five times a

analyzing architecture at any angle

/5xa/ Graphics made with six axis industrial robot

Recently we took part in the exhibition called six.axis.made curtesy of Koło Imago.

Also we have created our website –



/txt/ The Feynman-Tufte Principle

A visual display of data should be simple enough to fit on the side of a van
By Michael Shermer on April 1, 2005


I had long wanted to meet Edward R. Tufte–the man the New York Times called “the da Vinci of data” because of his concisely written and artfully illustrated books on the visual display of data–and invite him to speak at the Skeptics Society science lecture series that I host at the California Institute of Technology. Tufte is one of the world’s leading experts on a core tool of skepticism: how to see through information obfuscation.
But how could we afford someone of his stature? “My honorarium,” he told me, “is to see Feynman’s van.”

Richard Feynman, the late Caltech physicist, is famous for working on the atomic bomb, winning a Nobel Prize in Physics, cracking safes, playing drums and driving a 1975 Dodge Maxivan adorned with squiggly lines on the side panels. Most people who saw it gazed in puzzlement, but once in a while someone would ask the driver why he had Feynman diagrams all over his van, only to be told, “Because I’m Richard Feynman!”

Feynman diagrams are simplified visual representations of the very complex world of quantum electrodynamics (QED), in which particles of light called photons are depicted by wavy lines, negatively charged electrons are depicted by straight or curved nonwavy lines, and line junctions show electrons emitting or absorbing a photon. In the diagram on the back door of the van, seen in the photograph above with Tufte, time flows from bottom to top. The pair of electrons (the straight lines) are moving toward each other. When the left-hand electron emits a photon (wavy-line junction), that negatively charged particle is deflected outward left; the right-hand electron reabsorbs the photon, causing it to deflect outward right.

Feynman diagrams are the embodiment of what Tufte teaches about analytical design: “Good displays of data help to reveal knowledge relevant to understanding mechanism, process and dynamics, cause and effect.” We see the unthinkable and think the unseeable. “Visual representations of evidence should be governed by principles of reasoning about quantitative evidence. Clear and precise seeing becomes as one with clear and precise thinking.”
The master of clear and precise thinking meets the master of clear and precise seeing in what I call the Feynman-Tufte Principle: a visual display of data should be simple enough to fit on the side of a van.

As Tufte poignantly demonstrated in his analysis of the space shuttle Challenger disaster, despite the 13 charts prepared for NASA by Thiokol (the makers of the solid-rocket booster that blew up), they failed to communicate the link between cool temperature and O-ring damage on earlier flights. The loss of the Columbia, Tufte believes, was directly related to “a PowerPoint festival of bureaucratic hyperrationalism” in which a single slide contained six different levels of hierarchy (chapters and subheads), thereby obfuscating the conclusion that damage to the left wing might have been significant. In his 1970 classic work The Feynman Lectures on Physics, Feynman covered all of physics–from celestial mechanics to quantum electrodynamics–with only two levels of hierarchy.
Tufte codified the design process into six principles: “(1) documenting the sources and characteristics of the data, (2) insistently enforcing appropriate comparisons, (3) demonstrating mechanisms of cause and effect, (4) expressing those mechanisms quantitatively, (5) recognizing the inherently multivariate nature of analytic problems, (6) inspecting and evaluating alternative explanations.” In brief, “information displays should be documentary, comparative, causal and explanatory, quantified, multivariate, exploratory, skeptical.”

Skeptical. How fitting for this column, opus 50 for me, because when I asked Tufte to summarize the goal of his work, he said, “Simple design, intense content.” Because we all need a mark at which to aim (one meaning of “skeptic”), “simple design, intense content” is a sound objective for this series.




/vid/ Aalto Talk with Linus Torvalds [Full-length]





/vid/ Rem Koolhaas: The reason I became an architect (Jan. 14, 2016) | Charlie Rose





/vid/ Manuel De Landa. Metaphysics As Ontology: Aristotle and Deleuze’s Realism. 2011






/txt/ This MIT website will tell you how memorable your photos are using artificial intelligence

You know a memorable photo when you see one, but now so does a new artificial intelligence (AI) system called LaMem.

