The next major evolution will come from multi-agent systems—networks of smaller, specialized AI models that coordinate across ...

Most current autonomous driving systems rely on single-agent deep learning models or end-to-end neural networks. While ...

ADELPHI, Md.-- Army researchers are collaborating to enhance multi-agent teaming capabilities for the Soldier that will lead to improved situational awareness and communication capabilities on the ...

According to a Deloitte survey, nearly 60% of the AI leaders and representatives are struggling with adopting AI agents, primarily due to integrating with legacy systems and addressing risk and ...

The biggest challenge to AI initiatives is the data they rely on. More powerful computing and higher-capacity storage at lower cost has created a flood of information, and not all of it is clean. It ...

Multi-Agent Reinforcement Learning (MARL) is an emerging subfield of artificial intelligence that investigates how multiple autonomous agents can learn collaboratively and competitively within an ...

AI agents hold much promise, with some saying they will revolutionize the workplace itself. But they can be a bit concept-y, and enterprises don’t always know where to begin. One-year-old startup ...

As AI-assisted coding becomes more common, a new pattern is emerging: multi-agent workflows. A multi-agent workflow refers to using various AI agents in parallel for specific software development life ...