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.

Doctor House is going electronic. Medics charged with diagnosing the kind of extremely rare diseases that Hugh Laurie's consultant routinely spots in TV drama House have found that artificial intelligence can do a similar job – but in seconds rather than days or weeks. From December, doctors at the University Hospital of Marburg's Centre for Undiagnosed and Rare Diseases (known as ZusE in German) will start using IBM's Watson to speed up their diagnoses. In 2011, Watson famously won the gameshow Jeopardy! Doctors are now training it on peer-reviewed rare disease literature to help them spot unusual illnesses.

You read that right – it's not Sherlock saying it this time! IBM Watson's cognitive computing capabilities and superhuman intelligence stand it in good stead to switch roles with the legendary fictitious detective Sherlock Holmes who could crack the most mind-boggling of mysteries, find answers to the unanswered questions everyone had and draw insights from information in a way no one could even fathom! For those of you who aren't yet familiar with IBM Watson, it is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. It has refined cognitive abilities to quickly understand context, learn from experience and draw inferences and insights from a sea of information that would otherwise be humanly impossible to wade through. This is way beyond mere data-matching or search engine functions – Watson uses advanced ways of inferring context, meaning, implications and fallouts of the voluminous content it indexes in its operations.

Open-source auto company Local Motors is constantly redefining itself. The company began with the Rally Fighter, whose designs were crowdsourced from the Local Motors community. The firm then jumped into the 3D printing industry, first 3D printing the Strati concept vehicle and then embarking on a complete line of 3D-printed road-ready cars, set to hit highways in 2017. At the grand opening of the company's new facility in National Harbor, MD, Local Motors redefined itself once again by unveiling "Olli", the first self-driving vehicle powered by IBM Watson. Watson will allow the auto to take advantage of a number of advanced cognitive computing capabilities, including the ability to answer questions from the vehicle's passengers.