Computer Sciences

Teaching AI agents to type on a Braille keyboard

In recent years, computer scientists have developed artificial intelligence-based techniques that can complete a wide variety of tasks. Some of these techniques are designed to artificially replicate the human senses, particularly ...

Engineering

New algorithm mimics electrosensing in fish

While humans may struggle to navigate a murky, turbid underwater environment, weakly electric fish can do so with ease. These aquatic animals are specially adapted to traverse obscured waters without relying on vision; instead, ...

Computer Sciences

We wouldn't be able to control superintelligent machines

We are fascinated by machines that can control cars, compose symphonies, or defeat people at chess, Go, or Jeopardy! While more progress is being made all the time in Artificial Intelligence (AI), some scientists and philosophers ...

Energy & Green Tech

Making smart thermostats more efficient

Buildings account for about 40% of U.S. energy consumption, and are responsible for one-third of global carbon dioxide emissions. Making buildings more energy-efficient is not only a cost-saving measure, but a crucial climate ...

Computer Sciences

DUAL takes AI to the next level

Scientists at DGIST in Korea, and UC Irvine and UC San Diego in the US, have developed a computer architecture that processes unsupervised machine learning algorithms faster, while consuming significantly less energy than ...

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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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