For my Visualizations class the first project was to use screen scraping to get some set of usable data and make a very simple visualization using Many Eyes. I chose to scrape the Archinect Salary Poll for unknown, probably masochistic reasons. The data can be found in its very messy raw state here, and a simple scatter plot with substantially cleaner (but US only) data can be found here, or can be seen below. And yes, I doubt those 23 year old six figure incomes as well. I really wanted to get different colors for men and women, but unfortunately the software doesn’t do that. If someone wants to come up with a better looking plot have a blast.

http://www-958.ibm.com/me/visualizations/cb450de2333011e08a94000255111976/comments/cb4a5874333011e08a94000255111976.js?width=425&height=350

Well, it’s official – my team project (with Arseni Zeitsev, Dena Molnar and Otto Ng) has been selected to be part of the MIT 150th anniversary celebration! The installation, similar to our project for Augmented Architecture last semester, will involve a large flexible grid of LED lights that respond interactively. I’ll keep you posted as to the progress…

Spoiler alert – in the next few months, you’re going to see a lot of noise from my corner about visualization, user experience, and games, as this is increasingly the concern of my thesis. Put very briefly, the current state of digital design environments are poorly situated to deal with both the overload of contextual and internal data involved in a project, and the interplay between flexibility and specificity that is necessary to explore the intersections and opportunities in that data. We have tools that can look at data (mostly with three-letter acronyms), and we have tools that are friendly to design (usually with fun, clever names), but very little that allows a bridge between the two. Autodesk is attempting to go a bit down this road with Vasari (as I mentioned a few weeks ago) and I feel that other, similar tools are not far off.

These kinds of tools, with immediate (hopefully real-time) feedback, clear metrics, and open interfaces, actually resemble games more than drawing or modeling interfaces. Actually, if you go by Chris Crawford’s definition, they are more accurately called toys or puzzles. This kind of interface is becoming increasingly familiar, whether it is an interactive graphic for the NYTimes or some kind of physics or geometry based puzzle. Many of these games (Tetris, for example) can actually be seen as a kind of human-based optimization algorithm, where people’s natural curiosity and competitiveness combines with an entertaining interface to incentivise the solving of a certain problem. Underscoring this trend would be Autodesk’s new game, Tinkerbox, for the iPad, which is a physics-based puzzle/toy/game (that appears to include some fancy stuff like linkages and inverse kinematics). It only takes a little creativity to imagine how this sort of interface could get applied to solving problems with bearing on real life:

The application of games to “real world” tasks has already been achieved in fold.it, where protein folding (a complicated geometrical problem that is difficult to solve computationally) has been turned into a game – like some sort of massively complex combination of tetris and a rubik’s cube:

This has been lauded as the “crowdsourcing” of science, but I think that’s actually pretty inaccurate. Crowdsourcing to me implies that they are tapping into a latent resource, but what is actually happening is much more clever. The first thing you have to do after installing fold.it is to go through a series of training tutorials – a common task at the beginning of every game. This particular set of practice games introduces you to the rules of protein folding, as well as the very clever interface (which I will cover in a later post). The game requires not only a fierce competitiveness, but also substantial three-dimensional visualization skills. I would argue that what is happening here is not crowdsourcing (which has actually been attempted with some success with Rosetta@home) but rather a search for people with the particular skills for this task, and a tool to allow them to show off their skills (and help cancer research in the process). In fact, in this Wired article it’s pretty clear that that was their goal from the start – the makers outright claim that they are “looking for prodigies.” What this highlights is that a well-designed game interface can allow for an incredible symbiosis between user and computer to produce results that would be impossible without such dedicated nuanced communication both ways. It’s not crowdsourcing- it’s some incredible new combination of translation and HR.

I have two conclusions to draw from all of this. One is that there is a whole world of puzzles that can draw from physical reality that haven’t been tried yet. What about a game based upon spring models? Or reticulation? Or thermodynamics? I’m no expert, so some of this probably has been tried, but it seems to be a wide field begging for exploration.

The other conclusion is that game environments are actually ideally suited to the design process. They provide an open-ended way to search through very complex and unpredictable problem domains, while allowing the user at every point to balance between quantitative and qualitative values. And while using some sort of “click and wait” or optimization process divorces the user from the problem, gaming your way to a solution keeps the user in constant direct interaction with the problem at hand.

It seems to me that in a few years, interacting with a model by moving sliders or modifying a matrix or table are going to seem hopelessly antiquated and slow, not to mention horrifically boring. It’s time to get physics, geometry, and the rest of the things we’re trying to compute out from behind the screen and poke at them in realtime, and see how they poke back.

