This talk was given at a local TEDx event, produced independently of the TED Conferences. Jerome Pesenti, lead developer of IBM's Jeopardy-winning Watson supercomputer, says development of software relying on natural language and machine learning needs new ways of gathering and treating data. Jerome Pesenti, lead developer of IBM's Jeopardy-winning Watson supercomputer, says development of software relying on natural language and machine learning needs new ways of gathering and treating data. About TEDx, x independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group.

What Is the Business Imperative for Cognitive Computing? Cognitive Computing is becoming the foundation for a new generation of solutions that have the potential to transform business. Unlike traditional approaches to building solutions, a cognitive computing approach allows the data to help determine the way applications are designed. This contrasts with conventional software development that begins with defining logic based on the current way a business operates. In her session at 18th Cloud Expo, Judith S. Hurwitz, President and CEO of Hurwitz & Associates, Inc., put cognitive computing into perspective with its value to the business.

Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. Cognitive computing comes from a mashup of cognitive science -- the study of the human brain and how it functions -- and computer science, and the results will have far-reaching impacts on our private lives, healthcare, business, and more. The goal of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition and natural language processing, the computer can mimic the way the human brain works. While computers have been faster at calculations and processing than humans for decades, they haven't been able to accomplish tasks that humans take for granted as simple, like understanding natural language, or recognizing unique objects in an image.

It seems like almost every day a new headline warns us that artificial intelligence (AI) will soon take over the world, or at the very least steal jobs. Even when AI is not in the news, Hollywood offers up a steady stream of entertainment that depicts a very near future in which life as we know it is threatened by super-intelligent machines. These scenarios have something in common: they oversimplify and misrepresent an important and broader set of transformative technologies that hold great promise for business and society. They indulge in fantasy rather than take into account a rational and better-informed dialogue currently underway in the scientific, policy and business communities about what we consider the third age of computing – the cognitive era. What is Cognitive Computing Cognitive computing -- of which AI is but one part – refers to an entirely new class of technologies whose purpose is to deepen human engagement, scale and elevate expertise, enable new products and services, and enhance exploration and discovery.

Suppose that, someday in the future, a data-besieged marketer could use menu commands or plain English text to ask questions about any data. That future, according to a company called Equals 3, is here, and it's called Lucy. Launched recently, she is the first marketing-focus portal built on services provided by IBM's now-legendary cognitive supercomputer, Watson. "There is nothing in the marketplace like Lucy," Equals 3 Managing Partner Scott Litman told me. "She is a user interface for all my marketing systems." The name of his company, which was founded last year, derives from the idea that a person plus a computer can equal something bigger than their sum.

In a talk in Pittsburgh in 1997, the late evolutionary biologist Stephen J. Gould allegedly characterized humans as "the primates who tell stories." Psychologist Robyn Dawes went much further, suggesting humans are "the primates whose cognitive capacity shuts down in the absence of a story." To be sure, we love a good story. Research suggests that anecdotes can be as persuasive as hard data, and that jurors are influenced by the quality of the prosecution's and defense's "stories" when deciding whether to find a defendant guilty. Even in science, we seek explanations, not mere descriptions; in history, we want a good narrative, not a mere sequence of events. Or do they offer something more?

In a talk in Pittsburgh in 1997, the late evolutionary biologist Stephen J. Gould allegedly characterized humans as "the primates who tell stories." Psychologist Robyn Dawes went much further, suggesting humans are "the primates whose cognitive capacity shuts down in the absence of a story." To be sure, we love a good story. Research suggests that anecdotes can be as persuasive as hard data, and that jurors are influenced by the quality of the prosecution's and defense's "stories" when deciding whether to find a defendant guilty. Even in science, we seek explanations, not mere descriptions; in history, we want a good narrative, not a mere sequence of events. Or do they offer something more?

Cognitive Artificial Intelligence (AI) is any computer program that can think, learn, and generally mimic human cognition. Cognitive computing is the process of understanding and building cognitive computer systems, including AI. Cognitive computing uses machine learning (algorithms that allow machines to learn from past experience by detecting patterns), rather than explicitly programmed algorithms (algorithms with a pre-defined and pre-programed sets of rules). The ultimate goal of cognitive computing is to build fully cognitive Artificial Intelligence. The concept of "smart machines" goes back to early science fiction, Alan Turing's theoretical machines and work on early AI, and the emergence of the term cognitive computing in the 50's. Despite the rich history, current cognitive AI is best understood by simply look at recent projects like Google's DeepMind and IBM's Watson two, similar but different, ways modern tech is going about creating machines that can think like humans.

The year ahead offers some promising trends--and optimizing the full potential of digital health will be the key to real progress. One of the reasons that meaningful use has not been as successful as hoped is lack of an infrastructure to support patient engagement and data exchange capabilities. Building that infrastructure is key to making sustainable changes to care delivery and to achieving the full transition to value-based care. That is why technology will be the KEY driver of healthcare trends in 2016. In 2015, we saw great strides made by providers, payers, and pharmaceutical companies to change the way we deliver, access, and think about healthcare.

IBM is busy reshaping itself for the future, moving out of commodity hardware and chip manufacturing and further into higher end, value-added services. At the heart of the new strategy is the artificial intelligence machine Watson, around which IBM now intends to build a business in cognitive computing. The vision of the 105-year-old company is that Watson works in tandem with users, doing the heavy-lifting analytics, but ceding the judgement to humans. In particular, it wants to help make sense of the data now pouring out of the Internet of Things (IoT). As part of the remodelling, the IBM is making some large investments in Europe, in areas including cloud computing, artificial intelligence and data analytics.