This SCINet working group aims to explore the potential benefits of Artificial Intelligence (AI) in animal behavior research. The working group will provide an open platform where participants can share knowledge, discuss challenges, and explore opportunities for leveraging high-performance computing resources through SCINet to advance animal behavior research. By leveraging cutting-edge AI techniques, we aim to tackle the potential limitations of traditional methods, including challenges with handling large datasets and the computational demands of advanced analyses.
A key factor enabling the application of AI to animal movement analysis is the large volume of data typically acquired in long-term GPS animal tracking studies (e.g., livestock spatial behavior studies within Long-Term Agroecosystem Research (LTAR) network). This rich resource provides an unparalleled opportunity for training and validating AI models. The large-scale nature of these data is particularly suited to Deep Neural Networks (DNNs), which excel at learning from complex, nuanced datasets without overfitting.
Recent rapid evolution of DNNs holds significant promise directly applicable to animal movement analysis. Notably, transformers, such as Generative Pre-trained Transformers (GPT), have demonstrated exceptional capabilities in handling long-context sequential information for language modeling. The transferability of the well-established techniques from natural language processing to animal movement analysis would be a compelling research topic to assess in our working group.
The ultimate goal of the working group is to utilize large datasets, cutting-edge AI techniques, and collaborative expertise to unlock new insights into animal movement pattern analyses. A deeper understanding of animal behavior, including movement patterns, habitat use, and interactions, is crucial for effective wildlife conservation and sustainable agroecosystem management.
If you are interested and would like to join, please fill out the Animal Behavior AI Working Group survey.
For more information, please contact John Y. Park., SCINet Postdoctoral Fellow (john.park3@usda.gov).