A quick look into the future: here are some screenshots of some material density maps I made today playing around with topostruct, a tool made by Panagiotis Michalatos and Sawako Kaijima (they have also made other structural analysis and design tools that you should check out – educational versions are available on their website). I made these with a crowd of other GSD students, and we were all having a blast. Topology optimization is not a priori a thrilling topic, but the way this tool is put together makes it engaging. I, for one, am looking forward to a future where structural analysis can be as intuitive and innately enjoyable as sketching.

Computational designers in architecture (and grasshopper dilettantes such as myself) love to (over)use genetic algorithms in everyday work. Genetic algorithms (or GAs, as the cool kids call them) are a particularly fancy method for optimization that work as a kind of analogy to the genetic process in real life. The parameters you’re optimizing for get put into a kind of simulated chromosome and then a series of generated genotypes slowly evolve into something that more closely fits the solution you’re looking for, with simulated crossover and mutation to help make sure you’re getting closer to a global optimum than a local one.

For those that don’t regularly optimize (I know I should more often, but it’s so much easier to just sit on the couch and vegetate), the imagery that gets used is of a “fitness landscape” where you’re looking for the highest peak or the lowest valley, which represents the best solution to a problem:

Often, however, you’re talking about a landscape with many dimensions, with lots of local optima – the lower peaks in the image above – that can seem like the best solution when they are not. Searching the entire problem domain takes far too long, and so this is when your optimization algorithm takes over – it uses a series of rules to try and search intelligently for the best (or at least close to the best) solution.

Genetic algorithms tend to get used a lot by architecture students, despite being fairly controversial in mathematics. A lot of this controversy has to do with how good GAs are at finding the actual optimal solution in a certain amount of time, versus selecting or writing another algorithm that works more well on that specific kind of problem. I think they are popular for the following reasons:

A. Design problems often involve dozens of variables, and GAs are well-suited to complex fitness landscapes.

B. Architects are often more interested in a very good solution rather than the best, and GAs can find a very good solution with very quick setup.

C. Architects are less interested in the runtime of the algorithm, so “faster” methods that aren’t quite as robust or optimal are not necessary.

D. GAs work well on a wide range of problems, which architects interpret to mean “work great on all problems” for the reasons above.

E. “Genetic algorithm” sounds really cool, particularly in a presentation or lecture.

F. Grasshopper has a built in GA solver called Galapagos that works really well and looks cool.

As it goes, I don’t really have problem with any of this. Architects are not mathematicians, so it’s great that we have a fairly robust, flexible method for optimization that can get us better solutions than we can think up by staring at the screen. I’m not going to complain.

Where I was trying to get with all of this is that when I read the post today on BLDGBLOG one of the things that popped into my head eventually is that a lot of what design does is similar to a genetic algorithm. Bear with me here…

The post linked above shows two projects by Lik San Chan that, loosely defined, are architectural narrative pieces about food and space. They are really interesting pieces of speculative urbanism, and you should check them out. However, in Geoff’s write up he mentions

…it’s one thing to create, analyze, or even editorially promote architectural projects as narrative ideas—that is, as scenario plans for future landscapes—but it’s another thing to look at whether or not such proposals do, in fact, operate successfully as solutions to the problems they highlight.

To which I responded in the comments,

Geoff, I love your blog, and I really really enjoy diving into projects such as these, but I have to say that I’m skeptical that using the harsh light of quantitative analysis on either of these proposals would really be a good idea. The tempelhof project has some obvious sociopolitical issues (as well as some probable agricultural ones), but even the smaller-scale fish market seems to have more to do with the Bartlett aesthetic and a romantic fictionalization of urbanism and light industry than it does with a real proposal for social change. You’ve argued very eloquently in the past for a place for architectural fiction. Suggesting that such fiction should be held up to rational standards would, I fear, not be kind to the work presented.

Don’t get me wrong: I would like to see both of the above projects re-imagined as real proposals. However, such an exercise would most likely involve an interdisciplinary team, a lot of effort, and the result would be much less likely to lend itself to viral blog dissemination.

As is usual in blog commenting, immediately after clicking “post” I thought of another thing I wanted to say to clarify what I had just written. I’m not going to double-post on someone else’s blog, however, as that would identify me correctly as a long-winded blowhard. However, I have my own forum for long-winded blowhardery here, so here it goes:

I can’t help but feel that a lot of design that takes a fictional or narrative slant is valuable precisely because it doesn’t attempt to quantitatively define the solution from the outset. To use an analogy to optimization and genetic algorithms (that I have thoughtfully provided exposition for above), the kind of freewheeling, speculative work that you see on BLDGBLOG provides a kind of free-associative mutation that help find globally optimal solutions to global problems (see what I did there). By definition, a lot of these projects are going to be completely nonrational and impossible, because if rationality was the prime directive we’d simply end up with a solution that was almost identical to the status quo.

