The designer can then have results from the surrogate model’s approximation of a simulation in real-time, which is good enough for them to make a quick decision. For that reason, the ARD team investigated surrogate models, where a computationally ‘cheaper’ predictive model is constructed based on a number of intelligently chosen simulation results. To solve this problem, we need to provide designers with results in near real-time. Covering over 200 different qualifications, our staff include aerospace engineers, computer scientists, game artists, sociologists, geologists and fine artists. Design optimisation based on their findings has been nearly impossible due to the time and work involved, undermining the benefit these tools can provide.ĭemonstrating the complexity of the cutting-edge nature of the architecture profession, the staff at Foster + Partners are specialised in an extraordinary range of disciplines. To assist in this diversified field, machine learning might enable us to solve problems and detect patterns historically dependent on the complex analytical simulations and programmes. There is an enormous scale and complexity to many architectural projects today, typified by a varied array of intersecting expertise and technologies. The first is known as surrogate modelling a direct replacement for analytical engineering simulations (such as structural deformation, solar radiation, pedestrian movement), which take valuable hours, or even days to complete. The ARD team have been exploring two ways in which it can be incorporated into the design process, as well as some potentially more revolutionary applications. In the context of architecture and design, machine learning has great potential to analyse our designs more quickly and at less cost. Courtesy of Science & Society Picture Library / Getty Images The earliest successful AI program was written by Christopher Strachey to play checkers, or draughts, and ran on the Ferranti Mark I. There are many systems and processes that need to be perfected in order to accurately mimic human intelligence an example would be the subset of AI known as machine learning, summarised by computer scientist Tom Mitchell as ‘the study of computer algorithms that allow computer programs to automatically improve through experience.’Įnglish mathematician and pioneer of computer science, Alan Turing, standing at the Ferranti Mark 1 computer at the University of Manchester in 1951. The cutting-edge area of ARD’s research investigates the potential for these systems to provide genuine design assistance Thankfully, AI has continued its evolution with mostly altruistic innovations: it can help us to understand and react to human speech, screen for breast cancer, develop robots and self-driving cars, or just recommend us films we’d like to watch. Since its genesis, it has been a fertile topic of debate, repeatedly making headlines – both good and bad – and becoming enshrined in popular culture and science fiction. At the forefront of this is the Applied Research and Development (ARD) team, who have recently been investigating the potential of using artificial intelligence (AI) in the creative process.Īs a field of academic study, AI was established in the 1950s and can be broadly defined as the development of machines that display human intelligence and behaviours. At Foster + Partners, we have a long history of exploring, adapting and harnessing available technologies in order to expand and improve our design capabilities. But at Foster + Partners, the Applied Research and Development group is rethinking this mindset and embracing machine learning, not to replicate or replace designers, but to enhance our knowledge, instincts and sensitivities, free us from routine tasks, and to optimise and push the boundaries of our designs.Īrchitecture today is a multi-skilled profession, calling on disciplines from structural and environmental engineering, to social and material sciences. Advances in artificial intelligence and machine learning are often perceived as a threat to the creative process.
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