Smart algorithms make packaging of meat products more efficient

In supermarkets you can find a large variety of poultry products, all conveniently packaged in fixed-weight quantities. However, poultry processing plants face numerous challenges due to these fixed-weight batches, growing ...


Keeping an eye on infrastructure systems: 4 tactics

Even minor disruptions in infrastructure systems can have fatal consequences. Researchers and practitioners counter that risk by taking action on multiple levels. Four examples.

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

Novel deep learning framework for symbolic regression

Lawrence Livermore National Laboratory (LLNL) computer scientists have developed a new framework and an accompanying visualization tool that leverages deep reinforcement learning for symbolic regression problems, outperforming ...

Computer Sciences

Researchers explore possibilities for an ultra-secure gun registry

Proposals to create a national gun registry have long been met with fierce opposition from gun rights advocates. While proponents say a registry would help in tracking guns used in crimes, opponents worry that it would compromise ...

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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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