This is the third installment in a multiyear conversation about the evolving role of data and technology in creating more people-centric design. We wrote about User Experience as a lens through which to revisit assumptions about design and create environments that enable performance and satisfaction. In our second, we discuss how Measuring Happiness—which is far more feasible than many people believe—offers new ways to explore the intersection of space and performance. Below, we discuss some near-future predictions based on our experience with clients.
When conversations about workplace and real estate turn to the disruptive potential of artificial intelligence, we sometimes offer a pithy response: it’s impossible to have artificial intelligencebefore one has regular intelligence. That is, one can’t hope to teach a machine to do things that are not yet well understood by flesh-and-blood humans. As we have learned more about cognition and psychology, the chasm between what we can observe and what is automatable actually feels wider, not narrower.
So, it is perhaps unsurprising that AI for architecture and design is still in the early stages of development. Despite quite a lot of pedagogy and thought leadership about design, we don’t really know how people do it—not in a linear, describable, procedural sense that a computer could understand. Computers can be given constraints and be programmed with aesthetic rules of thumb like the Golden Ratio, but these are merely human heuristics, not the capability to make actual judgements about quality.
Even so, the information that is already at our collective fingertips is more valuable than we may think. As we recently wrote for Corporate Real Estate Journal, a workplace is an environment full of tools that are data–enabled, or easily could be: secure access points, destination elevators, computers, mobile devices, and even smart appliances. Today’s tech-enabled workplaces now provide enough data to begin differentiating between human factors that are relatively consistent from place to place—for instance, the positive impact of access to daylight and a clear sense of prospect—and those that are particular to an individual environment and culture.
The ubiquity of low cost computing power and the availability of rich data sources have set the stage for a period of rapid innovation in smart offices. Innovators in programs like MITDesignX are continuing to explore the intersections between technology and design—their annual cohort regularly includes at least one AI-themed project. What shape might this all take in the near future?
Social data, the difference between math and AI
Compared to human awareness, much of what passes for AI these days is really just fancy mathwith a side of marketing. The exact dividing line between AI and regular computation is a fuzzy one. A fascinating piece by AI researcher Arend Hintze makes clear: if one thinks of AI on a scale ranging from digital alarm clock to self-aware machines that can run the world, we are still pretty far from the latter. Once one understands the way the algorithms are built, the artifice of intelligence often falls away.
Still, a smart speaker is qualitatively different from a calculator. By creating a program of sufficient complexity and giving it access to a rich collection of human-generated data, we have created a simulacrum of intelligence. While it may not be intelligent in the philosophical sense—it lacks free will or self-awareness—it exists in the space between a simple machine and consciousness, and it delivers value to the user.
What really defines artificial intelligence in the sense that people tend to use it may be the type of data that informs an algorithm’s decision-making. To simulate or augment human intelligence, machines must first be given human-centric data on which to operate and human criteria with which to evaluate it. As we like to say, smart buildings are social buildings.