New manufacturing technologies that take advantage of robotics and automation like additive and subtractive fabrication tools, and pioneering work in AI, simulation, and synthetic biology, including entirely new ways of understanding how collectives of cells and “selves” explore creative ways to reach goals (look up the Levin Labs work on morphogenesis) are enabling us to shape the world like never before. One of the most foundational new methods for designing and making things is at the intersection of advanced manufacturing, the IoT, and AI. It’s called Goal-Directed or Generative Design.
The concept is that humans can only keep a few goals or dimensions of a given challenge in their head at any one time. But what if you could articulate individually or as a team all of the dimensions that constrain or open up your option space? Consider an automobile. We might have a goal for it to be smooth riding, but able to handle turns at high speeds, to be lightweight to save of energy, constrained to be easy to maintain with only a few tools, to explore various ways of manufacturing each part, subassembly or entire system based on the obstacles your current budget or location or existing factory puts in your way. If you articulate them as almost the negative space of what you can’t do and the positive space of what you aspire to do, from form and function and manufacturing methods and material options to maintenance and disassembly or disposal dimensions, and then enlist agents to explore those higher dimensional spaces, you can search a much vaster collection of possible solutions than might be possible by one person or team alone.
In a provocative experiment that my team ran a few years ago, we explored the idea that a product could participate in its own redesign.
Along the way the team also learned new ways of thinking about design, not only for the vehicle but for the surrounding ecosystem–from new supply chains to new accessories and environments. They were gaining a sort of “system sense” and exploring far more possibilities per unit time than ever before. While the team had always understood the trade-offs they were making as they played out scenarios, with generative design they were able to get an innate sense of how one change over here rippled through the system over there. They found that the way they made things shifted and the act of defining their goals and “playing” with the system–setting weights on this or that goal higher or lower than some other goals–exposed their tacit assumptions and created a rich feedback loop of discovery. Design became more like Mendel’s exploration of pea pod hybrids. Tweak a characteristic here or there and see how successive generations play out in a game of genetic recombination.
At the beginning of the project Felix Holst, the Chief Product Officer for the “Hackrod” team, had concerns that generative design systems would marginalize the role of human creativity in the process. “I thought, that’s it, my career is over.” He noted, “it was so profoundly different than the way I learned to design.” As he explored the complex feedback loop between setting goals, sensing forces, and exploring design possibilities he realized, “the power of generative design, running with cloud processes, outstrips anything a team of human minds alone can come up with.” Soon he began to play with the possibilities and fast became a convert. He now feels like he’s in a renaissance in his career and sees the power that these tools bring to extend his own creativity to new heights. “I can’t look back, the old way of designing is too limiting for me.” What he discovered was that the generative design system was a new kind of team member and that together they could collaborate and go farther and faster than humans (or machines) alone could ever hope to do.
While Hollywood would like us to believe that most innovation comes from the lone mad genius inventor, Felix knew that the challenges ahead required deeper and deeper forms of collaboration. As complexity increases and more and more problems are at the intersections of different disciplines rather than squarely in one or the other, teams of agents, sometimes human, sometimes biological, sometimes computational will become the central currency of innovation. Those teams may be super powered by fostering a mixed dialog between both human and these other machine and biological learners.
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