Undergraduate Women in Computer Science: Experience, Motivation and Culture is a report on a study of women in computer science at Carnegie Mellon. While it is only a preliminary report it strikes me as balanced and interesting. Their initial findings include some reflections on what got men and women into CS – a number of male students talked about the computer as a toy or game that they got caught up playing with in an undirected way. Female students, by contrast commented on what they wanted to do with computing.

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# Category: Foundations of Computing

## Error Correction

In a paper I gave in Georgia I picked up on a comment by Negroponte in Being Digital to the effect that error correction is one of the fundamental advantages of digital (vs analog) data. Automatic error correction makes lossless copying and transmission possible. Digital Revolution (III) – Error Correction Codes is the third in a set of Feature Column essays on the “Digital Revolution.” (The other two are on Barcodes and Compression Codes and Technologies.)

To exaggerate, we can say that error correction makes computing possible. Without error correction we could not automate computing reliably enough to use it outside the lab. Something as simple as moving data off a hard-drive across the bus to the CPU can only happen at high speeds and repeatedly if we can build systems that guarantee what-was-sent-is-what-was-got.

There are exceptions, and here is where it can get interesting. Certain types of data can still be useful when corrupted, for example images, audio, video and text – namely media data – while others if corrupted become useless. Data that is meant for output to a human for interpretation needs less error correction (and can be compressed using lossy compression) while still remaining usable. Could such media have a surplus of information from which we can correct for loss that is the analog equivalent to symbolic error correction?

Another way to put this is that there is always noise. Data is susceptible to noise when transmitted, when stored, and when copied.

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## Quantum Computing and Information

Jerry McGann has been talking about quantum models of textuality for some time. (See McGann, “Preface to _Radiant Textuality: Literary Studies After the World Wide Web_”, Romanticism and Contemporary Culture, Praxis Series, Romantic Circles.)

In general whenever Jerry is interested in something it is worth thinking about, even when I don’t get it. Here, therefore are some links on quantum computing.

An introduction to Quantum Computing is a short and clear introduction.

Quantum Computation and Quantum Information is the site to a book with that title where you can download/read the first chapter.

Seb’s Open Research blog also has an entry on Quantum computing weblogs (**Note: **blog now gone). This entry got me going on the subject.

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## Intersections of Math and Multimedia

The following are some intersections of mathematics, computer science, philosophy and multimedia.

1. Descartes and Analytical Geometry

2. Frege and Russell – the intersection of logic and philosophy of mathematics

3. Turing’s solution of the Halting Problem

4. von Neuman and Game Theory

5. Euler, Graph Theory and the Semantic Web

6. Set theory, Kleene and Regular Expressions

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