Computer Sciences

Algorithm eliminates blurred images caused by shaky footage

Duke University computer engineers have designed algorithms capable of sharpening video blurred by a shaky camera. Newly integrated into Adobe's After Effects video editing software, the solution is bringing relief to tripod-less ...

Machine learning & AI

Researchers are helping artificial intelligence understand fairness

"What is fair?" feels like a rhetorical question. But for Michigan State University's Pang-Ning Tan, it's a question that demands an answer as artificial intelligence systems play a growing role in deciding who gets proper ...

Computer Sciences

A 26-layer convolutional neural network for human action recognition

Deep learning algorithms, such as convolutional neural networks (CNNs), have achieved remarkable results on a variety of tasks, including those that involve recognizing specific people or objects in images. A task that computer ...

Electronics & Semiconductors

A diffractive neural network that can be flexibly programmed

In recent decades, machine learning and deep learning algorithms have become increasingly advanced, so much so that they are now being introduced in a variety of real-world settings. In recent years, some computer scientists ...

Computer Sciences

Novel algorithm enables statistical analysis of time series data

Whether it's tracking brain activity in the operating room, seismic vibrations during an earthquake, or biodiversity in a single ecosystem over a million years, measuring the frequency of an occurrence over a period of time ...

Machine learning & AI

The brain inspires a new type of artificial intelligence

Machine learning, introduced 70 years ago, is based on evidence of the dynamics of learning in the brain. Using the speed of modern computers and large datasets, deep learning algorithms have recently produced results comparable ...

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Algorithm

In mathematics, computing, linguistics, and related subjects, an algorithm is a finite sequence of instructions, an explicit, step-by-step procedure for solving a problem, often used for calculation and data processing. It is formally a type of effective method in which a list of well-defined instructions for completing a task, will when given an initial state, proceed through a well-defined series of successive states, eventually terminating in an end-state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as probabilistic algorithms, incorporate randomness.

A partial formalization of the concept began with attempts to solve the Entscheidungsproblem (the "decision problem") posed by David Hilbert in 1928. Subsequent formalizations were framed as attempts to define "effective calculability" (Kleene 1943:274) or "effective method" (Rosser 1939:225); those formalizations included the Gödel-Herbrand-Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's "Formulation 1" of 1936, and Alan Turing's Turing machines of 1936–7 and 1939.

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