Architecture is one of the last few specialized subjects (or maybe the only one?) where the indirect communication (aka “written” ) of the subject is fraught with a serious linguistic flaw.
Let me explain: architects traditionally make drawings to explain a design.
But drawings only explain what is built – architects leave dark marks on the paper ( these marks are called by a french term: Poché) to represent what is built. Drawings represent spaces in buildings rather poorly. Spaces, need to be interpreted by the observer. And interpretations often go wrong.
So the serious linguistic challenge in architecture is that it has a figure-ground issue.
Those who want to understand any built-environment in depth need to know both the built-matter (e…g. walls) as well as the what is NOT the built-matter aka spaces… But spaces emerge in the built-matter because the architect left behind built-matter… and hmmm, the built-matter was left there because the architect thought of the spaces. So both the figure and the ground are hopelessly intertwined.
Just like the figure-ground illusion.
Of course, when architects talk about “creating” space they mean modulating some part of the universal space in such a manner that some opportunities to function are thwarted and some are promoted.
(And as everyone knows; space in this universe is something that can neither be created nor destroyed. It just is there. And we humans happen to live in a tiny sliver of inhabitable space on this blue planet)
For e.g. When Trump visited Ahmedabad in India, in 2020 February; the local government in that city built a wall along the road which would have been taken by Trump. The reason? They wanted to thwart the opportunity for Trump to see the ugly, higgedly-piggedly slums behind. And they promoted the opportunity to uphold the image of a neat and clean city.
That was just a simple, dramatic example. But if you look at any built environment, you would see this interplay.
So when any built-environment needs to be discussed in any serious way, the representation system should not ignore this figure-ground problem and instead, it should be able to deposit all the alphabets that are needed in the subject. That means both spatial representation and represenation of the built-matter.
BTW, there are other subjects that do have this unique representational challenge. Music, for example, puts out both musical sounds (notes, drum-beats, etc) as well as silences between those sounds. When music need to be communicated indirectly (i.e. via a written medium) that written script MUST necessarily talk both about the sounds as well as the silences. And the musicians do have musical notation system to do this rather well.
(Why this parallel was given here; is to explain that it is not just architecture which has a figure-ground issue)
Imagine the linguistic situation in another way: Imagine asking a writer to write some novel without using a few of the alphabets. Say, he is told (as an example) do not use e, k, m, z … now possibly, such a novel could be written but it actually cripples the representation; and can leave a lot to the imagination (or interpretation) of the reader.
On similar lines, when the final means of communication of architecture reside in drawings – which accurately only talks of the built matter; and the spaces merely interpreted… the world would be receiving a poor set of indirect communication.
No wonder, architects are famous for being isolated in their own silos of knowledge. With nobody really sure of what really went into these buildings.
TAD solves this problem due to a mathematical insight by our founder, Sabu Francis. His theories won him the 1991 special award by JIIA (Journal of Indian Institute of Architects)
Using the taxonomy he discovered, TAD actually can talk about ALL aspects of the built environment; be it the built matter or the spaces. This is the first design software that does this quite well – and now this way of looking at architecture has started gaining traction.
This way of looking at architecture also makes the data very tractable by automated systems peering into what was left behind by architects using TAD. Thus one can envisage writing AI programs that can get meaning from the data without requiring architects to manually interpret and provide the feedback.