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A new technique enables on-device training of machine-learning models on edge devices like microcontrollers, which have very limited memory. This could allow edge devices to continually learn from new data, eliminating data privacy issues, while enabling user customization.
A new system of algorithms enables four-legged robots to walk and run on challenging terrain while avoiding both static and moving obstacles. The work brings researchers a step closer to building robots that can perform search and rescue missions or collect information in places that are too dangerous or difficult for humans.
Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on “edge devices” that work independently from central computing resources.
A team led by the University of California San Diego has developed a new system of algorithms that enables four-legged robots to walk and run on challenging terrain while avoiding both static and moving obstacles.
The new field of AI Ethics includes Responsible and Trustworthy AI, with the goal of doing no harm with artificial intelligence algorithms.Read More
Join industry experts as they dig into how enterprises can break down silos and turn data into company-wide AI and machine learning solutionsRead More
OpenAI’s Whisper is a deep learning model that shows promise in transcribing audio in several languages. It’s also a nod to open source.Read More
Cloud AI Experience is designed to enable organizations to listen and understand customers and employees by coordinating technology, interactions and touchpoints.Read More
Tesla’s Optimus robot did not dazzle robotics experts and can’t yet walk. But if the project delivers, it could give the company an edge in manufacturing.
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