May 11, 2017 marks the 20th anniversary of supercomputer Deep Blue’s victory over world champion chess player Garry Kasparov. Deep Blue’s victory was the first time a computer beat a human chess master in a standard match format.
Technology has advanced dramatically since 1997, and so have anxieties about artificial intelligence and the possibility of automated bots taking over human jobs. Some estimate that by 2025, up to 40% of jobs could go to robots. If machines can do our jobs better, what does our future at work look like?
Workers in almost every field will be affected in some way by automation. Machines are better than humans at repetitive, brute force tasks, and can now even beat humans in well-defined games like chess and Go. They could replace workers in service industries and administrative positions, and even have some management capabilities. But for innovators who are tasked with problem solving and imagining the future, human curiosity and playfulness will always have the advantage.
Take this recent scene at a Fortune 500 company where I worked. Senior level executives gathered on the floor of a cleared out conference room like preschoolers at recess. Recycled cardboard boxes, colorful shards of construction paper, gnarled pipe-cleaners, scented markers, and hot glue were peppered around the room. The participants huddled with their teammates around their prototypes and put the pieces in place, building thoughtfully as they work together.
In the non-profit and public sector, design thinking—which often looks like structured, open-ended play—has become a popular vehicle for creative problem solving and innovation.
Major corporations like Procter & Gamble have used design thinking to create new product lines that have turned around struggling brands like Mr. Clean. Design thinking is no longer considered just a marketing tool. The United Nations has also used it to redesign informal sustainable development settlements, like remaking a football pitch in Kenya. Design thinking is now taught in places like the business school at Cornell, and it even has its own department at Stanford. It allows for real-time improvisation and engagement that makes things work.
In the real-life training session I described, an internal knowledge management software project was running late and over-budget. It was not clear if the work in progress really addressed the needs of the employees who would be using the tool.
The executives hit “pause” and “reset” on the project. They started from the beginning, using empathy as a tool. Through interviews and observations, they tried to understand their colleagues’ knowledge management needs. From there, they formulated a problem statement, reframing the original problem as needed.
If you look beyond the low-fidelity arts and craft aesthetics, the methodology that we teach is not that different from the scientific method: understand, hypothesize, test, rinse, and repeat until we get it right. Our approach goes beyond problem-solving and also works to cultivate a creative culture through a designer’s mindset.
This mindset begins with empathy for the needs of our fellow humans. It is open to a diversity of viewpoints and professional disciplines. It withholds judgement when it is time to ideate, and relies on evidence to make decisions about what works. This requires skilled facilitators who have built intuition through experience. This intuition helps us determine when to foster open-ended play, and when to switch to a more analytical, critical mode of thinking.
While computer artificial intelligence can help us optimize systems and processes, and even replace humans in many job functions, machines cannot yet have that intuition, nor the ability to empathize, reframe problems and truly innovate.
Technological innovation is attempting to bridge this gap. Last year, researchers reported that Google Translate had developed its own meta language to translate between languages it had not previously been trained to engage. In other words, in a vaguely frightening, sci-fi-like development, computers can now at-least partially program themselves, and their human masters don’t fully understand what is happening. But optimization is not the same as innovation. While Google Translate can make it easier to communicate in different languages, communicating beyond the language barrier is still distinctly human. Humans will always win on gestures, making mistakes, humor, and how that all feeds into human connection.
There may be a day when robots learn to brainstorm, play, and innovate, and therefore deliver an unfair advantage to human beings. But for now, humans corner the market on playful and divergent thinking—the kind that breaks through barriers and sparks new ideas. The robots may be coming for our jobs, but it is too early to call checkmate on human ingenuity.
Lee-Sean Huang is a designer, educator, and futurist based in New York City. He is a co-founder of Foossa, a service design and storytelling consultancy and a participant of the Allies Reaching for Community Health Equity (ARCHE) Public Voices Fellowship with The OpEd Project. He also teaches design and futures thinking at the Parsons School of Design.