Tech Times

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 ...

Statistical method broadens forecasts by modeling uncertainty beyond average outcomes

When it comes to statistics, we usually expect to be informed about what happens "on average." But sometimes the key ...
JD Supra

IP Hot Topic: Incident to the Very Nature of Machine Learning – Federal Circuit Says Training Data Alone Won’t Rescue AI Patent Claims

On July 7, 2026, the Federal Circuit affirmed summary judgment that claims of two machine learning patents covering orthodontic image analysis ...
eLife

Comparing the outputs of intramural and extramural grants funded by National Institutes of Health

Funding mechanisms impact the cost effectiveness of the science conducted, as extramural NIH grants to universities excel at producing papers and citations, while intramural NIH hiring more ...
IPWatchdog

Federal Circuit Finds Deep Learning Patents Ineligible

“A deep learning device be trained on a specific subset of data is incident to the very nature of machine learning.” – Federal Circuit The U.S. Court of Appeals for the Federal Circuit (CAFC) issued a ...
inews.co.uk

The day I scared my son away from home – and learned to be a better father

I still carry a deep shame within me. As my son lay down in the middle of the stairs, refusing to move, I lost my temper. Yes ...
Phys.org

Machine-learning method maps the uncertainty of biodiversity scenarios: The Bigfoot connection

To effectively protect biodiversity in an era of climate change, ecologists first have to know where animal and plant species are located and then be able to predict what habitats will be available to ...
The Lancet

When to and when not to use machine learning in risk prediction models

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...
Frontiers

Integrating meteorological and breeding data to predict maize yields using machine learning algorithms

Accurate crop yield prediction is vital for ensuring global food security, particularly amid growing environmental challenges such as climate change. Although deep learning (DL) methods have shown ...