Get lost

Originally published in BNieuws in October 2020. You can find this article there too.

Cities are full of surprises, mostly pleasant, sometimes unpleasant or even boring. All these elements combined form a complex whole, which is hard to grasp for the human mind. Relying on your phone to get around in this complex whole, however, leaves you with a shallow image of a city. I think that it is nowhere comparable to the experience of inhabitants, which bothers me. Therefore, I would like to introduce you to the concept of “getting lost”.

In this age of GPS navigation, the act of getting lost has itself been lost. Think for a moment about the last time you genuinely ended up somewhere without knowing where you were. There are many forms of being lost and the perception of what it means undoubtedly has changed over the years. The availability of an all-knowing device in your pocket, the amount of information around you and the extensive network of public transportation, makes being lost less serious. Bringing being lost back to the old days is quite complicated in a world where information is all around you. So, how does one knowingly get lost?

There are several strategies, but this is the one that worked for me:

1. Find a vague indication of where you want to be heading, preferably by bike. The first game I played was cycling from Delft to a village a bit outside Delft. For a higher chance of getting lost, consider a residential, not touristic location. In the case of Delft, this could be Maasland, Bergschenhoek, Monster, etc.

2. Don’t look at maps! Not during your attempt, not beforehand, never. Additionally, avoid looking at signs. This will be quite hard, but you must try. They just give too much information away. However, bring your phone with you anyways, just in case things actually get out of hand.

You’ve succeeded when you come to a point where you don’t know how to get back and have the slightest clue of your location. You will find out that the more you play this game, the more difficult it becomes to get lost. Once you get familiar around a particular place, the chance of getting lost drops drastically. In that case, you will have to go to even more alien places. The world is your oyster at that point.

The first time I tried to get lost, I aimed for Berkel en Rodenrijs, but in that area is not very challenging to keep track of where you are. So I kept going and ended up in Rotterdam, which seemed not very challenging either at first. I tried again, until the point that I was almost lost. Almost, since I noticed a somewhat familiar railway bridge in the distance. The train station in Rotterdam-Noord. Damnit. It took another 30 minutes, but eventually, I succeeded: I was completely lost! It happened somewhere around the Insulindeplein in Rotterdam, after taking a ‘wrong’ turn. It is a weird feeling, one of distress and joy combined, but boy, I sure was glad.

What to take from this? I think that we are so used to moving around in a city with a purpose, that we’ve lost the skill of looking. Being lost forces you to look around with all your senses and therefore opens up a whole new world. It makes you vulnerable but connects you to your environment at the same time. So next time you are in a new place or city, you have a choice; do you take the standard way of moving around, or do you challenge yourself with an adventure into the unknown? 

Houses of Machines - AI in Design

Originally published in BNieuws in October 2019. You can find this article there too.

Recent developments in artificial intelligence have posed a challenge to the world of architecture and design. Contrary to previous computer methods, which could only have a deductible output based on a formal input, artificial intelligence replicates the creative process. This has posed a threat to architects: what role will AI have for the architectural profession? It is necessary to find the right approach to handle AI for architects, urban planners and landscape architects, as the development of techniques for architectural, urban and landscape analysis have been rapidly developing, with a possible future out-performance of architects by these techniques.

Say a system is desired that creates images of domes. It does not know what a dome is, so a discriminator is set up which tries to determine if the picture given to it is a dome or not using a value from 0 to 1, with 0 meaning: ‘This is not a dome’ and 1 meaning: ‘This is definitely a dome’. To do this properly, a large dataset containing images of domes is used, labelled with their according values. To create images, a generator is set up, which generates an image from a large number of parameters. The created image is sent to the discriminator, which renders the initial image of pure noise to be nothing like a dome: the discriminator gives a 0 to the image, with scores being assigned to the parameters of the generator as well. So the generator adjusts its parameters to create a more dome-like image, repeating this process over and over, until the parameters are tweaked just so that the discriminator is convinced the generator produces images of domes. This method is called a generative adversarial network (GAN) and is one of the most common methods for content generation using AI today. A variation on the GAN-method is to use the ‘style’ of pre-existing images as an input for the generator. To use the dome example: say, a baroque dome will be ‘played’ in a gothic style. The discriminator will decipher the ‘gothicness’ of gothic images provided and tweak the generated images of baroque domes so that it looks both like a baroque and a gothic dome.

‍Design Methodology

Design methodology is the study of the methods of design, with an emphasis on architecture. The term ‘design methods’ is quite a broad term: it can describe everything from the process of design in the mind, to the ways of planning, doing business and dealing with contractors. Every building or structure has been built using one or more design methods. Even animals maintain design methods: a birds nest is built on the principle of gathering materials nearby, continuously changing the design according to the needs at the moment of construction.

