The pace of new research in the healthcare and pharmaceutical industries is astounding, and while this progress promises to uncover new disease correlations and treatment methods, it also produces massive amounts of often disparate data, complicating the path to discovery. Researchers need to be able to ask questions of the vast and rapidly growing bodies of biomedical research and to get the answers to their queries in close to real time.

Natalia Vassilieva will discuss how AI can help make sense of the vast amount of data in healthcare by building models for biomedical language understanding. She will share specific examples of NLP models that Cerebras developed with customers, who are using Cerebras’ industry-leading AI compute systems to train large transformer-style models from scratch, reducing both training time and power consumption for tasks such as biomedical text mining and rapid, large-scale medical literature search, a critical capability for advancing drug discovery.