AI and Cognitive Solutions

It’s hard to get people to learn about – and be interested in – any new product. It’s perhaps even harder for B2B products, which tend to be less intuitive (and let’s just say it: less fun to think about).
IBM’s Watson hasn’t had this problem. Though it has no user interface and isn’t a consumer product, the bundle of Artificial Intelligence and Machine Learning services known as Watson is arguably a household name (thanks in part to a successful run on Jeopardy).


Beth Smith, General Manager of Technology at IBM Watson, joined Big Boulder Chairman Chris Moody on stage this morning to discuss the state of AI.

What do the uninitiated need to know about AI in 2017? Here are four places to start:

  1. AI helps read through data humans don’t have time to review
    AI technologies like Watson are (rightfully) associated with the act of thinking – commonly referred to as “cognition” within the industry. But one of the first places AI tools can help is just by reading at scale: humans don’t have the time or capacity to review millions of pieces of information when starting a project – Watson does.

    Researchers at the University of North Carolina’s Cancer Center trained Watson on all their literature on diagnosis and treatment of the disease; they then had Watson review patient records and treatment plans. Watson made the same decisions as the Cancer Specialists 99% of the time. For 300 patients, the AI suggested alternative courses of treatment that it discovered with its big data literature review.

  2. You may hear about AI a lot, but tools like Watson aren’t yet used to their full potential
    As it stands, business leaders are aware of Watson and its peers, but few have grasped the field’s near limitless applications. So to-date, most AI implementations have focused on the most intuitive use: deepening customer engagement.

    Chatbots and other consumer-facing, digital experiences are where most people will experience Watson in 2017. And IBM has big goals to make sure Watson’s “talked” with everyone: by the end of 2017, Beth and her team are working to make Watson speak with a billion people around the planet.
  3. Businesses that succeed at implementing AI have both interested executives and folks in the trenches
    According to Beth, it’s not enough to have executives interested and aware of AI technologies: the people “on the ground” need to be fluent and interested, too.

    “Data Science teams seems to have exploded over the past two years,” said Beth. In her experience, this bodes well for adoption of tools like Watson. “Ideally, there’s people playing in the data sandbox already” with a solid understanding of how Machine Learning and AI work, so when more complex systems like Watson are implemented, there’s less to learn.

  4. AI doesn’t replace people – Watson still needs teachers
    Watson is a quick learner, but it still needs a place to start. When Watson gets deployed, it’s still  built on a human training the system.
    “Many successful companies have established a center of excellence to observe Watson, and offer it more training when it needs it,” said Beth. Though the tool is smart enough to notify its human handlers when it needs to learn more about an idea, interaction, or outcome, ongoing human observation is key for making sure AI technologies run as effectively as possible.

What’s next?

“We’re transitioning from programmable, rules-based computing to a cognitive era,” said Beth. What does that mean for humans? For one thing, technologies like neural nets don’t require programmers to anticipate every possible condition of an interaction. The upshot: businesses get more time to focus on growth and refinement – while patients and customers get better services, quicker.

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