Evaluation Approaches discusses the importance of evaluation-driven AI and why it’s the key to solving new problems in a variety of industries. This module also explores the validation of AI systems, including the advantages and disadvantages of each evaluation method.
Expect to learn:
- Foundational definitions for evaluation approaches in AI.
- The differences between automatic and human-in-the-loop evaluation methods.
- How to apply a set of automatic metrics to natural language processing.
Time Commitment: Approximately 2 hours and 15 minutes
Dr. Bonnie J. Dorr
Associate Director and Senior Research Scientist
Dr. Dorr is a leading researcher in the field of natural language processing and is an associate director and senior research scientist at IHMC. Dr. Dorr joined IHMC from the University of Maryland, where she is Professor Emerita in the Institute for Advanced Computer Studies and the Department of Computer Science. She holds both a master’s degree and a Ph.D. in computer science from the Massachusetts Institute of Technology and a bachelor’s degree from Boston University.
Dr. Arash Mahyari
Dr. Mahyari is a research scientist at IHMC working on machine learning and signal processing. He joined IHMC from ABB Robotics R&D Center in San Jose, CA, where he worked on developing the next generation of artificial intelligence for industrial robots. He received his dual degree in electrical engineering (Ph.D.) and statistics (MSc) from Michigan State University and a MSc in electrical engineering from Shiraz University.
Dr. Ian Perera
Dr. Perera is a research scientist at IHMC working on applying linguistic and cognitive models and knowledge towards natural language understanding and the intersection of such understanding with computer vision and other data-grounded applications. He graduated magna cum laude with a B.S.E. in digital media design at the University of Pennsylvania and obtained his Ph.D. in computer science at the University of Rochester in 2016. Dr. Perera has been at IHMC since 2013.