AI News, Designing with Machine Learning

Designing with Machine Learning

The research team is currently developing ways to ensure our spaces have the right mix of meeting spaces in our locations How many meeting rooms do you need in an office?

Even experienced architects and designers struggle to allocate the correct number of meeting spaces, relying mainly on rules of thumb and intuition to overcome the lack of empirically verified guidance.

On any given day at WeWork you can find our members in meeting rooms, pitching potential clients, holding an interview, conducting a lunch and learn, or hashing out the details of their next big idea during a brainstorming session.

With so many important interactions happening every day, we take our role seriously in developing spaces that encourage our members to connect, learn, and grow.

In total, we gathered information from over 800 meeting rooms, and examined a variety of space types that included everything from a boardroom in London, a screening room in Los Angeles to a conversation room in Shanghai (a room with couches instead of desk chairs).

It’s a concept that has been around for decades, but with the recent rise of significant breakthroughs like self-driving cars and voice-activated assistants, machine learning has become a much talked about subject.

Even with this recent attention, it hasn’t made much of an impact on architectural design, and our application of machine learning in the evaluation of architectural layouts remains highly novel in the design industry.

The neural network gathers data about a building’s layout ( left) and feeds this data through a series of artificial neurons that are trained to recognize differences in a building’s layout (middle).

Based on our studies, we estimate that the neural network is 40% more accurate than human designers in predicting how frequently a meeting room will be used by the building’s occupants.

These graphs compare the actual number of hours a meeting room was occupied with the hours predicted by our designers (left), and by our neural network algorithm (right).

As we add more locations to our portfolio, open locations in new cultures, and test new layout strategies, our team will have more data to feed the neural network.

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