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On November 5th 2019, I had the opportunity to participate in a keynote panel discussion at KMWorld 2019: Knowledge Management In the Age of Smart Machines. During my discussion (at the 15:10 mark on the video below) I spoke of the need for diversity of thought in the development of algorithms for machine learning applications.

In a post from 2011 about the need for diversity I stated:

The power that Knowledge Management (KM) brings to an organization is its ability to leverage the power of diversity. I am not speaking of just diversity of race, gender and/or religion, but diversity of thought.

Through collaboration, knowledge sharing, and knowledge reuse it is important to leverage different points of view, different experiences and different cultural backgrounds to stimulate diversity of thought. This diversity of thought leads to innovation. This innovation will enable organizations to deliver unique and or improved products and services to its customers as well as improve the way the organization does business.

Diversity of thought is encouraged and utilized today in the push by corporations to support Board Diversity in expanding the makeup of their corporate boards, through Affirmative Action programs to promote a diverse workforce and through a myriad of organizations that understand that diversity of thought will improve everything from our educational system, healthcare system, create new jobs, and improve how our leaders work together. The need for diversity of thought will continue to be a catalyst for our culture to improve the way we live, work and play.

Also, in my discussion I mentioned that smart machines will not in all cases replace the human performing the task(s) but will augment or assist the human in performing the task.

Smart machines include robots, self-driving cars and other cognitive computing systems that are designed to work through tasks without human intervention. Many smart machines can replace humans in completing a task; robotic automation in manufacturing facilities, for instance, can and does replace human workers. But some smart machines, such as those used to diagnose diseases and recommend the best treatments, work for humans (i.e., doctors).

Technology research firm Gartner Inc. predicts that smart machines will enter mainstream adoption by 2021. Furthermore, it expects smart machines to be the most disruptive class of technologies over the coming decade. Gartner puts cognitive computing, AI, intelligent automation, machine learning and deep learning under the smart machines umbrella.

Take a moment to review the video, I look forward to your comments and questions!



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