Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...

The ability of computers to learn on their own by using data is known as machine learning. It is closely related to ...

Agnik, the global leader of the vehicle analytics market, announced today that it is going to offer a wide range of Deep Machine Learning-based solutions for powering its new and existing products in ...

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

Scientists are trying to tame the chaos of modern artificial intelligence by doing something very old fashioned: drawing a ...

RLinf is a flexible and scalable open-source RL infrastructure designed for Embodied and Agentic AI. The 'inf' in RLinf stands for Infrastructure, highlighting its role as a robust backbone for ...

Researchers at the University of Science and Technology of China have developed a new reinforcement learning (RL) framework that helps train large language models (LLMs) for complex agentic tasks ...

In just three months, the crew of three young scientists overcame a swarm of challenges to achieve this groundbreaking advancement in robotic autonomy and space operations. “The APIARY team’s ...

Genome assembly remains an unsolved problem, and de novo strategies (i.e., those run without a reference) are relevant but computationally complex tasks in genomics. Although de novo assemblers have ...

The bird has never gotten much credit for being intelligent. But the reinforcement learning powering the world’s most advanced AI systems is far more pigeon than human. In 1943, while the world’s ...

Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a Synthetic Biological Intelligence (SBI) ...