Thursday, February 28, 2008

Lecture 7

For the texts I wanted you to get a different sense of the background of the algorithm -- light reading from Berlinksy, but it will help us loosen our heads about this.

As for the Deleuze reading on Foucault, I thought it would be useful to have a formal description of the "diagram."

Hopefully we can see what is so strange about the algorithm from Berlinksy'spoint of view.

Btw as a summary of Tues, what I wanted to get at was as many possibly different ways of looking at the "analogy" between computation and architecture. Many offered up points about computation and CA (the output) and architecture. Others looked at a methodological relation.

I think this is interesting because I think I specified a Turing machine, but I didn't specify what I meant by Architecture. To the extent that many questioned the diagrammatic aspects of CA, it was important to linger on a bit about that. What I liked most was the question of whether we could read the diagram in the rule set. This is really important. The fact that Durand had already in a way done much of this ought to show us how we might be able to avoid metaphors from other fields by which we return computation to a diagram. Matt's point that the child's game I mentioned is not in architecture is perfectly right. So the point was after reading the discussions posted on the blogs, maybe we could just practice an analogy with something more basic, more innocent and avoid momentarily too much pressure with architecture. I think that was asking a lot. But I think it was the right question. The Berlinksy reading might provoke us to think a bit differently about this.

Tuesday, February 26, 2008

CA = TM = RS

System = Hardware (processor and monitor) + Software
  • the processor is a generic machine.
  • the monitor is a means of display (tape, grid, could be other).

Hardware is only the means.

Software = RS (rule set)

Syllogism:
1) TM is RS
2) CA is RS
3) CA is TM

Perception

What is perception ? How do we consistently perceive a reality and commune, celebrate, document and discuss its existence. Conventions must exist. An economy of descriptors must exist.
What for us is a picture, may for another -- say a blind person -- be meaningless. The CA approach to documenting and calculating variables within a reality allows for vibrancy in the way this information transposes from what is happening to an understanding of what is happening.

The flexibility -- and deceptively basic simplicity -- of this transposition naturally disposes it(CA) to be adapted to projective exercises such as art, design, and architecture.

Relegating to the Pictorial

Perhaps the Turing machine can be likened (albeit possibly a stretch) to some of the architectural ideals set forth in the 1960s.  Most specifically, the best known work of Superstudio and the conceptualized Continuous Monument, wherein nomadic people could plug in at any point on an immense grid which hyper-consumed major cities, theoretically freeing these people from the fragmentary space of the modernist tower-block, and spontaneously materializing a minimal domestic fantasy life.  This method of providing the means for the complex needs of living by way of a seemingly simple predisposed urban megaplan is interesting in the fact that it breaks down living to its most simple set of needs (the ruleset) and reconfigures it in a spatially succinct realm (the infinitely long tape, or here, the plane which stretches around the Earth).  Similar to the Turing Machine, the Continuous Monument was also very much conceived as a "paper" architecture, and should be viewed as a way of understanding the possibilities of technology instead of so much as a prescriptive model.

Cellular automata act as a diagrammatic model for the output of a predetermined ruleset.  This diagram can be used as a measure for the level of complexity able to be achieved by these rules and needs a further set of criteria applied in order to see the physical effects.

02.26.08 email questions

What is the equivalent of a Turing machine in architecture?

I think it would be difficult to find an equivalent of a Turing machine in architecture. While both processes have a physical output the way they get to that point seems to be different. In a Turing machine there is an established set of limited states and values by which the entire system operates to produce physical output on the tape, when this tape is analyzed it can be seen as the pure result of the procedure of the machine. It is not always evident in architecture how the “result” and “procedure”, and in many cases the procedure acted out is not one of a limited amount of states or values but one of flux where new unanticipated obstacles are implemented. Where as the turning machine runs a procedure without being effect by anything outside the machine architecture, at times has to absorb these outside influences during the course of a procedure.



















The best equivalent to a Turing machine I can see in architecture is the high rise building as the construction and space is produced repetitively up the entire building, each one the same. When occupied these similar cells or spaces will adapt to various functional needs, in a scenario where these functional needs can be seen as analogous to the states of the turning machine the variation could be the result of such a producer playing out.



And in what sense do cellular automata "picture" a state of affairs?

The “picture” created by cellular automata is a result of the rule set it follows. This demonstrates a state of affairs in that the rule set acts upon the line previous to it. Like a precedent study or a virgin site the new line is an advancement forward, some type of improvement upon the next, based on the rule set. In this way cellular automata can illustrate a model of simplification which can help us visualize this process.

False choice.

I’ve spent the week mulling over this. Bluntly, it is neither pictorial nor diagrammatic. The elements of Turing Machines et. al. are information.

