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The earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity, aiding in computations such as multiplication and division. Algorithms for performing computations have exi... | Computer science |
abstract data type (ADT) A mathematical model for data types in which a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations. This contrasts with data struct... | Glossary of computer science |
Many devices or programs need data to be presented in a specific format to process the data. For example, Apple Inc devices generally require applications to be downloaded from their App Store. This is a non data-agnostic method, as it uses a specified file type, downloaded from a specific location, and does not functi... | Agnostic (data) |
In 2020 J. Cook and Mertz used catalytic computing to prove to attack the tree evaluation problem (TreeEval) a type of pebble game introduced by Cook, McKenzie, Wehr, Braverman and Santhanam as an example where any algorithm for solving the problem would require too much memory to belong in the L complexity class, prov... | Catalytic computing |
The field of computational gastronomy aims to enhance understanding and innovation in culinary science through computational tools. By analyzing the relationships between food components, health, and flavor, researchers seek to create innovative culinary experiences and improve food preparation techniques. Despite its ... | Computational gastronomy |
Going back in history, computers in sports were used for the first time in the 1960s, when the main purpose was to accumulate sports information. Databases were created and expanded in order to launch documentation and dissemination of publications like articles or books that contain any kind of knowledge related to sp... | Computer science in sport |
FRP follows a two-step processing strategy: Filter: an efficient filter function f f i l t e r {\displaystyle f_{filter}} is applied to each object x {\displaystyle x} in the dataset D {\displaystyle {\mathcal {D}}} . The filtered subset D ′ {\displaystyle {\mathcal {D}}'} is defined as D ′ = { x | f f i l t e r ( x ) ... | Filter and refine |
History of computer science List of pioneers in computer science History of Artificial Intelligence History of Operating Systems Computer Scientist Programmer (Software developer) Teacher/Professor Software engineer Software architect Software tester Hardware engineer Data analyst Interaction designer Network administr... | Outline of computer science |
Prefetching works by predicting which memory addresses or resources will be accessing and load them into faster access storage, like caches. Prefetching may be used: Hardware-level, such as CPU memory controllers Software-level, strategies in compilers, operating systems, logic in web browsers or file systems Processor... | Prefetching |
Notable examples of technology transfer in computer science include: == References == | Technology transfer in computer science |
The study of new and fundamental design methods, models and techniques that enable automated, coordinated and cross-layer transitions between functionally similar mechanisms within a communication system is the main goal of a collaborative research center funded by the German research foundation (DFG). The DFG collabor... | Transition (computer science) |
Tumusiime attended Mount Saint Mary's College Namagunga , then did a Bachelor of Computer Science at Makerere University from 2008–2011 and Business and Entrepreneurship at Clark Atlanta University in 2015. Tumusiime is the Founder, CEO of Zimba Zimba Group Ltd from December 2014 to date, which currently collaborates w... | Sherifah Tumusiime |
The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period. Although the earliest machine learning model was introduced in the 1950s when Arthur Samuel inven... | Machine learning |
An academic discipline A branch of science An applied science A subfield of computer science A branch of artificial intelligence A subfield of soft computing Application of statistics Applications of machine learning Bioinformatics Biomedical informatics Computer vision Customer relationship management Data mining Eart... | Outline of machine learning |
It was first reported in a technical report in April 2007, during the middle of the construction process, when there were only 73 million images. The full dataset was published in 2008. They began with all 75,846 nonabstract nouns in WordNet, and then for each of these nouns, they scraped 7 Image search engines: Altavi... | 80 Million Tiny Images |
The artificial neuron used in the original paper is slightly different from the modern version. They considered neural networks that operate in discrete steps of time t = 0 , 1 , … {\displaystyle t=0,1,\dots } . The neural network contains a number of neurons. Let the state of a neuron i {\displaystyle i} at time t {\d... | A Logical Calculus of the Ideas Immanent in Nervous Activity |
x86-64 ARM64 NVIDIA GPU AMD GPU Intel GPU Apple GPU Google TPU AWS Trainium, Inferentia Cerebras Graphcore IPU | Accelerated Linear Algebra |
Given a training set E {\displaystyle E} consisting of examples e = ( s , a , s ′ ) {\displaystyle e=(s,a,s')} , where s , s ′ {\displaystyle s,s'} are observations of a world state from two consecutive time steps t , t ′ {\displaystyle t,t'} and a {\displaystyle a} is an action instance observed in time step t {\displ... | Action model learning |
Let T be the total set of all data under consideration. For example, in a protein engineering problem, T would include all proteins that are known to have a certain interesting activity and all additional proteins that one might want to test for that activity. During each iteration, i, T is broken up into three subsets... | Active learning (machine learning) |
At the MIT Spam Conference in January 2004, John Graham-Cumming showed that a machine-learning spam filter could be used to defeat another machine-learning spam filter by automatically learning which words to add to a spam email to get the email classified as not spam. In 2004, Nilesh Dalvi and others noted that linear... | Adversarial machine learning |
AI/ML development platforms serve as comprehensive environments for building AI systems, ranging from simple predictive models to complex large language models (LLMs). They abstract technical complexities (e.g., distributed computing, hyperparameter tuning) while offering modular components for customization. Key users... | AI/ML Development Platform |
AIOPs was first defined by Gartner in 2016, combining "artificial intelligence" and "IT operations" to describe the application of AI and machine learning to enhance IT operations. This concept was introduced to address the increasing complexity and data volume in IT environments, aiming to automate processes such as e... | AIOps |
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