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Xavier Health AI Summit 2020
August 25 - August 27
Agenda Highlights 2019
Actionable insights on these topics and more:
As we look to the future of the pharmaceutical and medical device industries, FDA and industry must continue to work together to move beyond our current paradigms of discovery, research and development and operations. FDA is actively engaged in exploring the power of artificial intelligence on many fronts. Walk away with an understanding of FDA’s vision for the use of AI across the industry.
Foundation of AI
Continue to deepen your understanding of “how it works” through foundational topics that could include “how machine learning works,” “how to validate AI,” “how natural language processing works,” and “what is naturals language generation?”. These types of topics will open your mind as to how your organization can take advantage of the power of AI even beyond how it is being used today. Be the champion to advance your company!
AI in Action
What is AI? How are AI systems built? How do we unpack knowledge from AI algorithms? Far from being indecipherable “black boxes,” you will instead find that building AI systems follows a replicable and intuitive process that is especially suited for implementation across the industry. Be confident in how your organization can implement AI!
Live AI Technology Demos
Participants will take a guided tour of technology that can be used to implement AI in your organization. These small-group demos also will show you AI in action and will give you a chance to ask your questions.
Putting AI Into Motion Across GxP Organizations
Whether you are already using the power of AI in your organization, or just getting started, this workshop will support your ability to advance the impact of your results. Spend the day during this hands-on pre-summit workshop diving into the challenges, opportunities and successful practices of machine learning, natural language processing, natural language generation. Gain a deeper understanding of how to increase the success of the outcomes, decrease the risk of decisions you make from the algorithms, and increase the confidence you have in relying on the results.
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