Presentation Details
| Conceptual advances from the application of machine learning in the study of animal behavior: an introduction Emily DuVal. Florida State University, Tallahassee, FL, USA |
Abstract
Machine learning and artificial intelligence are new analytical tools changing the way we approach complex, interconnected problems in animal behavior. They are also a key part of changing funding priorities that offer distinct opportunities for researchers in the field of animal behavior. In this symposium introduction, I will provide a short overview of AI and ML approaches, from early predictive models and image-based deep learning to current frontiers of multimodal and foundation models that integrate behavior across data types. I will then connect these general and modern analytical approaches to the central aims and guiding principles of the study of animal behavior. Rather than focusing on the underlying mathematics, this talk will focus on establishing a general foundation for considering how these new methodologies support discovery, hypothesis generation, pattern finding, and decision-making across multiple data types in animal behavior. Subsequent talks in the symposium will illustrate applications of these approaches, highlight conceptual advances that they are producing, and consider the current frontiers for the applications in the field of animal behavior.
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No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.
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