We can abstract the output of a CA by agreeing on a convention and call this a diagram. A similar scheme on a universal CA, Turing Machine, or GOL simulation would net a visual appearance nearly as verbose as the original, and therefore would not be terribly diagrammatic. There’s no “meta” message to be pulled out of these systems other than the rule sets.

Nor can I gain much traction in describing even Lindenmayer systems as pictorial. They speak about topological relationships of the plant’s branching, but rarely fool me for a second as to their origin. Additionally, there is some fancy, and I mean _FANCY_, math on the back end to get {{a->a},{b->a,b}} to look like my houseplant. As for CA, come on… It’s just not a picture. It’s not representing anything beyond the information itself.

I feel like the pictorial trap is precisely where so many have gone wrong. These systems provide a rich test bed for theories of morphogenesis, social systems and a dizzying array of other phenomena, but it is often forgotten that these are models of phenomena and not the phenomena itself. The substitution of a model for its phenomena is endemic, and is demonstrated with whip cream on top the philosophy of Nick Bostrom, who believes we are living in a simulation. WTF?! A group of mathematicians has placed the probability at 1 in 20. No joke.

Perhaps this can feed into the other question on the table:

Computation between Geometry and Topology

On the one hand, these systems excite me to a degree that I’m uncomfortable relegating them to a prepositional phrase, yet they’ve enjoyed a grammatical identity in the hands of Noam Chomsky in his research into linguistics. In the interest of completely dorking out this evening I re-checked his Wikipedia entry, and, lo, he’s got this automata theory of formal languages. I read about this a while back and quite honestly couldn’t make any sense of it, but I would guess that Peter’s four food groups would have a place on Chomsky’s table. Turing machines are mentioned by name.

In his “Computational Theory of Morphogenesis”, Przemyslaw Prusinkiewicz, draws the axes of his consideration along three lines.

  1. One or many
  2. Computing capability (a finite automaton or an automaton with counters)
  3. Information exchange with the environment

The last of these certainly involves communication, or this kind of go-between again. In modern agent based programing, the geometry takes the role of the discrete unit, but it seems like a computational grammar serves the role of the information exchange. (Or was that topology). I'm not positive these mix and I need to think about this. More later.




State of Affairs of the cellular automata

Frozen in time; a momentary consequence of the inputs that brought it to its current configuration.

Interact or React

If we view architecture in terms of interface with its own immediate environment, at each step of the creation process (which is generative in one form or another) the next element or condition interacts or reacts to its current condition or that of its adjacent environment. In other words, it has a starting point, moves directionally, reads the context of its adjacency, imprints the space, either changes or retains its state or stops. If particular moves, imprints, states or stops are dictated by a fixed relationship, the architecture is generated by a Turing Machine.

The same could be said for the building process or any assembly of components – whether it be a pattern of bricks or steel structure. None the less, the more interesting proposition concerns intuitive or artful generation of form; is this an unconscious programmed function, perhaps programmed by human experience? In such case, the architect is a TM.

Perhaps there is an analogy to the empirical; reality based in that it is derived from experience or experiment and not derived from theory. Maybe it is the empirical architect who is the TM.

errata: she is always and never the same

#1: Mechanics of The Game of Life
Not having read the Complexificaiton reading last week, I didn’t know that the Schelling’s racial housing preference model was outlined there. Variants on game of life (herein, GOL) models are extremely interesting. The mechanics are barely touched on in the chapter from Casti. The reading is a great overview of the systems but doesn’t serve to differentiate between traditional automata and those which expand on GOL (or substitution systems for that matter).

In GOL, the problem associated with applying an arbitrary word-wrap to output is still on the table, but mitigated by the fact that both the rules and the game board for assume a 2D space. There must have been something in the air in the 1940’s when Mr. Conway was dreaming this up. Scan line television sets had been commercially available for a only a few years, and a patent for a “Cathode Ray Tube Amusement Device”, a precursor to the first videogame, was issued in 1947. GOL gives the appearance of cohesive objects that update in a way that suggests motion, but they are merely tags that are flipped based on evaluations of the surrounding tags. The updating scans just like an old oscilloscope: infinitely but over and over the same field. This is distinct from Turring's machine or Uslam's automata that run over a conceptually infinite tape or column. This introduces a meaningful notion of a neighborhood, where tags update based on the properties of neighboring cells, not just on the history of the simulation. Also of note, is the fact that motion can move "upstream" (kind of), during successive refreshes, although the tendency for glider guns to move down and to the right can be considered an artifact of the scanning.

