In today's rapidly evolving technological landscape, many large companies view the burgeoning capabilities of computation and machine learning merely as tools to enhance existing processes—making them better than they were before; better, stronger, faster. However, this perspective overlooks the transformative potential that these technologies bring to the table, particularly in fields like architecture and urbanism.
The Evolution of Computational Power
As computational power increases, new skills and abilities emerge that were previously beyond our practical reach. The concept of neural transformer networks, for example, dates back to the 1940s when Warren McCullock and Walter Pitts published their seminal paper, "A Logical Calculus of the Ideas Immanent in Nervous Activity." Over the decades, this concept has evolved, and its implementation today has unveiled capabilities that are nothing short of revolutionary.
Overcoming Past Limitations
For the past decade or two, the application of computation to solve complex, multi-dimensional problems in the built environment has been hampered by two significant limitations:
Problem Definition: Humans have traditionally excelled at defining complex architectural problems, a skill that machines struggled to match.
Computational Power: The sheer brute force required to process and express variables in useful resolutions was lacking.
Additionally, there were more immediate, less complex problems that computational capabilities could address, making the vast physical realm of construction and urbanism a daunting frontier to tackle. Mothodology in this field is deeply rooted and built upon layer after layer of established methods and best practices, analogous to the working parts of a combustion engine. It can be hard to innovate on top of the established system even if the idea is revolutionary, due to the complexity the system is built on.
The Turning Point
Today, even a high-end laptop can tackle an n x 10^1 dimensional problem over a weekend, effectively overcoming the second limitation stated above. This advancement brings us to a critical juncture where properly defining the problem (and to some extent addressing the more obvious, immediate issues) can lead to groundbreaking outcomes.
We are now in an exciting era where accurately defining problems allows us to explore solutions that add value across multiple metrics simultaneously. By harnessing the power of advanced computation, we can navigate through high-dimensional problem spaces, optimizing for return on investment, beauty, community benefit, public pride, and other performance indicators.
The Future of Problem-Solving
No longer do we need to rely on arbitrary judgments. With precise problem definition and the ability to run sophisticated simulations, we can collect viable solutions and explore new possibilities. This approach opens up a world of opportunities for innovation and efficiency in architecture and urbanism, transforming how we design and build our environments.
In conclusion, the integration of advanced computation and machine learning into architectural problem-solving is not just about doing the same things better. It's about unlocking new potential, redefining possibilities, and achieving outcomes that were once unimaginable. The future is here, and it's time to embrace it.
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