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Drive unprecedented creative breakthroughs for your organization with AI
Are minds more creative than machines?
That has been a decades-old truism in thinking about AI. At first glance it may seem well-supported. Case in point: an AI-powered computer couldn’t have predicted Uber’s fantastic success. There was nothing prior to Uber that the machine could have used to predict Uber, a first-of-its kind creation. But we shouldn’t be too quick to claim victory over AI. While the “Uber” example is instructive, most human innovation does not begin de novo, or truly from scratch.
Most human creativity is based on an import-export business of ideas. My research has uncovered many of the ways this takes place, including: the grounding of higher-impact science in novel combinations of existing work; the disproportionate success of research that cites literature of a wider age range; and that creative teams, including those creating Broadway plays, with a mix of members who’ve worked together before and haven’t—including both incumbents and newcomers—are most likely to produce more innovative work.
These studies are part of a large body of work suggesting that innovation is built on taking ideas from one area where they are well understood and taken-for-granted conventions, and bringing them into a new area where they are suddenly seen as inventive.
Even Google’s famed search algorithm is not an original creation. The algorithm borrows from work done in the 1970s and 80s, when Google and the internet were unimaginable. Far from the domains of engineering and technology, network scientists such as sociologist Michael Schwartz at Stony Brook University wanted to know which friends would provide the best recommendations about an auto mechanic, a doctor, or teacher. Long story short, the answer was that it had to do with the structure of a friend’s network; the dynamics of such structures became much of the basis for Google’s search-engine recommendation system.
So a key insight about creativity is that you don’t have to be a creative giant like Marie Curie, Picasso, or Galileo to be innovative. You just have to be familiar with lots of different ideas and link them together or export them to other fields to make a convention an invention, as my research suggests. When asked about his superhuman capacity for creativity, scientist Linus Pauling, one of only four people to win more than one Nobel Prize, said, “You can't have good ideas unless you have lots of ideas.”
That may be easier said than done. The challenge is that most human can’t access lots of ideas. There just isn’t time. Especially with the volume of information and ideas available today, most individuals simply don’t have the bandwidth to gain more than one area of expertise.
Luckily, AI can help. AI-based technology ingests, stores, and accesses unprecedented numbers of ideas, images, frames of reference, or musical styles, and therefore can provide extraordinary numbers of new and innovative combinations of existing ideas. AI, in fact, is a creativity machine.
Consider architectural design. AI-based algorithms are helping architects break out of design-related boxes. For example, new systems can integrate the renderings and concepts of thousands of architects and related professionals to combine their designs in novel but viable ways. An idea from a Mumbai city planner might be incorporated into an Oslo-based office park.
In the same way, AI could be aimed at integrating ideas related to consumer products, auto parts, and heavy equipment, working across geographies, functional areas, and teams to deliver more innovative, box-breaking solutions at lower costs and within tighter budgets.
Consider the story of the first chair co-created by humans and AI. Designer Philippe Starck recently teamed with furniture-maker Kartell and tech firm Autodesk to design a structurally sound, sleek-looking, comfortable chair with minimal material and cost. The team fed the AI system questions such as “How can we rest our bodies using the least amount of material?” as part of an iterative process to help it learn and work within constraints. The result was a highly functional, aesthetically pleasing, cost-effective, award-winning chair aptly named “A.I.” that debuted in early 2019.
On the opposite end of the spectrum from constraints is limitlessness. Here, the “A” in “AI” could well stand for “agnostic.” Because machines subscribe to no particular culture or worldview, they can avoid the many biases to which we humans are prone, pushing well past our limits. In the art world, for example, there is a sky-high subjective factor when it comes to valuation; to wit, a Da Vinci painting sold in 2017 for over $450 million, a new record.
Was that a fair price? It’s hard to say, because humans are notoriously biased when judging the value or attractiveness of art, people, or pretty much everything. We let context misguide us, for example, as demonstrated by behavioral economist Dan Ariely’s experiment in which people judged a pictured individual to be more attractive than another (who was objectively of the same attractiveness) only when researchers included a computer-generated image of a slightly less attractive version of the former.
AI-driven machines, in contrast, are less likely to fall prey to context-related or other biases. That ability will be increasingly valuable in everything from judging the value of a piece of art—whether a painting, song, novel, or story—to avoiding longstanding gender-, ethnicity-, and appearance-related biases in hiring and other talent decisions, such as resume screenings. In short, AI can help place meaningful guardrails around typically subjective processes, reducing bias and improving diversity and performance.
The takeaway? Mind or machine shouldn’t be an “either-or” when it comes to creativity. Use both to develop novel combinations and push past human biases, boosting creativity—and value—in your organization.
