Computer Sciences

Quantum computer programming for dummies

For would-be quantum programmers scratching their heads over how to jump into the game as quantum computers proliferate and become publicly accessible, a new beginner's guide provides a thorough introduction to quantum algorithms ...

Computer Sciences

Scientists use reinforcement learning to train quantum algorithm

Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve ...

Computer Sciences

Why GPT detectors aren't a solution to the AI cheating problem

In the wake of the high-profile launch of ChatGPT, no fewer than seven developers or companies have countered with AI detectors. That is, AI they say is able to tell when content was written by another AI. These new algorithms ...

Robotics

Autonomous excavator constructs a 6-meter-high dry-stone wall

ETH Zurich researchers deployed an autonomous excavator, called HEAP, to build a 6-meter-high and 65-meter-long dry-stone wall. The wall is embedded in a digitally planned and autonomously excavated landscape and park.

Computer Sciences

Facebook enhances AI computer vision with SEER

At a time when many versions of AI rely on pre-established data sets for image recognition, Facebook has developed SEER (Self-supERvised) – a deep learning solution able to register images on the Internet independent of ...

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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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