Figure Skaters at the 2026 Winter Olympics Are Pushing the Limits of What's Possible For years, quad axel jumps seemed impossible. Then Ilia Malinin landed one in 2022. As he heads to the Milano Cortina Games everyone wants to know what's next. In 2021, famed Russian figure skating coach Alexei Mishin said that no figure skater would ever be able to successfully perform a quad axel in his lifetime. The following year, two-time Olympic gold medalist Yuzuru Hanyu was training to master the jump, but when he attempted it at the 2022 Winter Games in Beijing, he fell short of finishing the four-and-a-half revolutions in the air. Mishin's pronouncement, it seemed, had been validated.

However, I really do have to establish my credentials, biases, and agenda for readers -- and sometimes people do ask. Okay, my full name is "Gregory Vaughn Goebel", and I live in Loveland, Colorado. I was born in Spokane, Washington, in 1953, and raised there. I did a hitch in the US Army from 1972 to 1975; went back to Spokane to get an electronics tech degree from Spokane Community College; then got an electronics engineering degree from Oregon State University. From 1981 into 2000, I worked for Hewlett-Packard in Oregon and Colorado, mostly as a factory contact.

This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.

The story of Alberta AI starts on April 1, 1964, with the opening of the University of Alberta's department of computing science. Researchers such as Randy Goebel would graduate from the department and set the foundation for early work on the science of natural language processing and AI in the 80s, the latter of which took the form of studying games like chess. By the '90s, however, several'AI winters' -- periods of funding constraints for research and lack of press interest to generate public excitement -- inhibited the discoveries coming out of the university. "The symptoms of the winter are more that industries [that] bought into the idea that expert systems could help them found that it was way too expensive to implement," said Goebel, now a U of A professor and principal investigator at the Alberta Machine Intelligence Institute (Amii). "If you scale up, it costs a lot to have you and me write down processes for chemistry, for example."

Companies can be easily dazzled by their newest workers, the chatbots. They never complain or get sick. And they don't ask for raises. But companies can be just as easily disappointed when customers become frustrated by opaque and time-consuming interactions with the bots. It's not the bots' fault, said a panel of artificial intelligence experts at a recent Executive Roundtable hosted by Earley Information Science (EIS), a leading consulting firm focused on organizing information for business impact.

Lidl has introduced an artificial intelligence (AI) chatbot to help improve the wine-buying experience for shoppers, as the food retailer continues its efforts to wrest market share away from the big four supermarket chains. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.

As described in our recent announcement about AI pioneer Randy Goebel joining the ROSS team as an advisor, Goebel is a professor in the Department of Computing Science at the University of Alberta, a founder and researcher with the Alberta Machine Intelligence Institute (AMII) and is involved with the development of the University of Alberta Google DeepMind relationship, the group behind AlphaGo. Goebel's theoretical work on abduction, hypothetical reasoning and belief revision is internationally acclaimed and his recent application of practical belief revision and constraint programming to scheduling, layout, and web mining has had widespread impact across multiple industry verticals. More recently, Goebel has been working on the application of machine learning to visual explanation and natural language processing, with focus on legal reasoning. He has previously held faculty appointments at the University of Waterloo and the University of Tokyo, and is actively involved in academic and industrial collaborative research projects in Canada, Australia, Malaysia, Europe and Japan. Goebel is on the advisory boards of the German Research Centre for AI, the Japan Science and Technology Organization and the Japanese National Institute for Informatics.

If Edmonton researcher Randy Goebel has his way, artificially intelligent judges and attorneys will become players in the courtrooms of the future. A professor in computing science at the University of Alberta, Goebel has partnered with scientists in Japan to develop artificial intelligence programs designed for the legal world. His team has already designed an algorithm capable of passing the Japanese bar exam. Now the computer scientists are taking their research one step further. The latest project is new artificial intelligence software that could weigh contradicting legal evidence, rule on cases and predict the outcomes of future trials.

Classic IVR automation already reduces customer service call costs by more than 90 percent--imagine what sophisticated A.I. can do. Join bot technology experts and A.I. scientists as they explore this and many other business implications of a world where bots join the conversation. "We've got to do something about how we communicate with customers," says Tobias Goebel, director of emerging technology at Aspect Softare. "We don't want to be stuck in the 20th century." He points to the primary market research that shows that the era of the phone call is coming to an end in a time when an entire generation rarely, if ever, makes phone calls, and even baby boomers would prefer to text customer service questions than pick up the phone to talk to a rep.