Casti mentions Schelling’s research from 1971, but even as recently as 1996 a serious study by Epstein and Axtell, affiliated with the Santa Fe Institute, used a variation on GOL to test models of social behavior. Models of social phenomena have actually become much more sophisticated. The Sugarscape model, a customization of GOL represents an individualistic polemic underscored by a Brookings Institution imprint. This is not to say that GOL is wrong, just that it isn't great for modeling society unless you believe that simple rules governing individual behavior give rise larger social phenomena, but do not feed back to the level of the individual...



above: a simulation from Epstein & Axtell's Sugarscape

See: Growing Artificial Societies: Social Science from the Bottom Up

http://books.google.com/books?id=8sXENe8QrmYC&dq=growing+artificial+societies&pg=PP1&ots=HLDOY56HRF&sig=3ECV5_fN9JDaHq1Pti__frNtP94&hl=en

R. Keith Sawyer’s Social Emergence does a fantastic job of surveying the research done in this field so far and situating this within classic problems of sociology.

http://books.google.com/books?id=Hgs007Rd_moC&printsec=frontcover&dq=Social+Emergence&ei=VqrDR_j4BYjcygS-gKGsCA&sig=n71iYvGiMj03Di9JonkOKzRboEk

erratum #2: What’s that in Mr. Wolfram’s pocket?

I thought that the shell question related to Steven’s discussion of allometric scaling of seashells in NKS, but realized the shell he keeps in his pocket is probably a tapestry coneshell, a species that happens to have CA-like patterning on it.



This is not B.S. There are plenty of real-life phenomena that behave just like CA.


Above is a chemical reaction in a petri dish known as the Belousov-Zhabotinsky reaction.

Monday, February 25, 2008

What is the equivalent of a Turing machine in architecture?
And in what sense do cellular automata "picture" a state of affairs?

A Turing Machine in Architecture?

A Turing Machine == a program. Not machine mechanically speaking - hardware - but machine in the abstract sense - software. These 'machines' takes any set of data or inputs (any data set), reads it (if it can) and spits out whatever its supposed to. A program defines what can be read.

What these rules do is basically independent of the circumstances in which they will play out. By circumstances I mean where or when or on what they will be acting on, e.g. the data can be anything as long as it is readable by the rule set. A program has a certain language, and if it encounters 'words' outside of its specific language structure the program will stop functioning. So at the onset, if a program is to be used, the language it uses must be clear of unreadable data. Cellular Automata are a way of picturing such defined arrays of stuff.

Cellular Automata are a way of picturing how rules effect a field of machines over time. One frame represents how the program has reorganized objects' transformable attributes or variables based on their relation to their immediate surroundings. The states of each individual unit in the array have certain possible states, states being the current array of variables.

Objects with series of affectable attributes are placed in a field as an initial condition and all are embedded with a program in order to read and to act. Beforehand the patterns of attributes to be acted on and their corresponding actions are defined. The program begins and if the program see's a certain pattern of variable states within the cell's vision it performs a pre-defined action which reconfigures that pattern.

It is interesting to think how a program "sees". What is it looking at and in what order? Either way it must be systematic.

By running a Cellular Automata, pictures are formed of possible states of the entire system as a whole, while the governing process only happens locally within the space. Certain local states are predicted so as to act upon them, but equilibriums or periodic patterns or chaotic systems can emerge within the whole even though the cells are acting independantly. Cellular Automata seen as a Video, or stills placed in sequence, allows us to see animate behavior. Not necessarily of physical of objects, but the data controlling them.

Is it possible to account for any type of data in a computational Architecture? Programs can be written to accommodate knowable and definable inputs, the most obvious being loads, specifically dead loads, in a structure.

Engineering could be a type of software which reads our often rediculous plans(or more recently parametric models) to spit out revised drawings. The rule-set is based on the need to resist load. A field could be forces in space which must be transfered to a surface (the ground)

Strictly speaking, Structural engineering at this point in time is not a form of computation. It is top down, it does not emerge meaning the rules are applied to a completed form , the rules themselves do not necessarily generate form. A sort of engineering could take place in design if 'rules for maintaining stability' were integrated and let run on a set of building parts.

It's interesting to note that two different data sets can only shoot out the same answer if the program(machine) is different. take the data set [1,1] the program 'add()' spits out two. The same program on a different data set, say [2,1], gives three. but you can get the same answer if you change the program to 'multiply()'.

simulation vs. representation

A diagram as a purely graphical representation of relationships is unfettered to a pictorial or simulative representation of a process. It is by nature a narrow abstraction.

