Machine learning & AI

AI models are powerful, but are they biologically plausible?

Artificial neural networks, ubiquitous machine-learning models that can be trained to complete many tasks, are so called because their architecture is inspired by the way biological neurons process information in the human ...

Robotics

Using large language models to code new tasks for robots

You've likely heard that "experience is the best teacher"—but what if learning in the real world is prohibitively expensive? This is the plight of roboticists training their machines on manipulation tasks. Real-world interaction ...

Computer Sciences

Trust the machine—it knows what it is doing

Machine learning, when used in climate science builds an actual understanding of the climate system, according to a study published in the journal Chaos by Manuel Santos Gutiérrez and Valerio Lucarini, University of Reading, ...

Engineering

Video: Modeling turbofan engines to understand aircraft noise

Airplane engines are loud—just ask anyone who lives near an airport. Increased air traffic from next-generation aircraft has the potential for even more disruptive noise. Researchers and engineers at NASA are working to ...

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Simulation & Modeling for Acquisition, Requirements, and Trainin

Simulation is the imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviours of a selected physical or abstract system.

Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning. Other contexts include simulation of technology for performance optimization, safety engineering, testing, training and education. Simulation can be used to show the eventual real effects of alternative conditions and courses of action.

Key issues in simulation include acquisition of valid source information about the relevent selection of key characteristics and behaviours, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.

This text uses material from Wikipedia, licensed under CC BY-SA