Within the history of the design discipline, we can speak of craftmanship. A person becomes a craftsman not by reading up on how to become one, but by trial and error, learning from previous work, which itself has been the product of years, if not centuries of trial and error. If one asks a craftsman how their craft was made, they most often cannot tell you how they made their choices of design, other than by instinct. The craftsmanship approach used to be the most common way of designing in architecture during the Classic up to the Gothic period. During Gothic ages, most buildings were built without a masterplan. The art of ad hoc stone masonry, combined with the art of geometry prevailed the design. And even though the underlying ideology for Gothic building was fairly well-documented, the evolution of the style has been completely due to craftsmen, not published literature.

‍Artificial intelligence and design methodology

While the development of ‘design methods’ appears to occur independently from that of artificial intelligence, there are essential similarities between the two, which provide a deeper understanding to the application of both fields in architecture. Notice how the generative adversarial network system eerily resembles the craftsmanship approach to design. The trial and error phase is analogous to the discriminator and the act of crafting without knowing how the craft resembles the generator. A large difference between the two subjects is the matter of time: where the mastering of a craft takes centuries, this network takes weeks to do a similar job.

‍Critique of Artificial Intelligence in Architecture

Some say that artificial intelligence offers a cornucopia of creativity, rendering architects virtually useless, flooding civilization with endless ‘good’ architecture. However, this is far from reality. AI systems, certainly at this point in time, come with great limitations, not only in computing power, but in general logic as well.

Suppose a fully functioning AI system is desired, that produces CAD-drawings given a list of requirements. How would this system work? First of all; using natural language processing, the list of requirements has to be deconstructed into bite-sized, AI-friendly pieces to be interpreted properly.

Now that is known what to build, how should it be built? Propose the system chooses a style at random, ignoring context. It is assumed this style is a classic style of architecture, constructed around the rules of classicism. The system has analysed the distinct placement of elements and their size, ornaments are constructed using StyleGAN, mechanical stability of the proposed structure can be easily calculated using statics software, et cetera.

However, this is far from perfect: beginning with the problem of language. While natural language input processing by AI is improving over time, it remains a weak link in the chain. Exact input being statements as ‘the maximum load on this beam is 500 kN’ and fuzzy input ‘make this room look nice’. The question arises to what extent exact input in architecture occurs. Whilst mechanical requirements are generally clearly defined, statements that appear to be exact, such as ‘the maximum surface area of this room is 50m2’ are not always as exact as they seem in architectural processes. The deviation of these semi-exact statements and their consequences of error differ from client to client. And even more so in architecture than in art: not only does every culture have their own interpretation of art, combined with the different ways of building and demands, there cannot be a consensus of universally good architecture.

‍The 'meaning' of architecture in relation to artificial intelligence

This raises the question: Is it possible to add this meaning to a system? Meaning can be highly interpretative, however, discrete meaning exists in architecture, for example: using chemical analysis, it is highly certain that the dome of St. Peter’s Basilica is made of concrete. So while an AI system might accurately know that Michelangelo painted the dome of St. Peter’s Basilica using its knowledge of all domes, for it to know that the sixteen figures represent the sixteen first popes, a whole other system is needed. For an AI to understand the dome at the level of an expert, an incredible amount of useful data would be required.

And what about the generation of such a dome? Using the GAN method, an image that appears to be such a dome at first glance is generated. The details of the dome are generated based on existing images, merging the existing details to a smudge without meaning. For the system to add meaning, it needs to know the context which is wanted from the user.

Is the process of designing a process that can be reduced to a logical system, or is it truly a black box? The two groups in this debate are that of the designer-scientists and that of the designer-artists. According to the argument of the designer-scientist, there exists a discrete, logically deductible system that can produce an architectural design from a list of requirements, with every step of the way being entirely understandable in its behaviour. The designer-artist adheres to the theory of the black box. According to this argument, while some steps of the design process can be clearly defined, creativity and design at their cores consist of expressions of  the philosophical concept of the sublime: an indescribable sense of quality.

While artificial intelligence could within its logic easily adhere to the designer-scientist (despite relying heavily on the black-box theorem), for it to accurately replicate the sublime, these indescribable experiences need to be described first, which is fundamentally impossible. Of course, there remains the possibility that the sublime consists of made-up constructs, envisioned by a mystique surrounding the - to a designer-scientist potentially describable - quality of the object.

To conclude, artificial intelligence has potential in the analysis of architecture and to serve as a source of inspiration. But is far too limited to create a functional building design at this point in time. Because of the difficulties in the notion of meaning and quality of architecture, one can doubt the feasibility of an AI-architect becoming a reality in the near future.