Former Cohere exec Sara Hooker has raised $50 million for her AI startup Adaption Labs—a bet on smaller, smarter models

Hooker says she wants to "eliminate prompt engineering" with AI models that intuitively adapt to varying tasks ...

When the State enters the cradle: How China and India are engineering early childhood for economic growth

Explore how China and India are transforming early childhood education to enhance cognitive development and secure economic ...
The Manila Times

Lake Erie College of Osteopathic Medicine Designs Medical Education Around Multiple Learning Pathways to Support Different Learning Styles

Medical education is often delivered through a single instructional format, but Lake Erie College of Osteopathic Medicine (LECOM) is taking a different approach by designing its medical curriculum ...

MemRL outperforms RAG on complex agent benchmarks without fine-tuning

MemRL separates stable reasoning from dynamic memory, giving AI agents continual learning abilities without model fine-tuning ...
BMJ Open

Generating actionable insights to support point-of-care suicide risk decision-making in a safety-net healthcare system: a machine learning approach to predicting dynamic risk ...

Background Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net ...
Hoodline

Washington State Board of Education Endorses Mastery-Based Learning Report to Legislature

The State Board of Education in Washington has officially endorsed its newest report on Mastery-based Learning (MBL), following a comprehensive demonstration project aimed to transform how education ...
IEEE

Improving Floating Offshore Wind Farm Flow Control With Scalable Model-Based Deep Reinforcement Learning

Abstract: This paper proposes a model-based deep reinforcement learning (DRL) framework to maximize the total power output and minimize the fatigue load of a floating offshore wind farm subject to ...
IEEE

Model-Based Safe Reinforcement Learning for Active Distribution Network Scheduling

Abstract: Data-driven methods, especially reinforcement learning (RL), are adept at addressing uncertainties but are poor at ensuring safety, which is a critical requirement in active distribution ...
VentureBeat

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...
unlv

The Power of Project-Based Learning: How Alum Mike Taack is Transforming Learning With WYLEES

“Is this even important?” “Why do I need to learn this?” As a middle school educator for over 15 years, Mike Taack, ‘11 M.Ed. Secondary Curriculum and Instruction, had heard those questions from his ...
marktechpost

Microsoft AI Introduces rStar2-Agent: A 14B Math Reasoning Model Trained with Agentic Reinforcement Learning to Achieve Frontier-Level Performance

Large language models have made impressive strides in mathematical reasoning by extending their Chain-of-Thought (CoT) processes—essentially “thinking longer” through more detailed reasoning steps.