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Takeaways: O’Reilly AI

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The first O’Reilly AI Conference served up an AI smorgasbord of ideas at the end of September in New York. We continue to struggle with the resurgence of use of the term “AI” throughout the mainstream press. As we have pointed out, simplistic definitions are erecting big hurdles to useful understanding of the field. Artificial intelligence is a large umbrella term that includes: machine learning of all types, digital assistants, conversational systems, Internet of Things, image and speech recognition, emotion and sentiment detection, cognitive computing, robotics, and more.

O’Reilly conference organizers did a workmanlike job containing the tendency toward topic overload and bringing crisp, thoughtful presentations forward. Across the breadth of coverage, there were several primary themes that bubbled to the surface in the talks I heard. Perhaps the most interesting is the growing sense that we are turning from dreams to reality. What works? What doesn’t? How much manual effort is involved in automating work? Can we eliminate the man behind the AI curtain?

As we start this move from dreams to reality, the topnotch thinkers at this conference were geared up to talk about what’s possible today, as well as to take some careful stabs at what might be possible tomorrow. I will be posting my top level takeaways from a number of speakers during this week. To get started:

Peter Norvig, Google: Machine learning is a sort of black box. It’s fast, but it makes spectacular mistakes, and it’s hard to figure out why. Because it’s not modular, it’s hard to isolate one part and debug it. Often, it’s not the code that’s the problem, but the data that is inaccurate. To make matters worse, the data keeps changing. Norvig called for new tools for non-traditional programming. This is relatively uncharted territory: how do you conceive of tools that you will need to build a system that you’re not sure what you want it to do in the future? Bottom line, however, “Machine learning is the worst possible system…except for all the others.”

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About the Author:

Sue Feldman is Co-founder and Managing Director at the Cognitive Computing Consortium. She also is PrAs VP for Content Technologies at IDC, Sue developed and led research on search, text analytics and unified access technologies and markets. Her most recent book, The Answer Machine was published in 2012. Her current research is on use cases and guidelines for adopting cognitive computing to solve real world problems.
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