If you've ever wanted to bottle up and save that holiday feeling - a new AI-produced drink could be just the ticket. "Holiday Spirit" is claimed to be the world's very first data-distilled rum and was created using IBM Watson. The supercomputer analysed data from social media posts in order to produce a bespoke rum "that tastes like a holiday". "In just six hours Watson was able to read 15 million posts on Facebook, Instagram and Twitter relating to holidays - and find the predominant emotions and concepts in those posts," explained Joe Harrod, big data analyst and AI expert, who works closely with Watson. "The machine then showed that feelings like joy and excitement should be part of a perfect holiday moment. "Then Watson read 5,000 rum reviews from review sites around the web, matching emotions from the reviews with ingredients.

If, for example, you're diagnosed with cancer, you might benefit from the platform, Watson for Oncology. "Normally it's up to a specialist doctor to meet with cancer patients, and to spend time reviewing their notes – which would arrive on paper format or in a string of emails," says Balkizas. "A doctor's decision will be limited to their individual experience and the information available in front of them." However, now the Memorial Sloan Cancer Treatment Centre in New York is training IBM Watson to be able to quickly provide evidence based recommendations to time poor clinicians. "It takes all those unstructured notes and restructures it in a way that the doctor can check easily, with treatment recommendations of which drug to give, which radiation or dosage," says Balkizas.

CES 2017 is all wrapped up but there's still plenty of buzz around the latest and greatest gadgets and gear that debuted at the annual tech mecca in Las Vegas. There were thousands of new products to digest, between wallpaper TVs, next-gen wearables and drones, and "smart" versions of pretty much every appliance and tool we use in our everyday lives. As expected, AI took center stage at this year's event. Most products were AI-powered, "smart" or "intelligent." It's already part of the lives of millions of people, and most customers at CES expected to see sufficiently mature and useful applications of AI.

It's been an interesting day. The 45th President of the United States of America took office just two hours ago, and he is clearly unlike any other President that has gone before him. So just for fun, I thought I might feed his inauguration speech into Watson in real-time, in order to see what the smartest computer in the world had to say about it. Would he notice any anomalies, or insights that the professional political commentators might have missed? Might we some people respect Trump a little more if they looked at his speech more analytically than emotionally?

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The IBM Watson AI XPRIZE, a Cognitive Computing Competition, was announced on the TED Stage on Feb 17, 2016. It is a $5 million competition challenging teams from around the world to develop and demonstrate how humans can collaborate with powerful cognitive technologies to tackle some of the world's grand challenges. Every year leading up to TED2020, teams will go head-to-head at World of Watson, IBM's annual conference, competing for interim prizes and the opportunity to advance to the next year's competition. The three finalist teams will take the TED stage in 2020 to deliver jaw-dropping, awe-inspiring TED Talks demonstrating what they have achieved. Ideas will be evaluated by a panel of expert judges for technical validity and ultimately, the TED and XPRIZE communities will choose the winner based on the audacity of their mission and the awe-inspiring nature of the teams' TED Talks in 2020.

TLDR: IBM's Watson is about human augmentation, not termination

For the learning phase, Levinson used a combination of rote learning as in case-based reasoning, restructuring to derive significant generalizations, a similarity measure based on the generalizations, and a method of back propagation to estimate the value of any case that occurred in a game. For playing chess, the cases were board positions represented as graphs. Every position that occurred in a game was stored in a generalization hierarchy, such as those used in definitional networks. At the end of each game, the system used back propagation to adjust the estimated values of each position that led to the win, loss, or draw. When playing a game, the system would examine all legal moves from a given position, search for similar positions in the hierarchy, and choose the move that led to a position whose closest match had the best predicted value.

This webinar will be the final opportunity before the close of registration for potential competitors to ask any questions they have regarding prize registration. Prior to joining, please be sure to read the Competition Guidelines, available on ai.xprize.org

The department's research in Machine Learning contributes to the state-of-the-art of individual methods and algorithms as well as combinations of methods targeting particular tasks, for example, combining data-intensive methods with knowledge-based methods to produce user explanations for decision support. Our strongest contributions to the international research front until now have been within Bayesian learning and probabilistic reasoning, evolutionary learning and neural networks, and instance-based learning and case-based reasoning. In addition, we have ongoing activities at a high international level within large-scale data and information management. Over the last years there has been an increased interest in combined methods, e.g.