Computer Sciences

Researchers sniff out AI breakthroughs in mammal brains

When you smell an orange, the scent is most likely combined with several others: car exhaust, garbage, flowers, soap. Those smells bind simultaneously to the hundreds of receptors in your brain's olfactory bulb, obscuring ...

Engineering

Haptic helmet for firefighters

Imagine firefighters trying to navigate through an unfamiliar, burning building full of suffocating smoke and deafening noise. Firefighting is exceedingly dangerous, and the ability for first responders to maintain communications ...

Engineering

Designing a puncture-free tire

Some golf carts and lawnmowers already use airless tires and at least one major tire company produces a non-pneumatic automotive tire, but we still have long way to go before they are on every vehicle that comes off the assembly ...

Engineering

Machine learning shapes microwaves for a computer's eyes

Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power ...

Machine learning & AI

AI can now read emotions—but should it?

In its annual report, the AI Now Institute, an interdisciplinary research center studying the societal implications of artificial intelligence, called for a ban on technology designed to recognize people's emotions in certain ...

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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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