Andrej Karpathy's new open source 'autoresearch' lets you run hundreds of AI experiments a night — with revolutionary implications

An AI agent reads its own source code, forms a hypothesis for improvement (such as changing a learning rate or an architecture depth), modifies the code, runs the experiment, and evaluates the results ...
openheart.bmj

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning

Background Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and benchmarking hospital performance. This study ...
InfoQ

New Research Reassesses the Value of AGENTS.md Files for AI Coding

Despite widespread industry recommendations, a new ETH Zurich paper concludes that AGENTS.md files may often hinder AI coding agents. The researchers recommend omitting LLM-generated context files ...

Large AI models can speed catalyst discovery by predicting performance before synthesis

Artificial intelligence (AI) is transforming the way scientists discover and design new materials. In a specially invited review published in Angewandte Chemie International Edition, Tohoku University ...
Microsoft

AI as tradecraft: How threat actors operationalize AI

Threat actors are operationalizing AI to scale and sustain malicious activity, accelerating tradecraft and increasing risk for defenders, as illustrated by recent activity from North Korean groups ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
northpennnow

Machine Learning Using Python: A Complete Learning Path With Practical Projects

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
BMJ

Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China

Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...
Hosted on MSN

Can this machine take down a giant oak tree?

A powerful machine takes on a massive oak tree. Watch the process and see if it has enough strength to get the job done. #TreeRemoval #HeavyMachinery #LandClearing Wife of teacher killed in senior ...
National Academies of Sciences%2c Engineering%2c and Medicine

DOE Should Develop AI-Based Foundation Models Fused with Traditional Computational Methods to Bring Paradigm Shift to Scientific Discovery

WASHINGTON — A new report from the National Academies of Sciences, Engineering, and Medicine examines how the U.S. Department of Energy could use foundation models for scientific research, and finds ...
openaccessgovernment

Leveraging machine learning and robust small area estimation methods for stand-level forest inventory

This special report introduces small area estimation (SAE) as a modern approach for producing reliable, stand-level forest inventory information Small area estimation (SAE) is a set of statistical ...