Morning Overview on MSN

Ghost lineages: The ancient DNA hiding in our genes today?

Fragments of DNA from long-extinct human relatives still circulate in modern genomes, and in some cases they do more than ...

PSCI Examines How AI Is Impacting IT Roles

Wilmington, Delaware - February 02, 2026 - PRESSADVANTAGE - PSCI shared perspective on how artificial intelligence is ...
The Lancet

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

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
IEEE

Applying Causal Machine Learning to Spatiotemporal Data Analysis: An Investigation of Opportunities and Challenges

Abstract: Traditional spatiotemporal data analysis often relies on predictive models that overlook causal relationships, making it difficult to identify true drivers and formulate effective ...
The Southern Maryland Chronicle on MSN

How are QA teams using machine learning to predict test failures in real time?

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...
Communications of the ACMOpinion

When AI Tools Train on AI Output: Model Collapse in Daily Workflows

The degradation is subtle but cumulative. Tools that release frequent updates while training on datasets polluted with ...
InfoWorld

Databricks adds MemAlign to MLflow to cut cost and latency of LLM evaluation

By replacing repeated fine‑tuning with a dual‑memory system, MemAlign reduces the cost and instability of training LLM judges ...
Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
IEEE

Defenses in Adversarial Machine Learning: A Systematic Survey From the Lifecycle Perspective

Abstract: Adversarial phenomena have been widely observed in machine learning (ML) systems, especially those using deep neural networks. These phenomena describe situations where ML systems may ...
Frontiers

MALDI-TOF MS in conjunction with machine learning: toward a new era for antimicrobial susceptibility testing

1 Department of Laboratory Medicine, Center of Infectious Diseases and Pathogen Biology, The First Hospital of Jilin University, Changchun, Jilin, China 2 College of Medical Technology, Beihua ...
Frontiers

Multimodal data for predictive medicine: algorithmic fusion of clinical data in anesthesiology and intensive care

Anesthesiology and intensive care medicine are among the most data-rich fields of medicine, where accurate and timely outcome prediction or risk stratification is important. During patient care, ...