It is absolutely vital to design that we simultaneously promote both speculative/narrative and optimal/performative approaches to architecture, as they are both vitally necessary to exploring the incredibly complex domain of reality.

…also known as math doodling.

I would like to point out that the majority of computationally minded architects (myself included) really don’t move that far beyond the surface presented in the videos linked above, or as Robert Woodbury would put it, “visual construction trumps mathematical inference for design.” Many thesis projects are really just (slightly) more complex and better-drawn versions of the infinite elephants.

But more seriously, this kind of “math doodling” (which is, if you watch the videos, obviously self-deprecating) should be seen as a valid way analogue for the way mathematics are explored in architectural design. I’m not saying that the deeper implications of the form are non-important, but to a designer they are only important in that they may point the way to a better or different implementation of the concept at play. What I find most energizing about Vi Hart’s videos is that they suggest that not only is this kind of visual understanding more fun, but that it is an end unto itself – you don’t need to sketch snakes just as a hook to understanding graph and knot theory (and how they interrelate) but also because it’s a valid use of the math itself. It’s non-abstract and non-arbitrary, which is pretty damn cool for math.

Wow, six months away from BIM and Autodesk drops something awesome while I’m not looking. In case you’re as clueless as I am, the labs released a free demo of their new project Vasari for preview and experimentation. Vasari is basically an enhanced, lightweight version of the conceptual massing environment in Revit, with some energy and carbon analysis tools built in. Think of it as Revit’s version of Sketchup. What this description belies, however, is the fact that all of the CME tools are there for you to play with, including reporting parameters and adaptive components. In addition, models created in Vasari play well with the full version of Revit. Which means one hell of a fun toy.

Someone at Autodesk must be whispering to the right people. A design process, whether digital or physical, thrives in an environment that is responsive, quick, flexible, and robust. Feature depth and breadth are fantastic, but when sketching a long feature list can make things complex and laggy, which can hurt more than help. Not to mention that in a larger office many of the people that will be doing conceptual massing won’t even care about half the categories or tools in Revit. Part of the reason that Rhino + pluginofchoice is a preferred tool in architecture schools across the country is that it is omnivorous, fast, and simple out of the box, with an enormous library of tools you can add on demand. It is very good at getting out of the way when you just need to get something done. Now, Revit will never be Rhino (and Rhino will never be Revit) as BIM has demands for object and model rigor that most surface modelers short of CATIA can’t really handle. But having tools like Vasari, with a more directed (and playful) environment go a long way towards making BIM more accessible to a design mindset and methodology.

Below is a demo video from labs themsel(ves?) The audio sounds a bit like the Home Shopping Network to me, but you’ll get a pretty good idea of what they’re getting at.

Below is my final research document for my New Frameworks in Design class taught by Paul Nakazawa. This was a relatively free-form professional practice class aimed at exploring new areas at the edges of practice. My project involved a series of interviews with a wide array of practitioners dealing with new areas in digital and/or computational design. I packaged them into a series of questions or issues to create somewhat of architectural layperson’s guide to the current relevant issues in computational practice.

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And with this, I am free to… work on my thesis.

My CAD/CAM final project (done in a team with Daniel Elmore and Frances Haugen) was an experiment in curved folding in plastic. We used curved origami (a really complex and interesting field) as a method to make flat-pack “bricks” that could have variable openings and be assembled in a relatively simple fashion with hidden fasteners (we ended up using brads.) The curved folds give the bricks stiffness and form. We used laser-cut 1/32″ translucent polypropylene. The material is strong, cheap, and forms a incredibly durable “living hinge” when scored. The form and cutfiles were generated using Rhino, with the help of Grasshopper and some C#.

Looking at it now I really want to combine it with the electronics work I’ve been doing in Augmented – the plastic is an awesome light diffuser and this would be an easy way to make a 5’x8′ led pixel display…

First of all, I’ve mirrored my Bomb Defender Game at a new, more permanent location. You can find it here.

I also have another applet that I created for my Augmented Environments class that demos a lighting effect we’re trying to recreate physically with a grid of LEDs – I’ll be updating here frequently if we happen to get the grant we applied for to realize the project. In the meantime, you can click on a screen.

I’ve got another post coming up soon with my final CAD/CAM project, and even more in the future with thesis ideas – now that the semester is over I need something to keep me busy.