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Cities / Autonomy enables adaptive built environment
Designers’ novel thinking about data, tools and methods is advancing to a point where it’s possible to foresee an autonomously crafted built environment, one that mimics nature’s ability to adapt to environmental change over time. This technology will be a vital way of dealing with the effects of an increasingly volatile climate.
When built environments’ systems possess artificial intelligence (AI) fed by sensors a degree of autonomous decision making becomes possible. Autonomy is achieved by combining local learning from cameras and sensors, correlated to data and intelligence drawn from other AI-enabled assets.
Built environments that respond to a changing climate
This combination of advances means our built assets will be able to respond to their environment, autonomously reacting to changes in temperature, weather, human usage patterns, and other factors. In this convergence, designers and data scientists contribute their insights into the model, to ensure the variety of aspects taken into consideration and sheer volume of data is provided to shape the kinds of adapting preferred scenarios the artificial intelligence understands. These scenarios in turn train machines to rapidly produce the most sensitive and customised design solutions. A continuous feedback loop of data from the asset’s environment and its users ensures success.
The comfort and energy performance benefits of this new approach are clear. A feedback loop is used by the Hong Kong-based start-up Ambi Climate, an Internet of Things (IoT) app that controls individual air conditioning units located in different rooms from a smart phone. Ambi Climate learns the inhabitant’s preferences (times at work, temperatures enjoyed), applies this knowledge, and autonomously creates a tailored profile.
In the future, because homes, buildings and urban infrastructure will be connected and self-aware through smart components, design updates will occur autonomously. Inefficient, over-scheduled maintenance schemes will be replaced by machine-learning algorithms that are far more capable of knowing when preventative maintenance is needed, based on a growing bank of performance data from sensors. Resources and energy will all be saved.
Challenges of autonomy
Autonomy also represents a challenge to traditional human roles in the design of the built environment, because it can match and surpass human solutions at scale. This approach will be an improvement on today’s often outdated, inappropriate designs, ones that often focus only on the requirements of society’s top one per cent. The challenge for human designers will be how to embrace this new data paradigm of timely, appropriate and scalable design solutions.
This amount of autonomy also presents a challenge to the operators and regulators of the built environment. Current data privacy and confidentiality barriers will need to be overcome and new local connectivity systems for instant and robust connectivity (hubs) will need to be developed to provide instant yet democratic harmonization of the built environment.
Adoption a question of time?
Fully autonomous operation might still be in the future, but a measure of it has already been achieved on projects like the 3D printed Daedalus Pavilion. On this project an algorithm autonomously adapted the material density required for the building. At the same time, a robot fabricator with cameras connected to AI capabilities was able to judge how far its landing position to deposit material was from the design position, and thus able to correct itself. An AI feedback loop allowed it to be quicker by being more daring – it learnt from its mistakes.
With the amount of investment currently being directed at machine learning and artificial intelligence I think it is more a question of when, not if, autonomous decision making like this will become possible.
How do you think we can grasp the opportunities and overcome the challenges of an adaptive built environment?