Technology powered by artificial intelligence (AI) is enabling organisations to improve their customer experience and boost loyalty and revenues. The role of customer data has never been more crucial. A recent expert roundtable discussed the importance of personalisation and how data drives smart decision making. It outlined why employees need the right skills and should feel empowered to take action on the insights being generated every day. Excellent data management, powered by AI-enabled platforms, can result in improved customer experience, engagement and loyalty.

There was once a time, not so long ago, when scientists like Casey Holliday needed scalpels, scissors and even their own hands to conduct anatomical research. But now, with recent advances in technology, Holliday and his colleagues at the University of Missouri are using artificial intelligence (AI) to see inside an animal or a person -- down to a single muscle fiber -- without ever making a cut. Holliday, an associate professor of pathology and anatomical sciences, said his lab in the MU School of Medicine is one of only a handful of labs in the world currently using this high-tech approach. AI can teach computer programs to identify a muscle fiber in an image, such as a CAT scan. Then, researchers can use that data to develop detailed 3D computer models of muscles to better understand how they work together in the body for motor control, Holliday said.

There was once a time, not so long ago, when scientists like Casey Holliday needed scalpels, scissors and even their own hands to conduct anatomical research. But now, with recent advances in technology, Holliday and his colleagues at the University of Missouri are using artificial intelligence (AI) to see inside an animal or a person -- down to a single muscle fiber -- without ever making a cut. Holliday, an associate professor of pathology and anatomical sciences, said his lab in the MU School of Medicine is one of only a handful of labs in the world currently using this high-tech approach. AI can teach computer programs to identify a muscle fiber in an image, such as a CAT scan. Then, researchers can use that data to develop detailed 3D computer models of muscles to better understand how they work together in the body for motor control, Holliday said.

In November 2020, the usual dark wet of fall settled into Seattle--and with the pandemic raging and outdoor gatherings less appealing, my social life took a nosedive. To fill my evenings, I decided to take on those things I always said I'd do if only I had more time, like practicing my Chinese. While I grew up speaking Mandarin, I'd never mastered reading or writing characters, so I fired up my long-neglected Duolingo account and committed to doing at least a lesson a day. Whether you've already got some language proficiency under your belt or are starting out as a complete beginner, Duolingo doesn't teach languages the way you might have learned them in school, with lists of vocabulary and verb conjugations. Instead, it makes you jump right in and start matching words with their meanings or translating sentences.

Qualitative and quantitative approaches to reasoning about uncertainty can lead to different logical systems for formalizing such reasoning, even when the language for expressing uncertainty is the same. In the case of reasoning about relative likelihood, with statements of the form $\varphi\succsim\psi$ expressing that $\varphi$ is at least as likely as $\psi$, a standard qualitative approach using preordered preferential structures yields a dramatically different logical system than a quantitative approach using probability measures. In fact, the standard preferential approach validates principles of reasoning that are incorrect from a probabilistic point of view. However, in this paper we show that a natural modification of the preferential approach yields exactly the same logical system as a probabilistic approach--not using single probability measures, but rather sets of probability measures. Thus, the same preferential structures used in the study of non-monotonic logics and belief revision may be used in the study of comparative probabilistic reasoning based on imprecise probabilities.

Amazon applied science manager Dr. Nashlie Sephus has lived in New York City, Atlanta, Silicon Valley, and Seoul while pursuing her education and work in machine learning. She knows the look of a community that's thriving from technology and innovation, but she didn't see that growth happening in her hometown of Jackson, Mississippi. That's why last week she concluded an 18-month process by signing contracts to secure 12 acres of land that will be home to the Jackson Tech District. The Bean Path, a nonprofit organization created by Sephus, will operate a maker and innovation space on the land. There will also be restaurants and residential lofts spread across eight buildings, all located near the historically Black Jackson State University.

Qualitative and quantitative approaches to reasoning about uncertainty can lead to different logical systems for formalizing such reasoning, even when the language for expressing uncertainty is the same. In the case of reasoning about relative likelihood, with statements of the form φ 􏰂≥ ψ expressing that φ is at least as likely as ψ, a standard qualitative approach using preordered preferential structures yields a dramatically different logical system than a quantitative approach using probability measures. In fact, the standard preferential approach validates principles of reasoning that are incorrect from a probabilistic point of view. However, in this paper we show that a natural modification of the preferential approach yields exactly the same logical system as a probabilistic approach — not using single probability measures, but rather sets of probability measures. Thus, the same preferential structures used in the study of non-monotonic logics and belief revision may be used in the study of comparative probabilistic reasoning based on imprecise probabilities.

A U.S. Air Force pilot grasps a flight control and weapons firing stick while preparing to launch a MQ-1B Predator unmanned aerial vehicle (UAV), from a ground control station in the Persian Gulf region on January 7, 2016. At 12:01 p.m. on January 20th, drone geek Maynard Holliday won't be a Pentagon robot expert anymore. One of the roughly 4,000 Obama appointees out of a job on Friday, Holliday is wrapping up two and a half years of work designing and buying drones for the Pentagon. A self-proclaimed Star Trek-loving science nerd who grew up obsessed with the thought of one day traveling into space himself, today Holliday's much more interested in a smaller kind of'space race' happening here on Planet Earth. As he shuffles his way out of Washington, he says he's got one big worry about the future of military drone warfare: What happens when and if killer drones go on autopilot?

No one can predict how the future will shake out, but we can make some educated guesses. Global design and strategy firm frog has released its 2017 forecasts for the technologies that will define the upcoming year. Last year, the firm correctly predicted that virtual reality would explode in popularity and that sensors in things like appliances and thermometers would continue to shrink in size. Around the world, large companies are leading the way in building solar-powered offices that don't rely on fossil fuels. Frog strategist Agnes Pyrchla expects the trend to continue in 2017. "Taking a nod from natural patterns," she writes, "material scientists and architects have developed bricks with bacteria, made cement that captures carbon dioxide, and created building cooling systems using nothing but the available wind and our vibrant sun." Business bots are going to be huge.

Terrifying, toothy, and with tears you can never trust, crocodiles are fearsome predators. But added to their ability to lay perfectly still with mouth agape for hours on end is the most powerful bite in the animal kingdom. Now research has shed light on exactly how crocodilians got their bite, and it's thanks to a second jaw joint which helps to spread out the full force when their teeth snap shut. New research has shed light on how crocodiles and alligators get their bite. The reptiles have a second joint in their jaws which helps to spread out the full force of their powerful bites, stabilising the jaw and keeping grip.