Control systems shape the performance of technologies from autonomous vehicles to power plants. With MATLAB and Simulink, you can virtually model, design, and optimize controllers before deploying ...

Confidence is persuasive. In artificial intelligence systems, it is often misleading. Today's most capable reasoning models ...

Friendships don’t just develop organically. Children learn through experience, reaction, and reflection, often in ways adults ...

NeurIPS NeurIPS, or Neural Information Processing Systems, is pretty much the biggest gathering for anyone serious ...

Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed.

I have eight years of experience covering Android, with a focus on apps, features, and platform updates. I love looking at even the minute changes in apps and software updates that most people would ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

In this tutorial, we build an advanced agentic Deep Reinforcement Learning system that guides an agent to learn not only actions within an environment but also how to choose its own training ...

The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for ...