Can computation give us an insight into the intelligence of the mind? In the summer of 1956, the field of AI was founded at the Dartmouth conference in which bold predictions of the future emerged. Those who participated proposed numerous ambitious futures, in which AI would be achieved within a matter of decades. The challenges to reaching those goals set during the summer of 1956 were due to the limitations in hardware and processing. These goals were eventually supported to a significant degree by the US Department of Defense with numerous contracts throughout the 1960s. The two leading models during that time were differentiated in the way they approached solving learning. One methodology was a natural approach, with a model based on neural networks, built up of a network of nodes called artificial neurons in which learning is governed by links and activation. The second methodology is known as machine learning, an applied system that replicated learning and problem-solving through environmental perception and a goal system that is capable of learning heuristics to maximize goals. The reciprocity between human behavior and digital technologies at that time was in the nascent stages of understanding but those early concepts have made leaps toward the advent of increased automation, computational design, AI, communications, and the ubiquitous computing devices that are embedded in everyday life.

The discoveries in computer technologies, both software and hardware have been responsible for the increased computational capacity we use in everyday devices. Since the 1970s, the number of transistors that fit in chips has doubled almost every two years. Moores’s Law is synonymous with computing advances and the proliferation of newer, faster, and more reliable processing systems. The processing of highly complex mathematical problems and large amounts of data are responsible for the accelerated efforts in building intelligent machines. Although increased bandwidth and ubiquitous communication technologies are a necessary pre-condition for building AI, it has yet to produce the critical mass required for a sophisticated network capable of AI. For many technology companies, achieving some semblance of AI to amplify productivity and/or added control will only encourage the application of technologies that connect people to computation at a global scale.

Your phone and your computer are extensions of you, but the interface is through finger movements or speech, which are very slow.

Today Elon Musk is known for leading some of the most ambitious technologies around energy, transportation, and aerospace engineering. This kind of infrastructure puts his mission of achieving space exploration at an advantage. It is integral to have this level of permeability into the world of technology and hardware because it advances software and computing applications, unlike a singular industry. In this environment software and AI begin to take on a much larger scale, permeating all of the necessary elements that are only increasing in production and demand. Telsa boasts the necessary hardware for full self-driving capability utilizing what is considered the world’s most cost-effective and powerful processing power coupled with machine learning software. SpaceX is perfecting the reusable self-landing rocket, while built and programmed by humans, is completely automated from the moment it leaves the ground. Drawing parallels between industries and disciplines for companies like this is necessary for innovation and particularly when building an infrastructure highly dependent on software and computing.

“While some lines and frames are more physically tangible than others, for the political geography of The Stack, it is the physicality of abstraction that is at the center of things.” — The Stack: On Software and Sovereignty

According to Benjamin Bratton, we are living in a multi-layered stack of hardware and software in which networks both modulate and order systemic control. Computation as principles is manifested in the real world through looping, branching, processing, and aggregation. We see this most clearly in the way that we occupy space using our digital devices and communication technologies. Architecture, Automotive, Aerospace, and Art are all concerned with embracing computation. Buildings as a system of systems have become deeply saturated with computational technologies. From design to construction and operation, buildings that define the urban landscapes of our cities are more telecommunication vessels than vehicles or satellites. The greatest challenge for design will be how best and most intelligently to transfer the agency of control to the computational technologies that we depend on for progress and growth.