AI Tech + Humans = Improved Bottom Line #KMWorld

KMWlogo_Stacked_Session Focus:  Semantic AI — how assisted intelligence improves human productivity. Metadata drives insight.

Session Description:

The AI hype is rapidly exploding into C-suites of organizations around the world and with good reason; the promise is compelling. The convergence of AI, robotic process automation (RPA), machine learning and cognitive platforms allows employees to focus their attention on exception processing and higher-value work while digital labor manages low-value, repetitive tasks. While the debate as to whether digital labor will add or eliminate jobs is ongoing, what’s important in today’s enterprise is how digital and human labor can be integrated to improve efficiency and drive innovation. Using real-world examples, this session covers how machine processing, when guided by human knowledge, curation and control, provides assisted intelligence (AI) to organizations which want to streamline processes, reduce operating costs, and improve ROI.

Speaker:Jeremy Bentley, CEO & Founder, Smartlogic

See Also:Artificial Intelligence Hits the Barrier of Meaning” by Melanie Mitchell.

[These are my notes from the KMWorld 2018 Conference. I’m publishing them as soon as possible after the end of a session, so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • How to Improve AI’s contribution to business value.  How semantic classification, harmonization, extraction, and enrichment makes AI work and deliver values.
  • What is real about AI?
    • the promise is real
    • the ability to automate higher-order work functions using digital rather than human labor at a fraction of the costs will disrupt global markets
  • What AI are we talking about?
    • Machine learning
      • Deep learning
      • Supervised/unsupervised
    • Natural language processing
    • Expert systems
    • Vision
    • Speech
    • Planning
    • Robotics
  • Myth #1. “You just give the machine the data; it learns and then delivers useful insight.”
    • If you are the machine, how would you look around the room and decide to classify/sort it?
      • a group of humans
      • organized by gender, hair color, handedness, whether people are wearing glasses
    • Whether you are a human or a machine, you need context in order to understand the data you are observing.
    • METADATA helps convey context. This is what machines need to gain and provide insight.
  • Myth #2. “We will  quickly assemble the training set, then pour it into the machine and it will deliver actionable intelligence.”
    • it’s actually not just one set:
      • a training set
      • a validation set = a control to compare the resulting output
      • a testing set = to test the output
    • You need to create this trio for each data set in which you are interested.
    • A one-dimensional classification is binary. It separates data into two groups.
      • if you ask the machine separate the humans into two groups: one group = people wearing black or white shirts, the second group = people wearing all other colors.
      • to do this exercise, the machine must first:
        • exclude everyone who is not wearing a shirt
        • learn the concept of color and how to differentiate among colors
        • learn the concept of shirt and how to deal with blouses and tops
    • It takes a lot ot time, effort, and specialist knowledge to assemble these training sets. The more data dimensions you include in the training set, the more likely you will need to include a mathematician in your team. This is another barrier between the business experts and the machine.
  • Myth #3. “Machines are going to be autonomous decision makers soon.”
    • Today, AI is not mature enough to provide full authonomous decision support
    • It can automate the next level of repetivive work, which is compelling from a cost and efficiency perspective
  • Takeaways.
    • the fewer dimensions, the better the quality of the machine’s output
    • subject matter expertise is paramount
    • humans provide knowledge and context
    • meaning is referenced from ontologies and then encoded in metadata
    • there should be an interplay between human and machine
    • we should play to respective strengths: humans do creative work, machines do repetitive work
    • metadata is how you encode context for the machine
  • AI Business Value Continuum.
    • Automated Intelligence — this is increasingly common
    • Assisted Intelligence — we have begun using assisted intelligence
    • Augmented Intelligence — we should have achieved this in the next 10 years
    • Artificial or Autonomous Intelligence — this won’t happen any time soon
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