Science Daily

Brain inspired machines are better at math than expected

Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The ...
IEEE

Toward Transparent Reinforcement Learning: An Explainable Deep Q-Learning Framework for Sequential Decision-Making

Reinforcement learning (RL) has demonstrated remarkable promise in sequential decision-making tasks; however, its explainability issues continue to hinder high-stakes domains that demand regulatory ...
Scientific American

Is AI on the precipice of revolutionizing math? It depends

LLMs have recently helped find solutions to a number of minor longstanding problems. But a new plan called First Proof is really putting them to the test ...
POWER Magazine

DOE Details 26 Genesis Mission AI Challenges, Targeting Nuclear Timelines, Grid Planning, and Energy Systems

The Department of Energy (DOE) has released specifications for 26 artificial intelligence (AI) challenges under its Genesis Mission that could reshape how ...
IEEE

Li-Ion Battery Doctor: A Fine-Tuned and Explainable Large-Language Model for Health Prognosis

Abstract: Health prognosis is crucial for reliable operation of Li-Ion batteries (LIB). Recently, machine learning (ML) methods have attracted significant interest for state-of-health (SOH) prediction ...