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Detecting solar flares, more in real time

Computers can learn to find solar flares and other events in vast streams of solar images and help NOAA forecasters issue timely alerts, according to a new study. The machine-learning technique, developed by scientists at ...

Soil study shows Australia at its most stripped back

New research from The Australian National University (ANU) and Geoscience Australia could provide a much clearer picture of the Australian landscape, and how to better manage it under a changing climate.

New algorithm rapidly finds anomalies in gene expression data

Computational biologists at Carnegie Mellon University have devised an algorithm to rapidly sort through mountains of gene expression data to find unexpected phenomena that might merit further study. What's more, the algorithm ...

Building a better battery with machine learning

Designing the best molecular building blocks for battery components is like trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients ...

Swiss army knife for genome research

It is the the dream of every molecular geneticist: an easy-to-use program that compares datasets from different cellular conditions, identifies enhancer regions and then assigns them to their target genes. A research team ...

NASA applying AI technologies to problems in space science

Could the same computer algorithms that teach autonomous cars to drive safely help identify nearby asteroids or discover life in the universe? NASA scientists are trying to figure that out by partnering with pioneers in artificial ...

Putting a conservation finger on the internet's pulse

Scientists from the University of Helsinki have figured out how to mine people's online reactions to endangered animals and plants so that they can reduce the chance of pushing species toward extinction.

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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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