Unlimited Complexity WITHOUT Marginal Cost

Many people are becoming familiar with AI generated art such as that made by DALL-E 2. Algorithmically-generated design operates in a similar automated manner, using a set of rules, constraints, and aesthetic themes to generate 2D and 3D designs. This might be something like a wind turbine blade design, an architectural facade, or a building layout fit for a certain purpose.

When paired with additive manufacturing (3D printing), both the design and manufacture of a 3D object can be made in one automated process, with incredible complexity, but no extra marginal cost. In the 1900s, we still had a fashion for beautiful and intricate design, but this has been replaced by stark utilitarian mass produced simplicity, inoffensive and generally timeless, yet also dull and soulless. We might soon see a renaissance, whereby plainness becomes passé in a world where beauty has become next to free.

In urban spaces, algorithmic design can be applied to interior and exterior building design, the sequencing of construction to build more efficiently, town planning for current and future predicted needs, and even warehousing and logistics. Some systems now can even predict the varying rental yields from installing apartments, shops, or offices on a certain floor. These capabilities have the potential to greatly reduce the time and costs required for construction.

However, algorithmic design also presents potential pitfalls. For example, many government and financial systems were built back in the 1960s, with built-in assumptions that a Dr. must always be a male, that persons married are always of the opposite sex, that people never change gender, or that gender markers will always be an M or F, not an X. All of these changes in society have created immense challenges for legacy systems designed to work in a certain way. The changes we have seen since were unimaginable, and were never accounted for, especially given very scarce computing resources.

We should learn from this, to build flexibility into system architecture so that components can be modified as necessary. Design processes have assumptions baked in, everything from fire regulations to the expected size and weight load of human beings. Most kinds of parameters will naturally vary across time, culture, geography, and this inevitability should be accounted for with tolerances and maintenance in mind. Moreover, since machine learning models need sets of examples to learn from (datasets), they come with temporal biases baked in. Their knowledge of the world will be forever dependent on when the model was trained, and the age of the data it learned from, with its old-fashioned impressions of a world that has since moved on.

Most of us have heard stories from acquaintances who got in trouble on social media for making a harmless statement which some content moderation algorithm took in the wrong way. People may receive a ban for discussing a chess game of black against white, or stating that they 'shot themselves in the foot'. Sometimes an unscrupulous engineer may even choose to interpret a particular statement in a particularly uncharitable manner that favors their worldview. Where context is lost, by accident or at will, justice and truth can never prevail. It's crucial that nuances are included in all forms of deliberation, and most especially in non-transparent algorithmic processes with the power to abuse us with Kafkaesque petty tyrannies.

We should be mindful of these challenges in the domain of urban design. After all, whilst machines are learning to navigate environments, they can never know the experience of doing so. We must never sacrifice the feel of an urban landscape at the altar of efficiency, nor cause a malfunction in any person's enjoyment of a resource. The greatest question of AI is not whether we can do something, but rather if and how we should go about it, in a manner we can trust. The actions of a system must be concordant with human needs, not the needs of the system. The 2020s will herald a desperate struggle to teach machines to recognize, acknowledge, and respect our values, before we are subsumed into their encompassing grasp.

If we can indeed achieve that, the future seems a little bit brighter.