalities long before patients begin experiencing symptoms.

"Q: How worried do you think we humans should be that machines will take our jobs? A: It depends what role machine intelligence will play. Machine intelligence in some cases will be useful for solving problems, such as translation. But in other cases, such as in finance or medicine, it will replace people." This Q&A is taken from Tom Standage's description of how he interviewed AI (language model GPT-2) for The Economist The World in 2020. As readers of this column's annual roundup of AI predictions know, this year's first installment of 120 AI predictions for 2020 featured my interview of Amazon AI in which Alexa performed slightly better than the previous year. For the new list of 99 additional predictions, I repeated Standage's question to Alexa, and got the response "Hmm, I'm not sure." The following AI movers and shakers are a lot more confident in what the near future of machine intelligence will look like, from robotic process automation (RPA) to human intelligence augmentation (HIA) to natural language processing (NLP).

AI and Machines Learning Innovations from H2O.ai Drive Personalized and Better Experiences for Jewelers and Consumers H2O.ai, the open source leader in artificial intelligence (AI) and machine learning (ML), announced Jewelers Mutual, one of the United States' and Canada's most established and trusted providers of affordable and comprehensive insurance for jewelers and consumers, has chosen its award winning AI platforms to provide AI and machine learning capabilities to better serve its customers. As a leader in driving customer-focused innovation and providing the latest technology to a long-standing industry, Jewelers Mutual is using H2O-3 open source and H2O Driverless AI to deliver exceptional customer experiences, prevent losses, and provide better protection and policies for both jewelers and customers. "We have been in the jewelry insurance business for over 100 years, and our leadership team has been looking to raise the bar for technology-driven innovation in the industry. After two years of experimentation with AI and machine learning, we came to place a high value on model transparency and explainability. Our business end-users demanded it. The initial AI platform we used was lacking in this area so we began searching for a new platform," said Andrew Langsner, Senior Manager, Embedded Analytics at Jewelers Mutual.

This research was sponsored by the DoD Joint Artificial Intelligence Center (JAIC) and was conducted within the Acquisition and Technology Policy Center of the RAND National Defense Research Institute, a federally funded research and development center sponsored by the Office of the Secretary of Defense, the Joint Staff, the Unified Combatant Commands, the Navy, the Marine Corps, the defense agencies, and the defense Intelligence Community. This report is part of the RAND Corporation research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity. Permission is given to duplicate this electronic document for personal use only, as long as it is unaltered and complete.

The cybersecurity provider landscape is cluttered with impossible claims, misrepresentations, and a confusing mix of inconsistent terminology. Worse, every minute you delay making a decision is another minute hackers have to gain access and knowledge about your network. With so much on the line, choosing what kind of platform and which company to trust with your company's data privacy can become a stressful decision. Leaning toward an AI-enabled platform is a step in the right direction, but which platforms actually do what they say they do? Luckily, you don't have to become an expert in AI cybersecurity to learn how to evaluate the efficacy of AI-enabled cybersecurity platforms.