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Dynamic network plasticity and sample efficiency in biological neural cultures: A comparative study with deep reinforcement learning

(A) Schematic illustration of the DishBrain feedback loop, the simulated game environment, and electrode configurations. (B) A schematic illustration of the overall network construction framework. The ...
Geeky Gadgets

Watch AI learn to walk using Deep Reinforcement Learning (DRL)

Deep Reinforcement Learning (DRL) is a subfield of machine learning that combines neural networks with reinforcement learning techniques to make decisions in complex environments. It has been applied ...
Parcel and Postal Technology International

AI system improves warehouse robot traffic flow, MIT study finds

Researchers at MIT and automation company Symbotic have developed an AI-based system designed to improve traffic flow among ...
InfoWorld

Styles of machine learning: Intro to neural networks

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks. Artificial intelligence ...
The Scientist

Artificial Neural Networks: Learning by Doing

Article reviewed by Grace Lindsay, PhD from New York University. Scientists design ANNs to function like neurons. 6 They write lines of code in an algorithm such that there are nodes that each contain ...
eWeek

Neural Networks vs. Deep Learning

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...