Better data annotation—more accurate, detailed or contextually rich—can drastically improve an AI system’s performance, ...

While multiple machine learning (ML) algorithms offered similar predictive performance, the cost-effective analysis revealed ...

This is no longer a purely conceptual argument. Research shows that increasingly large models are already showing a ...

Successful AI agents require enterprises to orchestrate interactions, manage shared knowledge and plan for failure.

By building stronger reasoning skills in AI systems, new approaches to data for training and testing will open a door to the ...

Microsoft's recent release of Phi-4-reasoning challenges a key assumption in building artificial intelligence systems capable ...

S3 decouples RAG search from generation, boosting efficiency and generalization for enterprise LLM applications with minimal data.

Want more charts like these? See our charts on the secrets of the jobless recovery, the richest 1 percent of Americans, and how the superwealthy beat the IRS. How Rich Are the Superrich? A huge sh ...

Recently, reinforcement learning (RL) algorithms have been demonstrated successfully for ... we propose a multiagent based RL (MA-RL) framework to tackle this issue. Particularly, we (i) partition the ...

In this paper, we propose RL-MUL, a multiplier design optimization framework based on reinforcement learning. Specifically, we utilize matrix and tensor representations for the compressor tree of a ...