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🔗 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.

🔗 Computer science

In computer science, Linda is a model of coordination and communication among several parallel processes operating upon objects stored in and retrieved from shared, virtual, associative memory. It was developed by Sudhir Ahuja at AT&T Bell Laboratories in collaboration with David Gelernter and Nicholas Carriero at Yale University in 1986.

🔗 Computer science

In computer programming, the Schwartzian transform is a technique used to improve the efficiency of sorting a list of items. This idiom is appropriate for comparison-based sorting when the ordering is actually based on the ordering of a certain property (the key) of the elements, where computing that property is an intensive operation that should be performed a minimal number of times. The Schwartzian transform is notable in that it does not use named temporary arrays.

The Schwartzian transform is a version of a Lisp idiom known as decorate-sort-undecorate, which avoids recomputing the sort keys by temporarily associating them with the input items. This approach is similar to memoization, which avoids repeating the calculation of the key corresponding to a specific input value. By comparison, this idiom assures that each input item's key is calculated exactly once, which may still result in repeating some calculations if the input data contains duplicate items.

The idiom is named after Randal L. Schwartz, who first demonstrated it in Perl shortly after the release of Perl 5 in 1994. The term "Schwartzian transform" applied solely to Perl programming for a number of years, but it has later been adopted by some users of other languages, such as Python, to refer to similar idioms in those languages. However, the algorithm was already in use in other languages (under no specific name) before it was popularized among the Perl community in the form of that particular idiom by Schwartz. The term "Schwartzian transform" indicates a specific idiom, and not the algorithm in general.

For example, to sort the word list ("aaaa","a","aa") according to word length: first build the list (["aaaa",4],["a",1],["aa",2]), then sort it according to the numeric values getting (["a",1],["aa",2],["aaaa",4]), then strip off the numbers and you get ("a","aa","aaaa"). That was the algorithm in general, so it does not count as a transform. To make it a true Schwartzian transform, it would be done in Perl like this:

🔗 Computing 🔗 Computer science 🔗 Biology 🔗 Chemistry 🔗 Genetics

DNA computing is a branch of computing which uses DNA, biochemistry, and molecular biology hardware, instead of the traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of DNA computing. The term "molectronics" has sometimes been used, but this term has already been used for an earlier technology, a then-unsuccessful rival of the first integrated circuits; this term has also been used more generally, for molecular-scale electronic technology.

🔗 Computer science 🔗 Mathematics

In computer science and graph theory, the Canadian traveller problem (CTP) is a generalization of the shortest path problem to graphs that are partially observable. In other words, the graph is revealed while it is being explored, and explorative edges are charged even if they do not contribute to the final path.

This optimization problem was introduced by Christos Papadimitriou and Mihalis Yannakakis in 1989 and a number of variants of the problem have been studied since. The name supposedly originates from conversations of the authors who learned of a difficulty Canadian drivers had: traveling a network of cities with snowfall randomly blocking roads. The stochastic version, where each edge is associated with a probability of independently being in the graph, has been given considerable attention in operations research under the name "the Stochastic Shortest Path Problem with Recourse" (SSPPR).