Skip navigation
Help

Theoretical computer science

warning: Creating default object from empty value in /var/www/vhosts/sayforward.com/subdomains/recorder/httpdocs/modules/taxonomy/taxonomy.pages.inc on line 33.

ComicStix asked:

I'm a freshman Computer Science student and we just started doing some actual projects in Python. I have found I'm very efficient when I use the pen and paper method that my professor suggested in class. But when I can't write my problem down and work my algorithms out on paper I am really slow. During labs, I always seem to have to take the assignment back to my dorm. When I get there and write it out I solve the problem that took me the whole class in like 5 minutes.

Maybe it's because I get stressed seeing people solving labs before me. Or maybe it's the pen and paper method.

I was browsing through forums and someone wrote that if you have to write your programs on paper then you shouldn't be a programmer. I'm really worried because I'm so much better when I can see what the program is doing and track my way through it before typing actual code. Am I doing something wrong?

See the original question here.

0
Your rating: None
Original author: 
noreply@blogger.com (Mitchell Whitelaw)

At CODE2012 I presented a paper on "programmable matter" and the proto-computational work of Ralf Baecker and Martin Howse - part of a long-running project on digital materiality. My sources included interviews with the artists, which I will be publishing here. Ralf Baecker's 2009 The Conversation is a complex physical network, woven from solenoids - electro-mechanical "bits" or binary switches. It was one of the works that started me thinking about this notion of the proto-computational - where artists seem to be stripping digital computing down to its raw materials, only to rebuild it as something weirder. Irrational Computing (2012) - which crafts a "computer" more like a modular synth made from crystals and wires - takes this approach further. Here Baecker begins by responding to this notion of proto-computing.

MW: In your work, especially Irrational Computing, we seem to see some of the primal, material elements of digital computing. But this "proto" computing is also quite unfamiliar - it is chaotic, complex and emergent, we can't control or "program" it, and it is hard to identify familiar elements such as memory vs processor. So it seems that your work is not only deconstructing computing - revealing its components - but also reconstructing it in a strange new form. Would you agree?

RB: It took me a long time to adopt the term "proto-computing". I don't mean proto in a historical or chronological sense; it is more about its state of development. I imagine a device that refers to the raw material dimension of our everyday digital machinery. Something that suddenly appears due to the interaction of matter. What I had in mind was for instance the natural nuclear fission reactor in Oklo, Gabon that was discovered in 1972. A conglomerate of minerals in a rock formation formed the conditions for a functioning nuclear reactor, all by chance. 

Computation is a cultural and not a natural phenomenon; it includes several hundred years of knowledge and cultural technics, these days all compressed into a microscopic form (the CPU). In the 18th century the mechanical tradition of automata and symbolic/mathematical thinking merged into the first calculating and astronomical devices. Also the combinatoric/hermeneutic tradition (e.g. Athanasius Kircher and Ramon Llull) is very influential to me. These automatons/concepts were philosophical and epistemological. They were dialogic devices that let us think further, much against our current utilitarian use of technology. Generative utopia.


Schematic of Irrational Computing courtesy of the artist - click for PDF

MW: Your work stages a fusion of sound, light and material. In Irrational Computing for example we both see and hear the activity of the crystals in the SiC module. Similarly in The Conversation, the solenoids act as both mechanical / symbolic components and sound generators. So there is a strong sense of the unity of the audible and the visual - their shared material origins. (This is unlike conventional audiovisual media for example where the relation between sound and image is highly constructed). It seems that there is a sense of a kind of material continuum or spectrum here, binding electricity, light, sound, and matter together?

RB: My first contact with art or media art came through net art, software art and generative art. I was totally fascinated by it. I started programming generative systems for installations and audiovisual performances. I like a lot of the early screen based computer graphics/animation stuff. The pure reduction to wireframes, simple geometric shapes. I had the feeling that in this case concept and representation almost touch each other. But I got lost working with universial machines (Turing machines). With Rechnender Raum I started to do some kind of subjective reappropriation of the digital. So I started to build my very own non-universal devices. Rechnender Raum could also be read as a kinetic interpretation of a cellular automaton algorithm. Even if the Turing machine is a theoretical machine it feels very plastic to me. It a metaphorical machine that shows the conceptual relation of space and time. Computers are basically transposers between space and time, even without seeing the actual outcome of a simulation. I like to expose the hidden structures. They are more appealing to me than the image on the screen.

