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An approach to human–machine collaboration in innovation

  • Tony McCaffrey (a1) and Lee Spector (a2)
Abstract
Abstract

If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a human–computer collaboration in which each partner assists the other in unearthing the obscure features of a problem.

Copyright
Corresponding author
Reprint requests to: Tony McCaffrey, Innovation Accelerator, Inc., 55 Snow Road, West Brookfield, MA 01585, USA. E-mail: tony@innovationaccelerator.com
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AI EDAM
  • ISSN: 0890-0604
  • EISSN: 1469-1760
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