System brings deep learning to Internet of Things devices

Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new—and much smaller—places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).

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HAMLET: A platform to simplify AI research and development

Machine learning (ML) algorithms have proved to be highly valuable computational tools for tackling a variety of real-world problems, including image, audio and text classification tasks. Computer scientists worldwide are developing more of these algorithms every day; thus, keeping track of them and quickly finding or accessing those introduced in the past is becoming increasingly challenging.

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Using social robots to improve children’s language skills

As robots share many characteristics with toys, they could prove to be a valuable tool for teaching children in engaging and innovative ways. In recent years, some roboticists and computer scientists have thus been investigating how robotics systems could be introduced in classroom and pre-school environments.

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New method brings physics to deep learning to better simulate turbulence

Deep learning, also called machine learning, reproduces data to model problem scenarios and offer solutions. However, some problems in physics are unknown or cannot be represented in detail mathematically on a computer. Researchers at the University of Illinois Urbana-Champaign developed a new method that brings physics into the machine learning process to make better predictions.

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Researchers use dynamical systems and machine learning to add spontaneity to AI

Autonomous functions for robots, such as spontaneity, are highly sought after. Many control mechanisms for autonomous robots are inspired by the functions of animals, including humans. Roboticists often design robot behaviors using predefined modules and control methodologies, which makes them task-specific, limiting their flexibility. Researchers offer an alternative machine learning-based method for designing spontaneous behaviors by capitalizing on complex temporal patterns, like neural activities of animal brains. They hope to see their design implemented in robotic platforms to improve their autonomous capabilities.

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A continuous data supply ensures data-intensive simulations can run at maximum speed

A pre-emptive memory management system developed by KAUST researchers can speed up data-intensive simulations by 2.5 times by eliminating delays due to slow data delivery. The development elegantly and transparently addresses one of the most stubborn bottlenecks in modern supercomputing—delivering data from memory fast enough to keep up with computations.

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