MW: There is a theme of complex but insular networks in your work. In The Conversation this is very clear - a network of internal relationships, seeking a dynamic equilibrium. Similarly in Irrational Computing, modules like the phase locked loop have this insular complexity. Can you discuss this a little bit? This tendency reminds me of notions of self-referentiality, for example in the writing of Hofstadter, where recursion and self-reference are both logical paradoxes (as in Godel's theorem) and key attributes of consciousness. Your introverted networks have a strong generative character - where complex dynamics emerge from a tightly constrained set of elements and relationships.

RB: Sure, I'm fascinated by this kind of emergent processes, and how they appear on different scales. But I find it always difficult to use the attribute consciousness. I think these kind of chaotic attractors have a beauty on their own. Regardless how closed these systems look, they are always influenced by its environment. The perfect example for me is the flame of a candle. A very dynamic complex process communicating with its environment, that generates the dynamics.

MW: You describe The Conversation as "pataphysical", and mention the "mystic" and "magic" aspects of Irrational Computing. Can you say some more about this a aspect of your work? Is there a sort of romantic or poetic idea here, about what is beyond the rational, or is this about a more systematic alternative to how we understand the world?

RB: Yes, it refers to an other kind of thinking. A thinking that is anti "cause and reaction". A thinking of hidden relations, connections and uncertainty. I like Claude Lévi-Strauss' term "The Savage Mind".

0
Your rating: None
Original author: 
John Timmer


The D-Wave Two.

D-Wave

D-Wave's quantum optimizer has found a new customer in the form of a partnership created by Google, NASA, and a consortium of research universities. The group is forming what it's calling the Quantum Artificial Intelligence Lab and will locate the computer at NASA's Ames Research Center. Academics will get involved via the Universities Space Research Association.

Although the D-Wave Two isn't a true quantum computer in the sense the term is typically used, D-Wave's system uses quantum effects to solve computational problems in a way that can be faster than traditional computers. How much faster? We just covered some results that indicated a certain class of problems may be sped up by as much as 10,000 times. Those algorithms are typically used in what's termed machine learning. And machine learning gets mentioned several times in Google's announcement of the new hardware.

Machine learning is typically used to allow computers to classify features, like whether or not an e-mail is spam (to use Google's example) or whether or not an image contains a specific feature, like a cat. You simply feed a machine learning system enough known images with and without cats and it will identify features that are shared among the cat set. When you feed it unknown images, it can determine whether enough of those features are present and make an accurate guess as to whether there's a cat in it. In more serious applications machine learning has been used to identify patterns of brain activity that are associated with different visual inputs, like viewing different letters.

Read 1 remaining paragraphs | Comments

0
Your rating: None
Original author: 
Stack Exchange

Stack Exchange

This Q&A is part of a weekly series of posts highlighting common questions encountered by technophiles and answered by users at Stack Exchange, a free, community-powered network of 100+ Q&A sites.

It's a response often encountered during technical interviews: "OK, you solved the problem with a while loop, now do it with recursion." Or vice versa. Stack Exchange user Shivan Dragon has encountered the problem and he knows how to answer: show that you're able to code both ways. Give the interviewer what he wants. But which method is generally preferable? A few more experienced programmers respond.

See the original question here.

Read 19 remaining paragraphs | Comments

0
Your rating: None

What_large

For the average human web crawler, the algorithms that power what is searched and seen on the web seem to be black boxes of magical machinery — but there's usually a meat-based observer pulling some strings behind the scenes. As The New York Times observes, computer algorithms are increasingly being guided with a human touch, necessary for giving subtle context for human language that can elude even the most powerful machines like IBM's Watson. Even Twitter, the Times reports, "uses a far-flung army of contract workers" to interpret trending search terms; these Twitter "judges" were able, for example, to recognize that Mitt Romney's comments about "Big Bird" during last year's presidential debate were about politics and not really...

Continue reading…

0
Your rating: None

An anonymous reader sends this excerpt from a UC Berkeley news release: "Our eyes may be our window to the world, but how do we make sense of the thousands of images that flood our retinas each day? Scientists at the University of California, Berkeley, have found that the brain is wired to put in order all the categories of objects and actions that we see. They have created the first interactive map of how the brain organizes these groupings."

Share on Google+

Read more of this story at Slashdot.

0
Your rating: None