Have you ever wondered how wild animals behave when no one's watching? Understanding these behaviors is vital for protecting ecosystems--especially as climate change and human expansion alter natural habitats. But collecting this kind of information without interfering has always been tricky. Traditionally, researchers relied on direct observation or sensors strapped to animals--methods that are either disruptive or limited in scope. Camera traps offer a less invasive alternative, but they generate vast amounts of footage that's hard to analyze.

Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual animals within the same species. In this work, we frame AIID as a hierarchical multi-label classification task and propose the use of hierarchy-aware loss functions to learn robust representations of individual identities that maintain the hierarchical relationships among species and taxa. Our results demonstrate that hierarchical embeddings not only enhance identification accuracy at the individual level but also at higher taxonomic levels, effectively preserving the hierarchical structure in the learned representations. By comparing our approach with non-hierarchical models, we highlight the advantage of enforcing this structure in the embedding space. Additionally, we extend the evaluation to the classification of novel individual classes, demonstrating the potential of our method in open-set classification scenarios.

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation. Animal ecologists are increasingly limited by constraints in data processing. Here, Tuia and colleagues discuss how collaboration between ecologists and data scientists can harness machine learning to capitalize on the data generated from technological advances and lead to novel modeling approaches.

A team of experts in artificial intelligence and animal ecology has put forth a new, cross-disciplinary approach intended to enhance research on wildlife species and make more effective use of the vast amounts of data now being collected thanks to new technology, as announced in a press release by École Polytechnique Fédérale de Lausanne (EPFL), a Swiss technology institute, which contributed to the study. The results were published in Nature Communications. The field of animal ecology has entered the era of big data and the Internet of Things. Unprecedented amounts of data are now being collected on wildlife populations, thanks to sophisticated technology such as satellites, drones and terrestrial devices like automatic cameras and sensors placed on animals or in their surroundings. These data have become so easy to acquire and share that they have shortened distances and time requirements for researchers while minimizing the disrupting presence of humans in natural habitats.

Wildlife research has gone from local to global. Modern technology now offers revolutionary new ways to produce more accurate estimates of wildlife populations, better understand animal behavior, combat poaching and halt the decline in biodiversity. Ecologists can use AI, and more specifically computer vision, to extract key features from images, videos and other visual forms of data in order to quickly classify wildlife species, count individual animals, and glean certain information, using large datasets. The generic programs currently used to process such data often work like black boxes and don't leverage the full scope of existing knowledge about the animal kingdom. What's more, they're hard to customize, sometimes suffer from poor quality control, and are potentially subject to ethical issues related to the use of sensitive data.

The field of animal ecology has entered the era of big data and the Internet of Things. Unprecedented amounts of data are now being collected on wildlife populations, thanks to sophisticated technology such as satellites, drones and terrestrial devices like automatic cameras and sensors placed on animals or in their surroundings. These data have become so easy to acquire and share that they have shortened distances and time requirements for researchers while minimizing the disrupting presence of humans in natural habitats. Today, a variety of AI programs are available to analyze large datasets, but they're often general in nature and ill-suited to observing the exact behavior and appearance of wild animals. A team of scientists from EPFL and other universities has outlined a pioneering approach to resolve that problem and develop more accurate models by combining advances in computer vision with the expertise of ecologists. Their findings, which appear today in Nature Communications, open up new perspectives on the use of AI to help preserve wildlife species.

Facial recognition technology is rapidly becoming ubiquitous, used in everything from security cameras to smartphones. But in the near future, humans may not be the only ones to be digitally captured. Researchers are training forms of artificial intelligence to recognize individual animals by their faces alone -- and even discern their emotional state just by reading their expressions. Much of the research into animal facial expressions has focused on species like dogs and horses. But some of the most cutting-edge work is aimed at an unlikely subject: the farmed hog.

Machine learning (ML) is an amazing field that enables a huge number of powerful and interesting techniques. ML is a broad field that has applications in many areas. From image processing to conservation, ML provides unique solutions to problems old and new. Here are some interesting and cool applications of machine learning. Neural networks (NNs) and deep neural networks (DNNs) are very popular machine learning techniques. This type of modeling is used in many of the best-known applications of ML. Image classification, face identification, and speech recognition are just a few examples.

Portuguese researchers have built software that accurately identifies up to 150 individual zebrafish almost 100 percent of the time by tracking their behavior, reports. Gonzalo de Polavieja, principal investigator at the Collective Behavior Lab, along with his colleagues built idtracker.ai, Idtracker.ai is composed of two convolutional neural networks that identify and track individual animals within a group. Their results were published in the journal . "The ultimate goal of our team is understanding group behavior," de Polavieja said.

AI systems excel in pattern recognition, so much so that they can stalk individual zebrafish and fruit flies even when the animals are in groups of up to a hundred. To demonstrate this, a group of researchers from the Champalimaud Foundation, a private biomedical research lab in Portugal, trained two convolutional neural networks to identify and track individual animals within a group. The aim is not so much to match or exceed humans' ability to spot and follow stuff, but rather to automate the process of studying the behavior of animals in their communities. "The ultimate goal of our team is understanding group behavior," said Gonzalo de Polavieja. "We want to understand how animals in a group decide together and learn together."