IBM's Watson has upended any romantic notions of songwriting by making it a precise formula of inspiration, emotion, musical theory and trends. The AI system's new music algorithm, called "Beat," helped producer Alex da Kid create his first solo EP of four songs, the first of which, Not Easy (feat. X Ambassadors, Elle King and Wiz Khalifa), dropped on Friday. To be clear, IBM's bot didn't write the song, but rather generated "completely new musical scores" that da Kid (who produced singles by Dr. Dre, Rhianna and others) used as inspiration for the tunes. For Da Kid, the algorithms collected music cultural trends and over the last five years, including Nobel Prize speeches, Billboard song lyrics, movie synopses and more.

Alex Da Kid wanted a deeper connection with his audience. But with so many millions out there, he needed a better way to listen to all of them. Watson AlchemyLanguage API helped by analyzing five years of natural language texts including New York Times front pages, Supreme Court rulings, Getty Museum statements, the most edited Wikipedia articles, popular movie synopses and more. Once Watson had learned the most significant cultural themes, Watson Tone Analyzer read news articles, blogs and tweets to find out what people felt about them.

IBM's ground-breaking Watson artificial intelligence software is now, for the very first time, being used to create better semantic targeting for native advertising. A demo is available here: http://preview-25.ayl.io/ As part of the platform, Watson scans all the publisher pages in ADYOULIKE's global network of premium publishers and analyses them in the same way as a human mind would: looking contextually for topics, sentiment and semantics rather than just scanning for simple keywords. This allows the platform to deliver native content in-feed in the most relevant and targeted way possible. Watson looks at where, why and how the existing editorial content on each site is'talking about' subjects and ensures advertisers are dynamically delivering the best native content to fit.

Jonas Nwuke discusses how IBM Watson's cognitive computing will tackle problems that are "uniquely human" At our PSFK 2015 conference, we were thrilled to have Jonas Nwuke of the IBM Watson team as one of our speakers. You're likely to have heard of Watson as the grand champion of Jeopardy!, from pieces about its culinary capabilities, or from PSFK and IBM Waston's Good Data contest, but these are just teases of what's in store for intelligent machines and cognitive systems. With some of the recent forward steps taken by AI and cognitive computing, we wanted to look back on how these technologies are being developed, and where we can still go. "grand challenge" of 2011 served to help prove a hypothesis that humans can create systems capable of navigating ambiguous, confusing, and complex environments like language. As Jonas mentioned, the objective, then, is to build a machine that helps us focus on things that make us uniquely human--"finding inspiration, leveraging creativity, and solving problems by making meaning of the seemingly meaningless."

At the core of IBM's Watson Analytics is a powerful bull's-eye tool; it employs a powerful user query as the basis of analysis. The Data Scientist or business user is free to play with the variables in any dataset and directly visualize how the changing user inputs impact the answers. The bull's-eye also helps narrow down the user query to help get a much clearer answer. This kind of focus can be especially useful in a large dataset, where the user is likely to get lost. IBM's design philosophy behind Watson was to keep the average user sharply focused on the query.

Did you know that the origin of the term "red tape" comes from red ribbons used to tie up oodles of legal documents? I learned it in Brian O'Keefe's masterful explication of the pesky problem of red tape, which appears on the cover of the just-out issue of Fortune. This is one of the articles we now refer to as "longform." It can't be summarized in a tweet, or even an essay topping a daily newsletter about the technology industry. What I can tell you briefly is that the problem is more persistent than you think.

IBM ibm Watson is teaming up with Quest Diagnostics dgx in a significant expansion of its ongoing cancer genomic sequencing and precision medicine push. The company announced Monday it is launching a new service with lab testing giant Quest and New York's Memorial Sloan Kettering Cancer Center called Watson Genomics from Quest Diagnostics. "The service will make Watson for Genomics widely available to physician and patients nationwide for the first time," said IBM in a statement. IBM has already struck prominent collaborative arrangements with major cancer research centers like those at the Broad Institute of MIT and Harvard. The firm is trying to help oncologists identify the proper treatments and clinical trials for their patients' specific cancers.

Cancer treatments are about to get an injection of cognitive computing. On Tuesday, IBM Watson Health and Quest Diagnostics launched IBM Watson for Genomics--a service that combines Watson's AI capabilities with genomic tumor sequencing to determine the best course of treatment for individual patients. This is the first time Watson for Genomics will be widely available to patients and physicians nationwide, according to a press release, as Quest Diagnostics serves half of US physicians and hospitals. Quest estimates that it will provide this service for thousands of oncologists who provide about 70% of cancer care in the nation. Tumor mutations vary for each individual, and treatment success often depends on how well a physician can match a therapy to a mutation.

Zürich, Switzerland / Bad Neustadt, Germany - 18 Oct 2016: Today, RHÖN-KLINIKUM AG (RKA), a private hospital group in Germany, has announced, that by the end of the year, it will begin piloting a Watson-powered cognitive assistance system to help support physicians at the group's Centre for Undiagnosed and Rare Diseases located at the University Hospital Marburg. Since it opened in 2013, the renowned Center has been contacted by more than 6,000 patients to visit Prof. Dr. Jürgen Schäfer, a leading expert in rare diseases, who is also known as the "German Dr. House," based on the character of the eponymous American medical television drama. Most of the patients he and his team meets with have year-long medical histories, which include a large amount of unstructured data, such as laboratory tests, clinical reports, drug prescriptions, radiology findings as well as pathology reports. "It's not uncommon for our patients to have thousands of medical documents, leaving us overwhelmed, not only by the large number of patients, but also by the huge amount of data to be reviewed," said Prof. Dr. Jürgen Schäfer, University Hospital Marburg. "This is especially challenging because our work is often like searching for the proverbial needle in the haystack -- even the smallest piece of information could lead to an accurate diagnosis."

Following on from an earlier story, where I introduced some common patterns used to build chat bots, we're now going to look at building some of those patterns using IBM Watson. If you haven't used the Watson Conversation service before, you may want to read about the basics of building a bot with Watson in "Getting Chatty with IBM Watson". One of the things I talked about was providing guidance at the beginning of the chat. To provide this before the user says anything you can add a "conversation_start" condition to a node. You can add more conditions to "conversation_start" nodes if you want to have different introductions depending on some external factor, e.g. from your app you could pass in the time of day in the context, and then say "good morning", "good afternoon" or "good evening" depending on that value.