NatureLM-audio analyzes complex animal vocalizations across thousands of species with simple, natural language prompts. With state-of-the-art performance on novel benchmarks, it tackles bioacoustics tasks like species classification, life-stage prediction, and even audio captioning, opening up powerful new possibilities for animal communication research.
We’re excited to announce that we’ve secured two significant grants – a $10M grant from technology entrepreneur and LinkedIn co-founder Reid Hoffman and a $7M grant from Waverley Street Foundation, a 501(c)(3) nonprofit investing at the intersection of climate solutions and community priorities.
Sara Keen, Senior Research Scientist in Behavioral Ecology and AI, reflects on a pivotal gathering at the Santa Fe Institute titled "Animals in Translation: Creating Criteria and Frameworks for Decoding Communication in Other Species." Joined by a diverse group of researchers, writers, and artists, the event aimed to define standards for successfully deciphering non-human communication systems amidst the burgeoning intersection of machine learning and animal behavior studies.
Senior AI Research Scientist Masato Hagiwara announces the release of BirdAVES—a series of self-supervised animal vocalization encoder models specifically designed for birds. BirdAVES achieves a remarkable 20+% improvement in performance across bird-related datasets and tasks compared to the previous AVES model.
New funding from Paul G Allen Family Foundation to support development of technologies that will reconnect us to nature
Human beings are part of nature. But the vast majority of what is happening in this complex, interconnected natural world we inhabit is often beyond our ability to perceive - let alone comprehend. This disconnection from the world around us arguably represents one of the biggest obstacles humanity faces in tackling the biodiversity and climate crises.
Fortunately the exponential progress we’re seeing in AI offers new ways of looking at the world - extending our ability to perceive through technology, and finding patterns in data that will help to make sense of what is happening around us.
At Earth Species Project we are focused on harnessing these new developments in AI to gain a better understanding of animal communication because we believe it offers a unique and valuable way to connect to the rest of nature.
We’re thrilled to announce that we have just received significant new funding of $1.2 million from the Paul G Allen Family Foundation that will support these efforts.
.avif)
ESP Senior Research Scientists Benjamin Hoffman and Maddie Cusimano announce the release of Voxaboxen, a new machine learning tool for research in bioacoustics. Based upon Earth Species Project's bioacoustics foundation model AVES, Voxaboxen is designed to detect and classify animal vocalizations in recorded audio, with the high level of temporal specificity that is required for studies focused on communication behavior. In this blog post, we will give an overview of why and how we designed Voxaboxen, as well as how to apply Voxaboxen to your own data. We anticipate this new tool will support and advance research by providing a straightforward way to automate annotation of large audio files.
While scientists have been studying the behavior and communication of other species for decades, the use of AI to help accelerate these discoveries is relatively new. In partnership with FootPrint Coalition (FPC), Robert Downey Jr’s initiative to accelerate technologies for sustainable abundance, and the Experiment Foundation, ESP is thrilled to announce the launch of a new grant program designed to catalyze new and emerging research into interspecies communication.
-min.jpg)
Sara Keen, Senior Researcher, Behavioral Ecology and AI reflects on how machine learning can help us find the signals in the noise and decipher meaning from the world of invisible information that surrounds us.
This blog provides an overview of ESP's technical roadmap - offering a path forward for our proposed and ongoing scientific work, which is focused on using AI to decode on non-human animal communication.
Machine learning (ML) has proven to be a powerful tool for learning latent representations of data (LeCun et al., 2015). For example, learned representations of human languages have enabled translation between different languages without the use of dictionaries (Artetxe et al., 2017; Lample et al., 2017). More recently, ML has been able to generate realistic images based on text descriptions (DALL-E 2), as well as predict and imitate the words we speak (AudioLM).
We hypothesize that ML models can provide new insights into non-human communication, and believe that discoveries fueled by these new techniques have the potential to transform our relationship with the rest of nature. We also expect that many of the techniques we develop will be useful tools for processing data in applied conservation settings (Tuia et al., 2022).