As it applies to cellular automata, the "generated" diagrams are necessarily a construct -- an interpretation -- of the rule set. As stated in Casti's text, referring to an L-System rule set, "This still doesn't look much like a plant. But we can convert strings of this type into a treelike structure by treating the symbols 0 and 1 as line segments while regarding the left and right brackets as branch points." While this method of representation makes much sense and yields unexpected results (in terms of patterning, for instance) another representation could likely yield similarly unexpected, if not more radical, results. This method inherently introduces a geometrical aspect to the CA process that would not exist without diagrammatic intervention. Therefore, this is not a simulation of the code-events but rather a description of its relationships. L-System equations attempt to describe plant growth, and this is only apparent through a diagram, which is drawn to mimic a tree. The diagram could just as easily mimic coral growth, veins of gold, or ice formation, depending on the geometry used to illustrate the system. Nevertheless, any geometry allows this code-event to be introduced into the spatial-material world, which is invaluable for understanding its process. That is, after all, the inherent purpose of a diagram.

As far as pictorial representations are concerned, these generally describe actual objects and actual events in terms of their likenesses. What you see is what you get. As a method of description, it is clearly successful at describing relationships. And while its representation might be skewed by individual perception, it is generally accepted that this is a simulation of real events rather than an abstract interpretation. It is a recounting rather than a birth. It is a snapshot rather than a register. And as such it is limited by the same constraints as the actual objects in terms of space and time.

As it pertains to our ongoing discussions regarding contemporary architectural practice, it seems that the reference -- both the diagram and the pictorial representation -- is somewhat troubling as they are often overly literal. The role of computation exists in a self-reflexive state: generator, recorder, and interpreter. While certainly multifaceted, is this a limited approach to computation as a creative agent?

Monday, February 18, 2008

What does Turing offer?

The Turing Machine offers us an algorithmic realization which begins, like all algorithms, from a mathematic abstraction. In reference to Leibniz, it seems to offer a form of universal verification. By creating a mediating space between our realities and the variegated discourse which occurs as a result of them (in attempts to explore, understand or quantify) these two disparate parts can now communicate. Geometry and topology, while both speak in terms of nature/mind relationships, they do so by memes and can only really exist in abstraction. The Turing Machine allows, via its state in between Nature/Mind or Abstraction/Reality, not only for these 2 sides to interact, and therefore a universal verification (e.g. quantum computing); it also allows for the creation of a 3rd plane: that of the connection itself, which in fact becomes the realization of Eisenman's event, the embodiment of Lynn's vicissitude. In philosophical terms, the universal verification this allows for comes at a cost; the opposed fields of Nature/Mind no long work in reference to a Metaphysic (although no one told Wolfram) , they now reflect the Pragmatism of James or Dewey in the sense that their new ability to communicate has allowed for an emancipation to grow as a patchwork autonomously, incrementally, but not necessarily in reference to one another.

Sunday, February 17, 2008

Week 6: two kinds of algorithm

Here's what I'm asking us to look at:
Casti's book is very good at explaining basic features of Turing's machine, cellular automata, L-systems, and game of life. there are the four basic food groups. they are an extended discussion of what we see in Rocker.
However, i want you to consider in what sense and why they are related to issues of emergence and complexity.
Now, so here we are. We looked at Geometry and Topology as models of meaning. We also looked at what Topology adds (and what is also problematic about its use) to the issues of archtiecture's use of geometry.
What interests me here is what does computation/emergence/complexity add? I don't think we're far enough into the semester to really say, but i'd like to continually give it a shot.

On the two algorithms. The other texts and the websites i've asked you to look at expand on the problem of the algorithm in Leibniz. The one, the algorithm is used in calculus for calculating the rate of change - -so its an expedient. The other, is a system of thinking -- and communicating. And so here we are again back at the issue of the syllogism in a way.

Finally, i want us to look again at Durand and see just how interesting it is to note what is happening in his Precis. these are lectures and material for engineering students that are now being trained as architects -- so there is a pedagogical component. But Hernandez makes it clear that he is underlining an incredible transformation in method.

I don't think we can call it computational, but i think we can call it protocomputational and i'll say why during class. For now, just note the issue of a kind of combinatorial logic.

Tuesday, February 12, 2008

Week 5: Syllogism and algorithm

I pointed out a few things about Aristotle's Categories: The generalization of subject and predicate and the question of what can be said about what. This was one important move. Rather than itemize what we have in the universe in terms of individual identities, or endless categories, we search for a simple terminology one that economically exhausts the notion of identity and inherence without having to list everything. But note that in the Categories we are really talking about the nature of propositions - literally what can be said about what. The syllogism is different. Subject and predicate now have almost algebraic values -- replaceable by whatever. The syllogism connects the universal to the predicate and back to a universal (in a new relationship) showing the inherence of the universal in the subject. Again, this is distinct from Plato. It is, moreover, an incredible invention of which there are 258 or so variations (we discussed later the relation between this and two term 1D cellular automata) some of which are valid, some of which are not (just like interesting patterns and homogeneous patterns in two term 1D cellular automata.

