Deep Learning for Biodiversity Conservation
Recent advances in sensor network technology, machine learning, and Big Data analytics can provide rigorous and cost-effective tools for monitoring biodiversity at scale. Conservation Metrics leverages these tools to monitor endangered species and ecosystems around the globe, and provides clients with the information needed for a data-driven approach to conservation. Matthew and David will discuss their technical approach and present several working case studies that show how deep learning can empower biologists to analyze petabytes of sensor data from microphones and cameras in remote corners of the world.
David J. Klein is the lead AI developer and advisor for Conservation Metrics. His scientific and entrepreneurial career has been devoted to developing neural-inspired learning algorithms for challenging sensor analysis applications, primarily in the auditory and visual modalities. A multiple startup veteran and advisor, David was the Algorithm Architect and Machine Learning Manager at Audience, developing brain-inspired speech analysis chips used in the iPhone and Galaxy; CTO and co-founder of BlackSwan Technologies, developing a neural-network based video CODEC; and CTO of Ersatz Labs, developing the first cloud-GPU deep learning platform. Deeply inspired by the natural world, his academic research explored the representation of complex sounds in the brain, and he developed the auditory system for a large AI entity at the Swiss Expo.02.