AI Models Are Starting to Learn by Asking Themselves Questions

An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence ...
Physics World

Reinforcement learning could help airborne wind energy take off

Machine learning technique teaches power-generating kites to extract energy from turbulent airflows more effectively, ...
Electronics360

Wind turbine control systems: From PID to reinforcement learning

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...

TII’s Falcon H1R 7B can out-reason models up to 7x its size — and it’s (mostly) open

According to TII’s technical report, the hybrid approach allows Falcon H1R 7B to maintain high throughput even as response ...
NextBigFuture

Nvidia CEO Jensen Huang CES 2026 Keynote – Next Gen Rueben GPU in Full Production. 5X Blackwell FP

Connect X9 (1.6 TB/s bandwidth), Bluefield 4 DPU (offloads storage/security), NVLink 6 switch (scales 72 GPUs as one), Spectrum X Ethernet Photonix (512 lanes, 200 Gbit optics for AI factories).
Tech Xplore

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Unite.AI

The Reinforcement Gap: Why AI Excels at Some Tasks but Stalls at Others

Artificial Intelligence (AI) has achieved remarkable successes in recent years. It can defeat human champions in games like Go, predict protein structures with high accuracy, and perform complex tasks ...
IEEE

A Collaborative Multi-Agent Deep Reinforcement Learning-Based Wireless Power Allocation With Centralized Training and Decentralized Execution

Abstract: Despite the success of Deep Reinforcement Learning (DRL) in radio-resource management within multi-cell wireless networks, applying it to power allocation in ultra-dense 5G and beyond ...
IEEE

Human-Guided Deep Reinforcement Learning for Optimal Decision Making of Autonomous Vehicles

Abstract: Although deep reinforcement learning (DRL) methods are promising for making behavioral decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to ...