The other thing i discussed was the question of a model of meaning -- again. And i will continue to discuss it throughout. I remarked on the consistency of the topological model of meaning in Lynn, Eisenman, Ben van Berkel, and others. The point was: just as the sphere represented a geometrical ideality in Boullee, the manifold or the intensive surface consitutes a figure of topological thinking. . . . but one that seems reducible to an image of that figure. It is not entirely so in all cases -- nonetheless Cecil's use of Seifert surfaces for the UN Studio Arnheim transfer station, their topological diagrams as well as the global topological image, FOA's folding and unfolding bands, Eisenman's folds: all these seem to project a figure that is the pictorial geometrical object of topology or catastrophe.

What was different between this and a straightforward platonic or Euclidean geoemtrical model is that the mathematics in the first case point to an ontology of events rather than things. This was the silly demonstration with the water bottle. I'm just glad the cap was on.

In other words, i was trying to explain how the model of meaning shifted from thing to event. Again, the references are to calculus (first mathematics to quantify events), topology (thinking space, form, and continuity in terms that aren't possible with geometry), and catastrophe theory (which is a hybrid of calculus and topology). (Keep in mind that one of Thom's basic questions is really about modelling phenomena -- but specifically, how to quantify qualitative transformations, like phase change.)

With the subject of this afternoon we looked at Woolfram and his description of cellular automata and i wanted to get a basic feeling for what this idea consists of. But one of the main points in that discussion is of course the interest in complexity and emergent behavior. There is and will be a lot more to say about this. One point is that historically it has been impossible for philosophy to deal with this problem. In Metaphysics Aristotle calls this the accidental, for which he says there can be "no science." Cleary Wolfram is arguing that indeed this simple functions and our ability to see their operations points to a new kind of science. But as Matt pointed out, we have to let the programs run.

Ok, this also brings to discussion the Turing text -- one of the points i wanted to make here is that the very notion that this was a thought experiment about the decidability of any statement about numbers is such an incredible fucking thing. it isn't a computer, it is the idea of this process which hardly even seems mathematical. Again, the cross between logic and mathematics here seems important. Both Leibniz and later Frege were interested in this idea. If there is one thing that I am interested in it is not just the application of the results of computation for architecture -- ie the spinoff results of Wolfram et al, but rather the thought experiment itself. That is why i was asking for an analogy. I wanted to get at that in architecture. Maybe, though I'm not sure, that would mean that we look at architecture kind of like the Entscheidungsproblem in math. hmmm

The Rocker discussion was really about the problem of taking computation, cellular automata, and the other stuff in such a literal kind of mapping way. The spatialization of that information is neither a diagram nor an internal geometrical operation to the geometry of the images she is showing. Rather, it seems, it is a pictorial mapping -- which can certainly become something else, and so there is nothing intrinsically wrong with, but it seems to be a kind of red herring.

This is why i also pointed out in Wolfram's images, the strange idea that if we lay the series one after another of each iteration then we get the image of complexity and it seems that it isn't part of the computational function or a rule in the cellular automata that you take the first iteration and the below show the second and so on. When we see the image of the shell that Wolfram carries with him, we feel as though we have a complete correspondence between computation or cellular automata and Nature. hmmm

Cellular Automata

The term 'behavior' in world of organisms refers to actions and/or reactions in relation to the environment. The complexity of interactions involved in the nature are somehow weaved by the complexity of the response system (nervous system) of the individual organism with an enormous pool of logics that direct one to act in certain way and adjust one's behavior from the learning process in order to continuously fulfill its goal(s), such as survival.

Although the Cellular Automata hasn't gotten sophisticated enough to adjust the rules (that govern its 'behavior') by itself through learning, the interesting idea demonstrated in this simple program is how complexity grows over time when it's allowed for considerable number of cycles(steps). For complexity to emerge in cellular automata, the previous step (the completed row of cells filled with either black or white) has to provide the 'environment' with certain degree of randomness that stimulates the new step to respond.

How well the new steps can create the complexity of the pattern within the rigid orthogonal grid would depend on the characteristics of rules which can prescribe either increasing (by diversification) or reducing (by generalization) complexity.

Indeed, Cellular Automata can operate without the visual representation in the form of the grid. However, its geometrical aspects can only be seen if the data is presented(interpreted) in graphical ways. The reason why I think it is an interpretation is that the pattern in its original form (algorithm) can actually been understood as completely different thing if one tries to print out the numerical operations and compares it with the grid representation. Most importantly, the predetermined geometry of the cells (squares on 2D grid) frames our understanding to the cellular automata to orthogonal system only. I wonder what would happen to the visual quality if vertical lines in the grid become diagonal and the cellular operation transforms from 2D to 3D.

scales

Works in a series of scales. simple ---> complex

Cellular automata displays a series of scales, the more infinite it becomes the more variables are added. As new states are added the automata becomes more complex.

