Robotics

Algorithm helps robots avoid obstacles in their path

If you've ever ordered a product from Amazon, chances are that a robot selected your purchase from a shelf, read the barcode and delivered it to the counter for packaging. Hopefully, it didn't collide with a human worker ...

Computer Sciences

Novel way to perform 'general inverse design' with high accuracy

Researchers from the Low Energy Electronic Systems (LEES) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), together with institutional collaborators, have discovered a ...

Machine learning & AI

Developing artificial emotional intelligence

Can artificial intelligence (AI) have emotional intelligence? Research published in the International Journal of Engineering Systems Modelling and Simulation, plots the roadmap.

Robotics

Engineers teach AI to navigate ocean with minimal energy

Engineers at Caltech, ETH Zurich, and Harvard are developing an artificial intelligence (AI) that will allow autonomous drones to use ocean currents to aid their navigation, rather than fighting their way through them.

Robotics

A system for designing and training intelligent soft robots

Let's say you wanted to build the world's best stair-climbing robot. You'd need to optimize for both the brain and the body, perhaps by giving the bot some high-tech legs and feet, coupled with a powerful algorithm to enable ...

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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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