Browsing: learning

Researchers have found that artificial intelligence (AI) can analyze retinal photographs and identify patterns associated with several established Alzheimer’s disease risk factors—years before dementia develops.

A new study has used a type of machine learning called a neural network to reveal how different kinds of training can change how learning happens—both in machines and in living brains.

Finbold’s machine learning algorithm predicted that, after an overall substantial rally in 2026 but also a significant decline since late June, Advanced Micro Devices (NASDAQ: AMD) stock is likely to consolidate and even slightly rise relative to its latest close at $477.57.

EDITOR’S NOTE: This article is excerpted from The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. The paperback edition of this book will drop on October 27, 2026, along with a new preface. Special offer: Pre-order the paperback now and receivefree, immediate access to the audiobook.

While Palantir (NASDAQ: PLTR) stock recorded remarkable gains through August and began the September 3 regular session with a 5% upsurge, Finbold’s predictive machine learning algorithms estimate that the coming weeks will bring a rather deep correction.

A team of researchers optimized deep learning software and created software that can automatically detect migrating fish near hydropower facilities.

Polygenic risk scores (PRSs) are used to predict disease risk from genetic variants. They usually incorporate the influence of hundreds to millions of genetic variants. However, their adoption for clinical decision making is currently low, partly because historical genome-wide association studies (GWASs) have overwhelmingly evaluated European cohorts, resulting in severe accuracy drops when applied to…

German researchers have developed a new method for analysing the epigenome — the genome’s control system that determines which genes are switched on and off. The machine-learning method identifies differentially methylated DNA regions without sample labels — a prerequisite for many existing algorithms — making it possible to identify previously hidden biological patterns as well…

Analysts have historically relied on detailed, manual work within the private credit sector to spot both opportunities and risks. This includes reviewing financial statements, pulling out key figures, and then forming a view on credit risk, based on both quantitative and qualitative factors. This could take days depending on the information they needed to extract.

EDITOR’S NOTE: This is the preface to The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. The paperback edition of this book will drop on October 27, 2026, along with a new, second preface, “Predictive AI Thrives, Despite Generative AI Stealing the Spotlight.”