Science

New research introduces a method for aligning ambiguous user requests with the specific preferences of AI tool retrieval systems, dramatically improving performance.

A new deep learning framework demonstrates that strategically filtering connections can dramatically improve the scalability and performance of recommender systems.

Researchers are developing systems that allow drivers to issue open-ended instructions to autonomous vehicles, moving beyond pre-defined commands.

Researchers are exploring how to imbue robots with the ability to predict actions by leveraging neural processes that mimic the human mirror neuron system.

Researchers are leveraging artificial intelligence to design the next generation of specialized AI chips, dramatically improving performance and efficiency.

A new theory, ‘Agentivism,’ argues that effective human-AI collaboration isn’t about offloading tasks, but about strategically delegating to artificial intelligence and internalizing the resulting capabilities.

Researchers are exploring new architectures that mimic the brain’s cognitive processes to create AI systems capable of sustained, self-directed reasoning.

A new approach tackles the cold-start problem in federated recommendation systems by utilizing rich textual descriptions of items instead of relying solely on sparse interaction data.

A novel approach uses generative AI to automatically build production code directly from test cases and natural language, potentially reshaping how software is created.

A new approach uses self-directed software agents to streamline high-throughput materials screening on the most powerful supercomputers.