Data Skeptic

Ecologist Théo Michelot discusses applying Hidden Markov Models to analyze time-series data in ecology. Topics include turning GPS observations into behavioral data, complexities of modern data sets, and challenges in modeling animal movements. The podcast delves into the importance of collaboration between ecologists and statisticians for successful research.

Researcher Brian Taylor discusses magnetoreception in animals and engineering navigation methods. They explore bio-inspired engineering for Mars missions, animal navigation using Earth's magnetic field, and replicating animal sensory mechanisms in robotics. The podcast showcases the complexities and challenges of animal navigation and the interdisciplinary approach needed for effective artificial navigation systems.

Evolutionary biologist and software engineer Ben Haller discusses modeling population genetics over time, delving into natural selection sources like resources, mate preferences, and competition. Explore the capabilities of SLiM for diverse evolutionary scenarios, its impact on research, and transitioning from software engineering to evolutionary biology with a focus on modeling multiple species and ecological interactions.

Dr. Jessica Hekman discusses dog welfare, behavioral genetics, and co-evolution with humans. Topics include genetic diversity in dog breeds, the limitations of CRISPR technology, and the genetic basis of dog behavior.

Barbara Webb and Anna Hadjitofi discuss decoding the honeybee dance, from antennae movements to neural mechanisms. They explore collaborations, sensory communication, decoding insect behavior for robots, and comparing insect brains.

Podcast explores AI's role in automating animal pose tracking for research, delving into the software SLEAP. Discusses advancements in deep learning for pose tracking in computer vision, user experience enhancements for scientific software, and the relationship between biological movement and brain functionality.

Sebastien Motsch, an assistant professor at Arizona State University, discusses modeling group behavior in biological systems. Topics include challenges in applying machine learning to behavioral analysis, exploring self-organized systems in politics, modeling bird flocking behavior, and understanding emergent behaviors in simple organisms like slime molds.

Exploring data loggers to study animal behavior in challenging environments, tracking tarantulas and temperature effects on biomechanical traits, advancements in animal accelerometer technology, sensor weight balance on animals, analyzing animal behaviors with accelerometer data, and guidance for students interested in field research.

Join the discussion with a psychology professor challenging traditional views on intelligence. Explore the measurement of intelligence in various contexts, including animals. Dive into the complexities of linking neuroscience with other disciplines and the evolving field of AI.

The podcast explores animal decision-making, discussing how animals perceive information and use it for learning. Topics include costs of learning in animals, experimental evolution using fruit flies, animal intelligence in species like dolphins and bees, and evolution of preferences in flies.