Computer Sciences

Do trucks mean Trump? AI shows how humans misjudge images

A study on the types of mistakes that humans make when evaluating images may enable computer algorithms that help us make better decisions about visual information, such as while reading an X-ray or moderating online content.

Computer Sciences

Three questions about quantum computing and secure communications

A radically different type of computing technology under development, known as quantum computing, could in theory decode secure communications and jeopardize military communications, critical infrastructure, and financial ...

Hardware

A deep learning-augmented smart mirror to enhance fitness training

In recent years, engineers and computer scientists have created a wide range of technological tools that can enhance fitness training experiences, including smart watches, fitness trackers, sweat-resistant earphones or headphones, ...

Security

Collaborative machine learning that preserves privacy

Training a machine-learning model to effectively perform a task, such as image classification, involves showing the model thousands, millions, or even billions of example images. Gathering such enormous datasets can be especially ...

Energy & Green Tech

A self-learning algorithm that helps save heating energy

A thermostat that predictively controls the indoor climate and thereby improves energy efficiency and comfort—Empa researchers Felix Bünning and Benjamin Huber came up with this idea while working in Empa's Urban Energy ...

Engineering

Optimizing fluid mixing with machine learning

Fluid mixing is an important part of several industrial processes and chemical reactions. However, the process often relies on trial-and-error-based experiments instead of mathematical optimization. While turbulent mixing ...

Internet

Unlocking the secret to private messaging apps

Whether you're sharing confidential information or swapping movie ideas with a friend, people are turning to private messaging apps that offer end-to-end encryption to protect the contents of their conversations.

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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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