Challenging traditional meritocratic models, this research calls for redefining the purpose of education in the age of AI. It argues that systems based on ranking and standardized testing ignore deep social inequalities and are misaligned with a world shaped by artificial intelligence and complexity. Instead, the authors propose an education model rooted in human interdependence—emphasizing collaboration, empathy, and creativity as essential skills for thriving in an interconnected, uncertain, and rapidly evolving global landscape.

Researchers from Nanjing University and UC Berkeley have unveiled a clustering-based reinforcement learning framework that balances novelty and reward to accelerate and enhance AI exploration across robotics, gaming, and real-world applications.

Researchers have developed a self-tuning AI framework that dynamically filters noisy graph data to boost reliability and accuracy across industries from healthcare to finance.