AI-Powered Devices Track Howls to Save Wolves
A new cell-phone-sized device—which can be deployed in vast, remote areas—is using AI to identify and geolocate wildlife to help conservationists track endangered species, including wolves around Yellowstone National Park.
The battery-powered devices—dubbed GrizCams—are designed by a small Montana startup, Grizzly Systems. Together with biologists, they’re deploying a constellation of the devices across the Greater Yellowstone ecosystem to record audio and video of when and where wolves or wolf packs howl.
Once fully deployed, the data can help scientists and conservationists better understand wolf behavior and create new strategies for deterring wolves from attacking livestock.
Conservationists retrieve audio data from SD cards on remote recorders every few months. That data is fed into and analyzed by AI models trained using terabytes of data of howling wolves. The model—a convolutional neural network—converts the audio into a spectrogram, which analyzes the data, identifying different aspects of a wolf’s howl and geolocating where the sounds originated.
Grizzly Systems trained the model using
NVIDIA A100 Tensor Core GPUs
in the Azure cloud and PyTorch framework running
NVIDIA CUDA-X libraries
. For inferencing, they use
NVIDIA Triton Inference Server
and ONNX Runtime for model optimization, with an
NVIDIA RTX 4090
for on-prem storage of sensitive data and local inference.
Video 1. A wolf pack recorded in 2023 in Yellowstone National Park vocalizes in chorus and asynchronously
Grizzly Systems CEO, Jeff Reed, PhD, highlighted how the system monitors large tracts of land 24 hours a day, every day of the year. The devices can help perennially under-resourced wildlife managers and state and federal agencies monitor lands that often lack personnel.
The AI model can identify varied pitches and intonations of wolf vocalizations, which can carry more than six kilometers from where they originate. Knowing where a pack moves by tracking their howls can help conservationists identify a wolf’s territorial boundaries.
While the model can’t yet identify individual wolves from their howls, Reed said future iterations of the technology will have that capability.
Figure 1. GrizCam collects sounds or video, runs through an on-device thin model layer, and analyzes data on a cloud-based LLM
Today, the GrizCams make up one part of a larger conservation effort aimed at balancing competing interests in the land.
These include the small but growing wolf population in Montana, which needs wild prey for food; the billion-dollar Yellowstone eco-tourism economy, which relies upon healthy wildlife populations; and the ranchers who need to protect their livestock, and whose land offers critical habitat for wildlife.
“Wolves, grizzlies, elk can be a hassle to a rancher because they might kill their livestock, or tear down their fences,” said Reed, who before starting up Grizzly Systems three years ago, spent his career working in the tech industry. “On the flip side, those ranches also provide critical habitat for wildlife on private lands around Yellowstone.
“If our devices can detect a lone wolf coming through a ranchland because we have AI on it, then we can playback the sound of guardian dogs barking, or a gunshot, or a large territorial wolf pack, which can “encourage” that wolf to move out of that area. But that requires vigilance throughout the day and the night—and nobody is sitting outside 24/7—which is where AI comes in.”
Another way AI is helping conservationists is by streamlining the data collection process.
The remote recorders—which can also be deployed with video capabilities—run a very thin-layer AI on-device, which weeds out most motion that would otherwise trigger false-positive recordings. The recorders can ignore wind rustling through grass or trees, or bright light reflecting off snow—two common stimuli that trigger false-positive recordings on remote devices.
As a result, the GrizCam’s batteries last longer and require less servicing by wildlife managers and landowners.
AI is also useful to conservationists as it quickly sifts through terabytes of recorded data to quickly identify and flag relevant audio or visual signatures.
While the on-device AI cuts down on unwanted recordings, it nevertheless records sounds and imagery of biological activity—including birds, elk, or bears moving across terrain and making noises.
“These acoustic recorders are gathering data with AI, they’re recording 24-7, every day for a year across 50 or so recorders,” said Reed. “With AI, we can crunch through the data, go through and identify wolves or other endangered species if they’re there, and then work with conservationists to say, ‘okay, we gotta go protect this area and do some additional conservation over there.’”
Grizzly Systems plans to continue its close collaboration with conservationists. It also foresees its rugged edge devices’ relevance for a variety of industrial use cases, including remote surveillance.
Reed points out that 97% of the Earth’s surface lacks access to an electrical outlet. A rugged recorder can monitor oil and gas rigs, as well as remote electrical transformers, which, in very rural areas, can attract vandals who take them offline.
“AI is a great example of how, if we can get it right, with battery life and ruggedness, we can monitor illegal activity that hurts us all,” Reed said. “Poaching, illegal wildlife trafficking, illegal logging or mining in the Amazon— this is activity that ends up hurting the vast majority of people and the planet–and which technology can help prevent.”
Read more about the
partnership
between Grizzly Systems and Yellowstone National Park.
Check out additional reporting on
wolf conservation
and
decoding wolf verbalizations
.
