Blog

  • From Frog Calls To Global Conservation: Scaling Citizen Science With AI

    From Frog Calls To Global Conservation: Scaling Citizen Science With AI

    The Growing Challenge of Biodiversity Data Citizen science projects are generating more data than ever before. Global biodiversity databases now contain millions of species observations, creating an incredible resource for ecological research. While collecting the data is not a problem, researchers still need to analyze the sounds hidden within thousands or even millions of recordings.…

  • Our 2025 Annual Report: A Year of Momentum for Interspecies Understanding

    Our 2025 Annual Report: A Year of Momentum for Interspecies Understanding

    Our 2025 Annual Report The past few years have seen a steady drumbeat of discoveries that illustrate the complex communications and culture of other animals, many of them accelerated by significant advances in technology. We are proud to be part of this incredible progress in understanding our fellow species. Our 2025 Annual Report highlights the…

  • From What to Who? AI Learns to Recognize Individual Animals

    From What to Who? AI Learns to Recognize Individual Animals

    Individual ID in Chatty Species In the wild, zebra finches don’t sing in isolation. They move through dynamic social groups, calling and responding to one another across space and time. For ecologists, understanding this communication requires knowing who is communicating with whom, and how information flows through a group. Tracking communication at the level of…

  • What Can a Primate Face Tell Us? A Scalable Approach to Studying Facial Expressions

    What Can a Primate Face Tell Us? A Scalable Approach to Studying Facial Expressions

    Anyone who has spent time observing primates knows how expressive their faces can be. A lip-smack, a brief glance, or a subtle shift of the mouth can signal curiosity, tension, submission, or affiliation. For ethologists, these fleeting expressions offer a rich window into the social dynamics of a group. Studying them, however, has traditionally required…

  • Animal Language Processing: An AI Convergence In Animal Communication

    Animal Language Processing: An AI Convergence In Animal Communication

    We are starting to see the study of animal communication shift from narrowly defined, species-specific, and hypothesis-limited analyses toward data-driven discovery at scale.  We refer to this convergence as Animal Language Processing (ALP), an approach that uses AI to study non-human communication at scale.

  • We’re Hiring! Director of Development

    We’re Hiring! Director of Development

    About Earth Species Project Earth Species Project (ESP) is a non-profit using frontier AI to decode animal communication. We believe the exponential progress we’re seeing in AI offers new ways of looking at the world and expanding the ability of human beings to learn from other species. Our hope is that this will make a…

  • Introducing Earth Species Project’s New CEO

    Introducing Earth Species Project’s New CEO

    Earth Species Project welcomes Steven VanRoekel as CEO. In this Q&A, Steve shares his vision for decoding animal communication with AI, his personal connection to wildlife, and what the next phase of growth looks like for ESP.

  • Unlocking Avian Secrets: How Tiny Biologgers Are Revealing the Hidden Communication of Carrion Crows

    Unlocking Avian Secrets: How Tiny Biologgers Are Revealing the Hidden Communication of Carrion Crows

    Lightweight biologgers and ESP’s machine learning tools reveal the hidden vocal world of wild carrion crows, capturing over 127,000 calls from quiet murmurs to long-distance signals.

  • What the World Thinks About AI and Animal Communication: Findings from Our First Global Survey

    What the World Thinks About AI and Animal Communication: Findings from Our First Global Survey

    The first global survey on AI and animal communication reveals striking consensus: people worldwide recognize animals’ complex inner lives and demand technology that fosters connection, not exploitation.

  • What Matters for Bioacoustic Encoding: A Practical Training Recipe for Building Generalizable Models

    What Matters for Bioacoustic Encoding: A Practical Training Recipe for Building Generalizable Models

    To better understand what matters for building a generalizable bioacoustic encoder, we tested 19 models across 26 datasets and a new evaluation benchmark. Our main finding is that a two-stage training approach—self-supervised pre-training followed by supervised post-training, both on a mix of bioacoustic and general audio—delivers the strongest performance.