Psychology & Psychiatry

AI faces look more real than actual human face: Study

White faces generated by artificial intelligence (AI) now appear more real than human faces, according to new research led by experts at The Australian National University (ANU).

Neuroscience

Blue Brain solves a century-old neuroscience problem

A team led by Lida Kanari now reports a new system for distinguishing cell types in the brain, an algorithmic classification method that the researchers say will benefit the entire field of neuroscience. Blue Brain founder ...

Neuroscience

The way a single neuron processes information is never the same

How do neurons process information? Neurons are known to break down an incoming electrical signal into sub-units. Now, researchers at Blue Brain have discovered that dendrites, the neuron's tree-like receptors, work together—dynamically ...

Medical research

Artificial intelligence and the future of medicine

Washington University researchers are working to develop artificial intelligence (AI) systems for health care, which have the potential to transform the diagnosis and treatment of diseases, helping to ensure that patients ...

Neuroscience

Neurons have the right shape for deep learning

Deep learning has brought about machines that can 'see' the world more like humans can, and recognize language. And while deep learning was inspired by the human brain, the question remains: Does the brain actually learn ...

page 1 from 40

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.

This text uses material from Wikipedia, licensed under CC BY-SA