An approach to human–machine collaboration in innovation

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 humancomputer collaboration in which each partner assists the other in unearthing the obscure features of a problem.

Human-AI Synergy in Creativity and Innovation

“…In sum, although we calculated that there may be about 10 18 possible interactions between one object and up to five other objects out of 10 million possible objects, taking into account the incredible number of ways that any two objects can interact with each other plus all the possible conditions that those interactions could take place in, raises our number of interactions at least several orders of magnitude and quite possibly many orders of magnitude [6]. The overall result is a number of possible interactions that becomes increasingly beyond the ability of current and projected supercomputers to explore even if they were running since the invention of the first computer.…”

“…Given that features are a crucial aspect of creativity, a definition adapted from the philosopher Nietzsche permits the number of features of an object to be quantified [6]. Nietzsche states: "The features of a 'thing' are its effects on other 'things': if one removes other 'things,' then a thing has no features" [14].…”

“…Yet another critique argues that AI, including IBM Watson, is still several breakthroughs away from being really intelligent, but this critique does not specify the particulars of these needed breakthroughs [5]. Our theory is that IBM Watson is exhibiting the limitations demonstrated in a 2017 mathematical proof of the limits to a computational approach to innovation and analogical thought [6]. No matter how many academic journal articles Watson processes about its topic of choice, it cannot overcome the proven limits.…”

“…The 2017 mathematical proof details two of these limitations [6]. Humans can help counter these limits.…”

“…All the proof's details are contained in Ref. [6]. The main conclusion is that no computational approach can fully take over the creative or innovative process.…”