When you pour water into this strangely shaped container, addition somehow turns into multiplication.

The secret behind it was hidden in a certain graph.

Physical computation, integrals, and logarithms all in one video. Nice.

mathematical knowledge is defined by its computation-granting abilities. Knowledge exists if and only if you can compute answers to questions exhaustively in the domain of that knowledge.

It could be said that no human yet has a “full understanding” of integration, or that Liouville’s theorem shows the impossibility of solving this problem completely. And so to add to “can you compute” you must allow a response, “impossible given the constraints.”

Once a field that doesn’t have strong computational roots becomes computational (see, topology becoming algebraic topology), you get mathematicians calling algebra the “devil” and complaining that a subject is no longer beautiful.

why are some algebraic structures more useful than others in software applications?

A “strong” structure admits constructive characterization theorems, and efficient algorithms to convert objects into various canonical forms.

Very nice post. Explains the immense power of linear algebra and graph theory.

Project by Chaski ﹙Saskia Knowles﹚: Open visual archive on cybernetics in the expanded field.

Also see: https://www.are.na/sam-hart/cybernetics-image-library

Project by Chaski ﹙Saskia Knowles﹚: Open visual archive on cybernetics in the expanded field.

Also see: https://www.are.na/sam-hart/cybernetics-image-library

If we want computers to be able to compute for us, then we have to accurately extract these models from our heads and record them. Writing Python isn’t the fundamental skill we need to teach people. Modeling systems is.

we have been putting forward this idea of a self-obviating system, which is a computational system that, by design, tries to make itself less and less necessary to the functioning of the system in which it is embedded. In this regard, many educational systems are by nature self-obviating systems. They’re systems that are training people to do something, where you don’t want them to be continuously reliant on the technology, you would like them to be able to be self-sufficient.

Article demonstrates how origami is Turing complete

This text presents a method for embedding a programmable, general-purpose, digital computer into Tetris. It describes the capabilities and performance of an implementation that runs Tetris on Tetris.

Imagine a novel, which is imbedded in a longer novel, which itself is imbedded in an even longer novel, and so on ad infinitum. Within each novel, the characters can debate the literary merits of any of the sub-novels. But, by analogy with classes of machines that can’t analyze themselves, the characters can never critique the novel that they themselves are in.

Although not originally designed for educationalpurposes, Plan 9 and Inferno are excellent tools for usein Computer Science education. Here I discuss how I usethem in my classes at Drexel University. The discussionincludes CS314: Computing in the Small using Plan 9, theOperating Systems courses CS370 and CS543 using Inferno,and the computer I take to the classroom forpresentations running Plan 9.

..there is never an unambiguous cut between the material, the political, the economic and the epistemological: If computer scientists ‘think through hardware’ then the process of production of that hardware has just as much influence on theoretical structures as those theories can exert on hardware through technological innovation

The Malleable Systems Collective is a community space that catalogs and experiments with malleable software and systems that reset the balance of power via several essential principles.

The ideas for might have forgotten or never knew or needed reminding or should have forgotten in computer science

ompact models of fundamental ideas have aided thinking in many sciences. We think they haven’t been done often enough in computing, but when done, have had similar positive effects [Mo]. We want our model to be explanatory, but we also want it to run well enough to serve as a practical artifact with a wide range of application. A good model of personal computing should certainly cover today’s good ideas and scales, but because it is an extreme abstraction, it does not have to reverse engineer shortfalls in unity and design; instead, it can be unified as much as possible if it covers the worthwhile parts of the end-user experiences.

ompact models of fundamental ideas have aided thinking in many sciences. We think they haven’t been done often enough in computing, but when done, have had similar positive effects [Mo]. We want our model to be explanatory, but we also want it to run well enough to serve as a practical artifact with a wide range of application. A good model of personal computing should certainly cover today’s good ideas and scales, but because it is an extreme abstraction, it does not have to reverse engineer shortfalls in unity and design; instead, it can be unified as much as possible if it covers the worthwhile parts of the end-user experiences.

Interaciion nets generalise Girard’s proof nets of linear logic and illustrate the advantage of an inte- grated logic approach, as opposed to the external one. In other words, we did not try to design a logic de- scribing the behaviour of some given computational system, but a programming language for which the type discipline is already (almost) a logic.