This particular solution finds the most exciting parts of a match by analyzing the crowd's cheers, as well as the players' gestures and facial expressions. It then automatically generates videos of the most thrilling moments, which are then posted on Facebook and published on the US Open apps. Noah Syken, IBM VP of Sports & Entertainment Partnerships, explained that USTA turned to Watson for help, because there could be as many as 18 matches going on at the same time. Even the fastest video team will have a hard time analyzing matches and stitching the best moments together as they happen. It probably also helped that IBM tested Cognitive Highlights as a proof of concept at the Master's Tournament earlier this year, and Wimbledon also used the technology to generate some videos. In addition to Cognitive Highlights, the US Open is also using Watson's Conversation API to power its Cognitive Concierge app.

I spoke with Tanmay Bakshi all about machine learning, how he got into developing software so early on, what he thinks about the Singularity and more in this interview outside the IBM Watson Summit in Sydney! Tanmay Bakshi is an IBM Champion, IBM Honorary Cloud Advisor, Algorithmist, machine learning and Watson developer, author, speaker and YouTuber! Thank you for your time Tanmay! A very big thank you to the team at the IBM Watson Summit in Sydney for helping organise this interview and supporting Dev Diner in its goal of helping developers get into emerging tech! Thank you to Heartbeat Intensity for putting together the fantastic music for this and for her work behind the camera!

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We are excited to join forces with Slack to host a series of workshops across Europe. These are one-day workshops, focused on making software that improves the workplace. The workshops will begin with API overviews from both companies during which Watson developer advocate Yacine, will dish out some handy tips on getting started quickly with many of the Watson services. We'll then stay to help you brainstorm, scope, and eventually build out your projects. Everyone can expect to walk away with a working prototype built on top of Slack and Watson Developer Cloud APIs.

PC Mag recently interviewed Rob High, IBM Watson's Vice President and Chief Technology Officer. Thanks to High's experience with Watson, IBM's artificial intelligence (AI) supercomputer, he is one of the preeminent thinkers in the AI space. In his interview, High spoke about how technology, and AI in particular, is transforming jobs, culture, and life for humanity. For High, one of the biggest misconceptions the public holds about AI is the sort of dystopian worldview we see in Hollywood and, in some cases, from other thinkers in the field. He points out that AI is not replacing the human mind, but augmenting human intelligence and amplifying its reach: "[I]f you look at almost every other tool that has ever been created, our tools tend to be most valuable when they're amplifying us, when they're extending our reach, when they're increasing our strength, when they're allowing us to do things that we can't do by ourselves as human beings."

Doctors are often deluged by signals from charts, test results, and other metrics to keep track of. It can be difficult to integrate and monitor all of these data for multiple patients while making real-time treatment decisions, especially when data is documented inconsistently across hospitals. In a new pair of papers, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) explore ways for computers to help doctors make better medical decisions. One team created a machine-learning approach called "ICU Intervene" that takes large amounts of intensive-care-unit (ICU) data, from vitals and labs to notes and demographics, to determine what kinds of treatments are needed for different symptoms. The system uses "deep learning" to make real-time predictions, learning from past ICU cases to make suggestions for critical care, while also explaining the reasoning behind these decisions.

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). Most ensemble methods use a single base learning algorithm to produce homogeneous base learners, i.e. learners of the same type, leading to homogeneous ensembles. There are also some methods that use heterogeneous learners, i.e. learners of different types, leading to heterogeneous ensembles. In order for ensemble methods to be more accurate than any of its individual members, the base learners have to be as accurate as possible and as diverse as possible. Bagging stands for bootstrap aggregation.

Since their first appearance decades ago, chatbots have come a long way thanks to leaps in natural language processing and generation (NLP/NLG), the branches of artificial intelligence that enable us to interact with computers in a conversational manner. Today AI-powered chatbots have established a prominent role in various fields, including customer service, healthcare, banking and more. Meanwhile, the technologies that power chatbot assistants are growing smarter and more efficient. I had a chance to talk with Rob High, Chief Technology Officer at IBM Watson, on the evolution of chatbots and where the trend is leading to. He shared some very interesting insights on the prospects and challenges that lie ahead.

To gauge the future of IBM (NYSE: IBM) one needs to understand its cognitive computing platform, Watson.