Artificial brains could point the way to ultra-efficient supercomputers

New research from Sandia National Laboratories suggests that brain-inspired neuromorphic computers are just as adept at ...
Morning Overview on MSN

LLMs have tons of parameters, but what is a parameter?

Large language models are routinely described in terms of their size, with figures like 7 billion or 70 billion parameters ...
New Scientist

Quantum neural network may be able to cheat the uncertainty principle

Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum ...
TDAN.com

Using Artificial Intelligence and Machine learning to Improve Data Quality in Cloud Native Data Pipelines

Introduction: Why Data Quality Is Harder Than Ever Data quality has always been important, but in today’s world of ...

These Hearing Aids Will Tune in to Your Brain

Imagine you’re at a bustling dinner party filled with laughter, music, and clinking silverware. You’re trying to follow a ...
Science Daily

AI may not need massive training data after all

New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers ...
Tech Xplore

No-code machine learning development tools

Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited ...
IEEE

Anomaly Detection in Industrial Machine Sounds Using High-Frequency Features and Gate Recurrent Unit Networks

Abstract: Detecting anomalies in industrial sound is critical for maintaining operational efficiency, preventing costly equipment failures, and ensuring workplace safety. However, it presents ...
Learn With Jay on MSN

Build a deep neural network from scratch in Python

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write ...
Semiconductor Engineering

Accelerating Semiconductor Innovation Through Machine Learning-Driven Modeling

The semiconductor industry is entering an era of unprecedented complexity, driven by advanced architectures such as Gate-All-Around (GAA) transistors, wide-bandgap materials like GaN and SiC, and ...
IEEE

Machine Learning Techniques for Stock Market Prediction: Neural Networks and Soft Computing Approaches

Abstract: This paper provides an updated overview of recent literature on stock market prediction using machine learning methods. Neural network-based models, particularly those focused on predicting ...