MIT’s website lets you upload your photos to try out the algorithm, which we first saw over at Discover Magazine.

To create LaMem, the researchers showed a random set of 60,000 images to users of Amazon’s Mechanical Turk site.

Read the rest of this entry »

/txt/ The buildings of the future will keep rearranging themselves

One of the great laboratories of the future and its side-effects is science fiction. ‘The wall flickered partially out of existence as he stepped through to the corridor,’ wrote Arthur C Clarke in his novel The City and the Stars (1956), ‘and its polarised molecules resisted his passage like a feeble wind blowing against his face.’

Typical of the symbiotic relationship between science fiction and fact, Clarke seems to have got this idea from the physicist Richard Feynman, who in 1945 predicted the possibility of molecular engineering. Feynman argued that any material could one day be constructed from the atom up – and, moreover, that complex miniscule mechanisms (‘nanobots’) would become viable, ‘a billion tiny factories, models of each other, which are manufacturing simultaneously’. It was conceivable not just that concrete, for example, would be strengthened with polymers but that it could come to resemble a living substance, mutating on demand. And then where would that leave architecture? Read the rest of this entry »

/txt/ How to Have 
 a Bad Career 
 in Research/Academia 
 Pre-PhD and Post-PhD
 (& How to Give a Bad Talk) 
 David Patterson UC Berkeley November 18, 2015

Acknowledgments & Related Work
  • Many of these ideas came from (inspired by?) Tom Anderson, David Culler, Al Davis, Ken Goldberg, John Hennessy, Steve Johnson, John Ousterhout, Randy Katz, Bob Sproull, Carlo Séquin, Bill Tetzlaff, …
  • Studs Terkel, Working: People talk about what they do all day and how they feel about what they do. (1974) The New Press.
  • “How to Give a Bad Talk” (1983),
  • “How to Have a Bad Career” (1994), Keynote address, Operating Systems Design and Implementation Conf.
  • Richard Hamming, “You and Your Research” (1995),
  • Ivan Sutherland, “Technology and Courage” (1996).
  • “How the RAD Lab space came to be” (2007),
  • “Your Students are Your Legacy” (2009)
 Communications of the ACM 52.3: 30-33.
  • “How to Build a Bad Research Center” (2014)
 Communications of the ACM 57.3: 33-36.



  • Part I How to Have Bad Grad Student Career,and How to Avoid One
  • Q&A
  • Part II How to Have Bad Research Career
  • Part III How to Avoid a Bad Research Career+ Richard Hamming (Turing Award for 
 error-detecting and error-correcting codes) 
 video clips from “You and Your Research” (1995)
  • Q&A
  • My Story: Accidental Academic (3 min)
  • What Works for Me (3 min)


Part I: Commandments on
to Have a Bad Graduate Career

I. Concentrate on getting good grades

  • –  Postpone research involvement: 
 might lower GPA
  • –  Aim for PhD class valedictorian!Alternative: Maintain reasonable grades– No employer cares about GPA » Sorry, no valedictorian

    – Only once I gave below B in grad course

    – 3 prelim courses only real grades that count

    – What matters: Letters of recommendation

    » From 3-4 faculty & external PhDs 
 who have known you for 5+ years


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/vid/ Richard Feynman Computer Heuristics Lecture





/txt/ How To Train Your Robot

Last Sunday, I taught six kids of ages 5 to 7 how to program. “In what programming language?” you may ask. Well…I didn’t use a programming language, at least none that you know of. In fact, I didn’t even use a computer. Instead, I devised a game called “How To Train Your Robot”. Before I explain how the game works, let me tell my motivation.

I learned how to program during my freshman year at MIT when I was 19. It’s not because I didn’t have a computer at home or I hadn’t heard about programming languages. It was because (a) I thought programming was boring and (b) no one had told me why I should bother. In fact, my computer teacher in high school had told me “you don’t need to waste your time learning how to program. Now we have visual tools to build programs. Programming languages are already obsolete.” That was in 1994 and he was referring to Visual Basic. Luckily for me MIT
wiped all that nonsense away in a matter of weeks. But does one need to wait to go to college to get the proper education?

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