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🔗 Computing 🔗 Computer science 🔗 Computing/Software 🔗 Computing/Computer science

Comb sort is a relatively simple sorting algorithm originally designed by Włodzimierz Dobosiewicz and Artur Borowy in 1980, later rediscovered by Stephen Lacey and Richard Box in 1991. Comb sort improves on bubble sort.

🔗 Computing 🔗 Computer science 🔗 Computing/Software 🔗 Computing/Free and open-source software 🔗 Linux

OpenSSI is an open-source single-system image clustering system. It allows a collection of computers to be treated as one large system, allowing applications running on any one machine access to the resources of all the machines in the cluster.

OpenSSI is based on the Linux operating system and was released as an open source project by Compaq in 2001. It is the final stage of a long process of development, stretching back to LOCUS, developed in the early 1980s.

🔗 Computing 🔗 Computer science

🔗 United States 🔗 Biography 🔗 Computer science 🔗 Telecommunications 🔗 Systems 🔗 Biography/science and academia 🔗 Cryptography 🔗 Cryptography/Computer science 🔗 Electronics 🔗 Systems/Systems theory 🔗 Telecommunications/Bell System 🔗 Cycling

Claude Elwood Shannon (April 30, 1916 – February 24, 2001) was an American mathematician, electrical engineer, and cryptographer known as "the father of information theory". Shannon is noted for having founded information theory with a landmark paper, "A Mathematical Theory of Communication", that he published in 1948.

He is also well known for founding digital circuit design theory in 1937, when—as a 21-year-old master's degree student at the Massachusetts Institute of Technology (MIT)—he wrote his thesis demonstrating that electrical applications of Boolean algebra could construct any logical numerical relationship. Shannon contributed to the field of cryptanalysis for national defense during World War II, including his fundamental work on codebreaking and secure telecommunications.

🔗 Computing 🔗 Computer science 🔗 Computing/Software

This is a list of notable lexer generators and parser generators for various language classes.

🔗 Computing 🔗 Computer science

Note G was a computer algorithm written by Ada Lovelace, and was designed to calculate Bernoulli numbers using the hypothetical analytical engine. Note G is generally agreed to be the first algorithm specifically for a computer, and Lovelace is considered as the first computer programmer as a result. The algorithm was the last note in a series labelled A to G, which she employed as visual aids to accompany her English translation of Luigi Menabrea's 1842 French transcription of Charles Babbage's lecture on the analytical engine at the University of Turin, "Notions sur la machine analytique de Charles Babbage" ("Elements of Charles Babbage’s Analytical Machine"). Lovelace's Note G was never tested, as the engine was never built. Her notes, along with her translation, were published in 1843.

In the modern era, thanks to more readily available computing equipment and programming resources, Lovelace's algorithm has since been tested, after being "translated" into modern programming languages. These tests have independently concluded that there was a bug in the script, due to a minor typographical error, rendering the algorithm in its original state unusable.

🔗 Computer science

Cuckoo hashing is a scheme in computer programming for resolving hash collisions of values of hash functions in a table, with worst-case constant lookup time. The name derives from the behavior of some species of cuckoo, where the cuckoo chick pushes the other eggs or young out of the nest when it hatches; analogously, inserting a new key into a cuckoo hashing table may push an older key to a different location in the table.

🔗 Computing 🔗 Computer science 🔗 Science Fiction 🔗 Cognitive science 🔗 Robotics 🔗 Transhumanism 🔗 Software 🔗 Software/Computing 🔗 Futures studies

Artificial general intelligence (AGI) is the hypothetical intelligence of a machine that has the capacity to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futures studies. AGI can also be referred to as strong AI, full AI, or general intelligent action. (Some academic sources reserve the term "strong AI" for machines that can experience consciousness.)

Some authorities emphasize a distinction between strong AI and applied AI (also called narrow AI or weak AI): the use of software to study or accomplish specific problem solving or reasoning tasks. Weak AI, in contrast to strong AI, does not attempt to perform the full range of human cognitive abilities.

As of 2017, over forty organizations were doing research on AGI.

🔗 Computer science 🔗 Statistics 🔗 Databases

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories:

• Agglomerative: This is a "bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
• Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.

Hierarchical clustering has the distinct advantage that any valid measure of distance can be used. In fact, the observations themselves are not required: all that is used is a matrix of distances. On the other hand, except for the special case of single-linkage distance, none of the algorithms (except exhaustive search in ${\displaystyle {\mathcal {O}}(2^{n})}$) can be guaranteed to find the optimum solution.

🔗 Computer science 🔗 Statistics

A Boltzmann machine (also called stochastic Hopfield network with hidden units) is a type of stochastic recurrent neural network. It is a Markov random field. It was translated from statistical physics for use in cognitive science. The Boltzmann machine is based on stochastic spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model that is a stochastic Ising Model and applied to machine learning.

Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks. They were one of the first neural networks capable of learning internal representations, and are able to represent and (given sufficient time) solve combinatoric problems.

They are theoretically intriguing because of the locality and Hebbian nature of their training algorithm (being trained by Hebb's rule), and because of their parallelism and the resemblance of their dynamics to simple physical processes. Boltzmann machines with unconstrained connectivity have not proven useful for practical problems in machine learning or inference, but if the connectivity is properly constrained, the learning can be made efficient enough to be useful for practical problems.

They are named after the Boltzmann distribution in statistical mechanics, which is used in their sampling function. That's why they are called "energy based models" (EBM). They were invented in 1985 by Geoffrey Hinton, then a Professor at Carnegie Mellon University, and Terry Sejnowski, then a Professor at Johns Hopkins University.