AI-aided design software startup Neural Concept raises $100M to accelerate product engineering

Neural Concept aims to accelerate these timelines by integrating AI directly into CAD and physics-based simulation ...

In Chasing Biological Efficiency, Innovators Are Turning To “Analog”

Biology-inspired, silicon-based computing may boost AI efficiency; AMP2 instead uses AI to accelerate anaerobic biology.
SiliconANGLE

MIT researchers use neural networks to design more durable and flexible composite materials

Researchers at the Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Laboratory have created a new, AI-powered system for designing more durable microstructured ...

Designing Reactive AI With Spiking Neural Nets

Wolfram-like attention framing meets spiking networks: event-triggered, energy-thrifty AI that “wakes” to stimuli.
Semiconductor Engineering

Accelerating Semiconductor Innovation Through Machine Learning-Driven Modeling

Using AI and machine learning as transformative solutions for semiconductor device modeling and parameter extraction.
TMCnet

WiMi Releases Next-Generation Hybrid Quantum Neural Network Structure Technology, Breaking Through the Bottleneck of Image Multi-Classification

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, launched a hybrid quantum neural network structure (H-QNN) ...
News Medical

What are Recurrent Neural Networks?

Recurrent neural networks are a classification of artificial neural networks used in artificial intelligence (AI), natural language processing (NLP), deep learning, and machine learning. They process ...
InfoWorld

The most popular neural network styles and how they work

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...
eLife

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...