Machine learning & AI

Reducing risk in AI and machine learning-based medical technology

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate ...

Machine learning & AI

Rainforest preservation through machine learning

Computer scientist David Dao develops intelligent algorithms that use satellite and drone images of rainforests to predict where the next sites of deforestation will be. He will be presenting his research at the climate conference ...


Ant-based troll detection

Uncovering trolls and malicious or spammy accounts on social media is increasingly difficult as the miscreants find more and more ways to camouflage themselves as seemingly legitimate. Writing in the International Journal ...

Computer Sciences

New algorithms train AI to avoid specific bad behaviors

Artificial intelligence has moved into the commercial mainstream thanks to the growing prowess of machine learning algorithms that enable computers to train themselves to do things like drive cars, control robots or automate ...


CyLab researchers propose new rules for Internet fairness

Just weeks after a team of Carnegie Mellon researchers showed that Google's new congestion control algorithm (CCA) was giving an unfair advantage to its own traffic over services using legacy algorithms, the same team has ...

Computer Sciences

A new parallel strategy for tackling turbulence on Summit

Turbulence, the state of disorderly fluid motion, is a scientific puzzle of great complexity. Turbulence permeates many applications in science and engineering, including combustion, pollutant transport, weather forecasting, ...

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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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