Tech Times on MSN

Brain-like learning rule cracks convolutional networks: Sakana AI measures the biology tax

Biologically plausible learning now reaches 96.7% on MNIST and 61.7% on CIFAR-10 without backpropagation, as Sakana AI ...
Phys.org

Using geometry and physics to explain feature learning in deep neural networks

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Optical Computing: Random-projection optical neural networks printed at record speeds

Energy-efficient visible-band intelligent vision processors demands both optical neural network (ONN) design and high-speed nanofabrication advances. Chinese researchers proposed a random-projection ...
TMCnet

WiMi Releases Resource-Efficient Quantum Convolutional Neural Network Based on QRAM, Accelerating the Practical Implementation of Large-Scale Image Classification Applications

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the release of its latest independently developed ...
Art of the Problem on MSNOpinion

The second computing revolution, why neural networks replaced 70 years of human-written rules

For decades, computers did exactly what humans told them, until a biologically inspired idea quietly made that approach ...
Phys.org

Self-learning neural network cracks iconic black holes

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...
Ars Technica

How a stubborn computer scientist accidentally launched the deep learning boom

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...
Nanowerk

Nanoprinting produces four million optical neurons in 15 minutes

A parallel fabrication method creates scalable optical neural networks for fast, accurate machine vision while sharply ...
MIT Technology Review

The next generation of neural networks could live in hardware

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...
Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...