Data Skeptic
A long-running weekly podcast that explores data science, machine learning, and AI through the lens of skeptical inquiry. Host Kyle Polich covers a wide range of topics – from interviews with AI researchers about new algorithms, to mini-series on subjects like natural language processing, time series, or GANs. Data Skeptic balances technical depth with clarity, making complex concepts approachable. Listeners gain both an understanding of cutting-edge techniques and a healthy skepticism toward hype, as Kyle and guests discuss real-world applications, limitations, and ethical dimensions of AI and data science.
Biodiversity Monitoring
The post interviews Hager Radi about the complexities of biodiversity monitoring, emphasizing that beyond simple organism counts, data gaps and bias hinder accurate modeling. It highlights challenges such as scarce observations for many species and the difficulty of estimating distributions with incomplete data. Hager’s approach leverages machine learning and remote sensing to predict species ranges despite limited samples, showcasing the tools she’s developed to address these issues.
Hacking the Colony
The post highlights Ashay Aswale and Tony Lopez’s research on swarm robotics inspired by ant colonies, emphasizing challenges such as lost pheromone trails and misinformation from rogue agents. It argues that distributed swarms offer redundancy and the possibility of caste-like...
Primate Poses
The post highlights how researchers are applying machine learning to automate behavioral observations of primates, focusing on PhD student Richard Vogg’s multi‑camera system for tracking lemurs and macaques solving puzzle boxes in the wild. It explains that multi‑camera setups improve...