The most basic gives 2 states per cell, and a cell’s neighbors defined to be the adjacent cells on either side of it. A cell and its 2 neighbors form a neighborhood of 3 cells. So there would be 2^3=8 possible patterns for a neighborhood.
With each rule new generations are created

What would happen if it weren’t based on a grid system?

Automata-Turing-Cellular: On/Off

Human understanding/action is a function of sensory perception. Sensory perception arises from electrochemical activity - in most cases on or off (not quite sure if it is analog or digital). The brain is both hard wired and emergent. We are both Turing Machines and Cellular Automata.

All circular functions diminish until they lose symmetry - at which time they either reach the tripping point or create one.

Cellular Automata’s geometrical aspects:

Cellular automata can build geometry through simple or complex patterns and repetitions. Rules specifiy relationships of lines/curves/angles and how they can change positions or orientations. The sequence can become so complicated that its outcome can’t be predicted. Simple shapes are displaced and repeated in different ways to develop new geometrical forms that wouldn’t be possible by hand, or wouldn’t be obtainable though a regular/identifiable pattern. Although, if a complex sequence was run long enough couln't there be a point when every sequence begins to become a recognizable pattern?

questions from email - Turing machine/Cellular Automata

Question 1: Analogy of a Turing machine?

Any device which can perform a simile rule set would seem to be an analogy for a Turing’s machine. Computer programs are particularly close because they illustrate a series of operations which are dependent on a group of variables controlled by a user which can provide an unpredicted input into the system causing certain operations or states to happen, and when they are of reasonable complexity they seem to be able to validate ideas or concepts we could not otherwise understand because of their complexity. So in the same way Turing’s machine tried to answer Entscheidungsproblem by building up from a simple series or logical operations, modern computer programs try to answer complex geometrical and logical problems by building up from a series or simple logical operations.

Question 2 Geometrical Aspects of Cellular Automata?

The ability of cellular automata to produce logical understandable patterns, as well as ones of apparent randomness demonstrates that both can be produced from the same simple system. This challenges the idea that randomness can not be logically produced or created, moving the term “random” from something that denotes a process as well as a formation or geometry into a term which can only articulate a product like geometry. With the ends removed logically from its origin, geometry now illustrates something more complicated than a mere representation of logic.

turing automata

1. There seems to be an analogy to Turing’s machine on a very abstract scale found in evolution. Circular machines reach a finite point, or a wall at which there is no possible next move. Similarly, however, over a longer period of time and perhaps without such concrete results evolution renders a ‘circular’ species extinct. The vast majority of species are ‘circle-free’. The ‘circle-free’ species are influenced by the immediately (relative time wise) preceding configuration. For example, since humans no longer have a rough diet of tough to digest plant material, humans no longer need the appendix to aid in our digestion. The ‘0’ or blank space in our code has been erased and our code has been transformed into one that is more compact and efficient.

2. Cellular automata have no geometrical aspects. They can be assigned to run on any type of geometry with the same sets of rules.

Friday, February 8, 2008

Week 3: Models of meaning continued

Again, we need to remember that the issue is how geometry stands as a model of meaning -- in fact how mathematics stand as a model of meaning. We are looking now at topology.
In terms of our discussions we want to recognize Lynn's as well as Balmond's, van Berkel's, and Eisenman's contrast of topology with geometry, Cartesianism, and stasis. The question is what is the model of meaning given to us through topology? Well there are a number of things, but one of the most critical is that it shifts the ontology of archtiecture from a thing to an event. Look at the essays again and note the terms they use in relation to topology.

Impossible to Avoid Geometry?

We should ask: what are some of the basic features, basic operations, that are internal to architecture?

By which I mean, when we are designing, are there specific things that we can’t avoid? – like drawing lines.

I would like to make a very simple proposition which is this: we can’t seem to avoid geometry. Now this might mean different things in different contexts. And so maybe there isn’t an ultimate foundation from which we could say, e.g., “This is how geometry is essential to architecture.” But at the very least, when we do something in architecture, that something seems to imply or involve geometry.

