Researchers have developed a technique that significantly improves the performance of large language models without ...

The infrastructure behind AI agents isn't static—it’s a living, evolving system. Designing effective data pipelines means ...

To realize business value from generative AI, start small, iterate fast and scale smart—while ensuring strong data, team ...

When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

I am a computational biologist interested in interpretable machine learning for genomics and health care. Interpretable ...

To flexibly and robustly handle diverse problems, AI systems can leverage dual-process theories of human cognition that ...

Researchers have long been interested in how humans and animals make decisions by focusing on trial-and-error behavior ...

By combining artificial intelligence with automated robotics and synthetic biology, researchers at the University of Illinois ...

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as ...

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much ...

Translator’s implementation of Statistical Machine Translation (SMT) is built on more than a decade of natural-language research at Microsoft. Rather than writing hand-crafted rules to translate ...

Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems.