適用於非營利組織的 AWS

The National Institutes of Health (NIH) Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative aims to modernize the biomedical research ecosystem by fostering innovation in biomedical research using technological advancements in the cloud. Join this session where we will have three separate presentations from representatives from the NIH, AWS, and Four Points Technology, LLC.

It has taken decades for scientists to understand natural human speech. Today, voice-activated, artificial intelligence interfaces such as Alexa, the natural language processing system by Amazon, can interact with humans at various levels. Join this webinar to learn more about the internals of the data flow within Alexa in response to a spoken command, how our natural language understanding stack works, and our investments in deep learning, automated model updates, and privacy-preserving machine learning (ML).

With artificial intelligence and machine learning on the rise, high performance computing (HPC) systems proliferating, and research teams more distributed than ever, reliable research data management is now more important than ever. We tend to take these functions for granted, and yet modern collaborative research would not be possible without them. When scientists can move and share data quickly, easily, and securely, they accelerate the pace of research and innovation. Globus has spent the past nine years developing a research data management service with over 100,000 users at thousands of organizations worldwide. Tune in to learn about what makes Globus unique, support for various compliance requirements, and common use cases, such as data migration from scientific instruments and cloud storage like Amazon S3.

Biomedical Researchers today are faced with a growing number of quantitative and qualitative methods that generate data from experiments, computational and observational studies, clinical studies and data gathered from individuals and from communities. The generation of this data is highly distributed, including individual scientists, research groups, patients and large cohort studies, and large-scale initiatives. In parallel to the rise in data generation and data use in research, computational sciences are also undergoing a metamorphosis. From new methods and tools to analyze and understand data (machine learning, deep learning, artificial intelligence, and virtual-reality technologies) to new platforms, that embrace heterogeneous computing (e.g. SUMMIT) or quantum information, the landscape of data-information-computing creates a need for a strategic vision in biomedical data science. Our aim is to provide for a connected data ecosystem that embraces the concept that data is Findable, Accessible, Interoperable and Reproducible. This seminar will engage participants to think about new ways in which we can extend our data science capabilities.