Geometry Lacking Space

(Let’s not get into this, just yet: Descartes development of an algebraic system for Geometry – geometry with symbols rather than figures. Now space has a metric, discrete and continuous, the grid comes later, but the coordinate system is the basis – whereas in Euclid, it is just continuous, metric, but continuous. Somewhere Panofsky writes about the problem of the infinite in the development of perspective)

By having you look at Euclid’s definitions, postulates, and common notions, I wasn’t considering that this would give us a foundation for geometry for architecture but rather, in its economy, we could look at the way in which geometry is defined from within mathematics. And so, when we consider the elements we are considering how they relate one to the other and so on and further that when we read them in a particular order they seem to be defined from the most simple elements to the most complex – that is, point, line, and plane, to metric relations between them, to figures and metric properties of those basic figures.

But notice that in the 23rd definition, Euclid seems to be saying something-- for the first time-- about geometry’s relation to space. One could say that he is already talking about space in the previous definitions. I’m not so sure. For one just because he is talking about the metric properties of figures, or the relationship between points and lines and planes, it doesn’t necessarily follow that he is pointing to geometry’s relation to space in general. Rather, it seems that whatever one constructs as ‘geometrical’ has these properties – for good. But in the 23rd definition, it seems as now those properties might also hold good in some fashion for something beyond the geometrical figure or element as such. By which I mean this: up until the 23rd definition we have something that counts as a general property for a geometrical thing – but it hasn’t yet been said what the space is around that thing only what that thing is as such. In other words: does geometry as such say anything about the nature of space (as opposed to geometrical figures)? If it does, it is ambiguous – the parallel definition simply says not what space is, but one of the conditions of space that holds good, for good. If two lines are intersected by a third and the interior angles are the same, then those two lines will never meet.

The fifth postulate, by contrast, is a demonstration that if those interior angles are not equal, those lines will meet on the side where the interior angle is less than 90.

[If you want to know something more about the history of his problem, see Bernard Cache, “A Plea for Euclid” on the server. The essay, among other things, is a critique of architect’s indiscriminant discussion of topology and digital design]

One of the points of this discussion is this: it is possible to talk about consistency in regards to geometrical propositions qua geometrical things, but not so easy at least to talk about space. Another point is to clarify for ourselves what we mean when we say that architecture is in some capacity always tied to geometrical propositions. That is maybe a more difficult discussion. In either case, let’s at least assume that without geometry, it is difficult to perform an architectural operation. But let’s also admit that an architectural proposition is not necessarily a geometrical one, and vice versa. The last point is that insofar as architecture needs geometry, then in some sense architecture is architecture insofar as it needs geometry. Without it, perhaps, it isn’t architecture. Perhaps.

Architecture, we might say, is always bound up with problems of spatial relations and it seems, or did seem for some time, that geometry was the only field of mathematics that seemed capable of providing general laws for the features of spatial organization (Descarte’s invention is still a continuation of geometry).

Thinking about space and continuity in other terms: Topology

Another field that became relevant recently was topology (for a history of topology see http://www-history.mcs.st-and.ac.uk/HistTopics/Topology_in_mathematics.html) . There are early indirect examples of this: Christopher Alexander used topological diagrams for his discussion of pattern languages. Hannes Meyer introduced a reconfiguration of topological urban space. Le Corbusier used a topological system to hybridize infrastructure (a sidewalk) and architecture in the Carpenter Center at Harvard. Baudrillard discussed the Centre Georges Pompidou by Foster and Piano as a topological system. Frederick Kiesler used a topological system in his Endless House, which he also discussed in his book Endless Space. (http://www.surrealismcentre.ac.uk/images/KIESLER.jpg and http://en.wikipedia.org/wiki/Frederick_Kiesler).

More recently, however, topology became a special field of interest especially during the 90s with the development of digital design (which, in general, really changed the formal domain of geometrical thinking in architecture through a new kind of plasticity, some of which was tied to animation techniques, others of which were related to the utilization of advanced geometrical control with nurbs curves and surfaces – Chu has I think correctly identified this as morpho-dynamism). Peter Eisenman, among others, invoked topology as a new way of thinking about spatial relations, and this is what should interest us (see for instance his essay on Rebstock on the server—topology is invoked indirectly through the reference to Rene Thom’s catastrophe theory, which is a combination of calculus and topology, the same way Descarte’s coordinate system is a combination of algebra and geometry). Jeff Kipnis suggested that topology was a way of removing the ideality of geometry – by which he meant, it was a means for rethinking architecture away from the traditional figure/ground relation. This is also what Eisenman had in mind and he and Gregg Lynn pioneered a new attitude toward space and geometry by introducing, thanks to John Rajchman, Gilles Deleuze’s discussion of the fold in A Thousand Plateaus.

