Innovation has become accepted as central to competitiveness in today’s world, both in new product development and in enhancement of internal processes. Companies struggle with innovation, and there have been numerous attempts to regularize and program it. But the development of truly breakthrough ideas is difficult, and recognizing them when they do arrive can be harder still. We have processes available for vetting ideas and passing them through a series of increasingly selective gateways until they reach the point of usefulness or are discarded altogether. But we do not have good processes for stitching together new ideas and reaching that eureka moment that says a critical new idea has been found.
Some of the ways that ideas are sourced include crowdsourcing, internal suggestions, brainstorming, and the like. There are idea factories employing innovative individuals who apply diverse experience to create an “out of the box” concept. And, there are programs such as TRIZ, an innovation program developed in Russia in 1946 that seek to apply a systemic process to ideation itself, based around principles extracted from patent literature subjected to contradiction, synthesis, and new arrangement. But creation of ideas is forever thwarted by the fact that we don’t really understand the creative process and may, in fact, be generalizing a multitude of processes in a way that makes them impossible to replicate.
The most recent forays into the area of programmatic innovation come from the evolving science of computational creativity, a term created to serve as a central term for a general examination of systemic creativity. This is a “grand collaboration” between the arts and the sciences that has resulted in attempts to apply abstracted principles of creativity to a diverse spectrum of areas ranging from music and poetry to general problem solving. It is supported by leading IT vendors, as well as by organizations such as the Association for Computational Creativity and by academia.
To date, the results of computational creativity have been highly mixed, partly because it underscores the problems of defining creativity across the range of fields in which it is important. Some general principles have been abstracted, but they have proven difficult to apply across all fields and, indeed, what is considered “creative” might well differ according to the territory. Is creativity in jazz the same thing as creativity in scientific research? Looking at the creative process itself is, however, beginning to yield new understanding, and the more recent application of big data to this territory is starting to provide some interesting results.
The most visible attempt to bring big data to bear on computational creativity is through IBM’s Watson program, applying its Jeopardy-winning computational appliance to the development of new food recipes (see “IBM Watson and Bon Appétit Team on New App That Transforms How We Cook“). As with Jeopardy, the result appears somewhat trivial on the surface, but they are extending what we know about creative processes and how they can be applied to a diverse range of problems.
Innovation has long suffered from the same issues as creativity, with a few added ones, such as a distinction between market perception of innovation as driven by targeted communications and actual innovation in true breakthrough product design. Creativity feeds innovation. We can program the processes by which innovations are vetted and selected; can we then program the creativity that leads to new design?
Ultimately, it is likely that computational creativity will lead to new tools for design, and new ways of enhancing human input. It is unlikely to take over the innovation process anytime soon. But, in a world starved for new ideas and creative new uses of energy, technology, and materiel, this is an area that deserves to be given closer attention.
Photo by Jared Tarbell via Creative Commons license.