Frontiers

Translating Machine Learning and Artificial Intelligence in Medicine: Focus on Clinical Impact

Artificial intelligence is reshaping modern medicine at an unprecedented pace. Predictive models now rival or exceed traditional clinical tools in accuracy, ...

Wet lab research and deep machine learning identify a key driver of long-term inflammatory memory

One of the most puzzling aspects of common chronic inflammatory skin diseases such as psoriasis is how they become chronic.
Tech Xplore

AI system learns to prevent warehouse robot traffic jams, boosting throughput 25%

Inside a giant autonomous warehouse, hundreds of robots dart down aisles as they collect and distribute items to fulfill a ...
Scientific Research Publishing

How Neuroscience Can Enhance Active Learning and Teaching in Practice: An Overview ()

Neuroscience, Active Learning, Teaching, Learning Process, Student Support, Effective Pedagogy through Neuroscience Share and ...
Que.com on MSN

The 2026 machine learning revolution: From generative chatbots to autonomous reasoning agents

The 2026 Machine Learning Revolution: From Generative Chatbots to Autonomous Reasoning Agents The first quarter of 2026 has marked a ...
Frontiers

Safe and Trustworthy Reinforcement Learning for Human–Robot Collaboration

Reinforcement Learning (RL) has rapidly emerged as a powerful approach for enabling robots to acquire adaptive, data-driven behaviors in real-world ...

New MiniMax M2.7 proprietary AI model is 'self-evolving' and can perform 30-50% of reinforcement learning research workflow

For direct API integration and via third-party provider OpenRouter, MiniMax M2.7 maintains a cost-leading price point of 0.30 ...
IEEE

A Comparative Analysis of Machine Learning and Deep Reinforcement Learning Approaches for Adaptive Intrusion Detection

Abstract: Cyber threats are rapidly developing, necessitating more adaptable and intelligent security frameworks for intrusion detection systems (IDS). Conventional Machine Learning (ML) methodologies ...
IEEE

LLM-Based Data Augmentation Method in Reinforcement Learning With Machine-Unlearning and Fine-Tuning

Abstract: Data augmentation in reinforcement learning (RL) aims to generate diverse and extensive datasets to enhance the learning process. Most existing studies on RL augmentation employ sample-based ...