Let’s now point out a few things about topology. First, topology is a distinct field of mathematics that emerged in the 18th century under the work of Leonard Euler. He produced at least two major insights into problems of spatial relation that significantly departed from geometrical concepts or spatial relation (for a history of this see: http://www-history.mcs.st-and.ac.uk/HistTopics/Topology_in_mathematics.html)

Definitions: topology “ignores individual differences among, say, figures bounded by closed curves , and treats them as a group that have certain invariants in common.” Barr 151. One way to get around this vagueness is to understand that topology is not as concerned with the specifics of a figure, but rather a mathematical logic that will relate an entire set of figures – and here the key word is set. “topology aims at the invariant in things; the the things have to be referred to somehow, if very genrally an, and the best way to referto things in this way, and yet retain the kidn of relationship that exist with topolotical invariants, is by treating them in groups, or sets. 163 Barr

(Important to note – see also the grammatical background to this issue in Euler who developed the formula for topological invariant given any polyhedron: Imre Lakatosh in Proofs and Refutations notes in a footnote that Euler made this discovery when he changed the terms that defined polyhedral from vertices lines, and faces to vertices, edges (acis), and faces.)

Topology as a different way of discussing space

“As a rule topologists confine the use of the word “continuous to processes, rather than spaces – a line is a 1-dimensional space – but if we want to use it for a line, then continuity relates the set of all the points on the line to the set of all the real numbers.” Barr 152.

What I would like to say is that topology offers a different way of talking about the properties of figures and of space. “In one sense it is the study of continuity: beginning with the continuity of space, or shapes, it generalizes, and then by analogy leads into other kinds of cointuity – and space as we usually understand it is left far behind.” And further: “A topologist is interested in those poerties of a thing that, while they are in a sense geometrical, are the most permanent – the ones that will surive disotrion and stretching.” And so from this point of view, one could also suggested that topology is interested in the kinds of and quality of connectedness. It may not seem important, but it not trivial that the two circles on a sphere, if they intersect, will have to do so twice, while on a torus, they only have to do so once. There is a topological distinction between a torus and a sphere, despite the fact that their surfaces both seem singular.

Another feature of topology that interests us is of course networks. But maybe the most important is that topological investigations leads to new kinds of surfaces and spatial organizations that we can’t consider or generate through geometrical thinking. Examples include the mobius strip and the Klein bottle. See, in particular chapter 2, new surfaces. And so also, with these new surfaces, new spaces and thus new spatial relations.

It should become a bit more clear now that when we are introducing the question of geometry and the question of topology in relation to architecture, we are looking at two different systems that are about spatial organization and where geometry seems limited in one sense, topology has advantages that allow us to think space in other terms that are equally meaningful, though the nature of precision is somewhat difficult and vague and this is perhaps because we keep trying to think of topology in geometrical terms.

Week 4: The Syllogism, Gothic Architecture, and other mattters

We look first at a fairly loose act, or at least a gesture which we may be inclined towards but maybe lacking in a certain precision. We categorize. We can do this for any number of reasons. Bioilogy strikes us as a common example. We categorize animals and in fact we know that the system of categorization or classification has shifted over centuries. Aristotles Categories however are intended to be far more penetrating than that, at least presumable, in so far as they penetrate to the limit of what can be said about what. That is, it tends toward exhaustion. And specifically what can be predicated in its greatest generality of something particular, a subject. In this gesture Aristotle at once initiates an entirely new standard of systematic thought and severs his metaphysic from Plato. Whereas for Plato considered the realm of Universals (Forms) as separate and distinc from particulars, in Aristotle Universals participate in particulars. (Plato's contribution we recall is to distinguish the world as such from the universe, one of the implications being that . . ?). Simple as it may sound, the distinction between subject andpredicate will provide us perhaps the first insight into a systematic terminology that allows for logical manipulation. It is the terminological reduction that is important. For the content can now be whatever and the terms can glide across what exists and what is the case of what exists relating, pairing, disti guishing, contrasting and so on. But as for the rules by which we can do this, we are not completely there yet. The syllogism describes a systematic means, a mechanism, a process, a set of rules, or rather just is all these things, by which we can exhaust the validity of inferences about the relationship between particulars and universals. It is not a picture of the world, it is not an analogy, but rather . . . Hmmmm.

Was an intersting point that someone made about google and emergence and whether or not the algorithm could be said to underlie the temple, not onlyu the gothicv building.

See also explicitness of argument and process of construction - in addition the relation between part and whole. Notice that, although Panofsky speaks of geometry, the model of meaning to which he aspires is that of logical argument. See also emergence.

Ok, these are some primitive notes, and I'll formalize them more later. There is also a lingering issue about the part whole stuff, by which I mean a difference between part/whole in geometry and part whole in the syllogism (?). I mean: Panofsky says a lot about geometry, but I can't remember where he speaks of it as a founding metaphor or even as a model of meaning. But, we'd have to go back and see.