AI 驱动的设备追踪嚎叫以拯救狼群
一种可部署在广大偏远地区的手机大小的新型设备正在使用人工智能识别和地理定位野生动物，以帮助保护主义者追踪濒危物种，包括黄石国家公园周围的狼群。
这款名为 GrizCams 的电池供电设备由蒙大拿州的一家小型初创公司 Grizzly Systems 设计。他们与生物学家合作，在整个大黄石生态系统中部署一系列设备，以录制有关狼或狼群叫的时间和地点的音频和视频。
一旦完全部署，这些数据可以帮助科学家和环保人士更好地了解狼的行为，并制定新的策略来阻止狼攻击牲畜。
环保人士每隔几个月就会在远程录音机上从 SD 卡中检索音频数据。这些数据由使用数 TB 的狼嚎叫数据训练的 AI 模型输入和分析。该模型是一种卷积神经网络，可将音频转换为频谱图，然后分析数据，识别狼嚎叫的不同方面，并对声音的来源进行地理定位。
Grizzly Systems 使用 Azure 云中的
NVIDIA A100 Tensor Core GPU
和运行
NVIDIA CUDA-X 库
的 PyTorch 框架训练模型。在推理方面，他们使用
NVIDIA Triton 推理服务器
和 ONNX Runtime 进行模型优化，并使用
NVIDIA RTX 4090
在本地存储敏感数据和进行本地推理。
视频 1.2023 年录制的黄石国家公园狼群以合唱异步发声
Grizzly Systems 首席执行官杰夫·里德（Jeff Reed）博士强调了该系统如何全年 24 小时监控大片土地。这些设备可以帮助长期资源不足的野生动物管理者以及州和联邦机构监控往往缺少人员的土地。
AI 模型可以识别狼发声的不同音高和音调，这些音高和音调距离发声地可能超过 6 公里。通过跟踪狼的叫声了解狼群的移动位置，可以帮助环保人士识别狼群的领地界限。
虽然该模型还无法从狼嚎声中识别出单个狼，但 Reed 表示，未来的技术迭代将具备这种能力。
图 1.GrizCam 收集声音或视频，在设备上的薄模型层中运行，并在基于云的 LLM 上分析数据
如今，GrizCams 参与了更大规模的保护工作，旨在平衡土地上相互竞争的利益。
这些群体包括：蒙大拿州的狼群虽然很小，但不断增长，需要依靠野生猎物为食；价值数十亿美元的黄石生态旅游经济，依赖于健康的野生动物数量；以及需要保护牲畜的牧场主，他们的土地为野生动物提供了重要的栖息地。
Reed 说：“狼、灰熊、鹿可能会给牧场主带来麻烦，因为它们可能会杀死牲畜，或拆掉围栏。”在三年前创办 Grizzly Systems 之前，Reed 一直在科技行业工作。“另一方面，这些牧场也为黄石周围私人土地上的野生动物提供了重要的栖息地。
“如果我们的设备因为搭载了 AI 技术而能够检测到一只孤狼穿过牧场，那么我们就可以播放护卫犬的狂吠声、枪声或大型领地狼群的声音，这可能会“鼓励”那只狼离开该区域。但这需要日日夜夜保持警觉，并且没有人全天候坐在室外，这是 AI 的用武之地。”
AI 帮助环保人士的另一種方式是简化数据收集流程。
这些远程录像机还可以通过视频功能部署，它在设备上运行非常薄的 AI 层，从而淘汰了原本会触发误报录像的大多数动作。录像机可以忽略穿过草地或树木的风沙声，或从雪地反射的强光——这两种常见的刺激因素会在远程设备上触发误报录像。
因此，GrizCam 的电池续航时间更长，并且野生动物管理员和土地所有者需要的维护量减少。
AI 对环保人士也很有用，因为它可以快速筛选数 TB 的记录数据，快速识别和标记相关的音频或视频签名。
虽然设备上的 AI 会减少不必要的录音，但它会记录生物活动的声音和图像，包括在地形上移动并制造噪音的鸟、麋鹿或熊
“这些声学记录器使用人工智能收集数据，它们每天 24-7 小时记录 50 个左右的记录器，”Reed 说。“借助人工智能，我们可以处理数据，浏览并识别狼或其他濒危物种（如果它们在那里），然后与环保人士合作，说‘好，我们必须保护这个地区，并在那里做一些额外的保护工作。’”
Grizzly Systems 计划继续与环保人士密切合作，并预计其坚固耐用的边缘设备适用于各种工业用例，包括远程监控。
Reed 指出，地球表面的 97% 地方没有电源插座。坚固耐用的记录仪可以监控油气钻井平台以及远程电力转换器，在非常偏远的地区，这些设施可能会吸引破坏者将其关闭。
Reed 说：“AI 是一个很好的例子，说明在电池续航时间长、坚固耐用的情况下，如果我们能够做到正确，我们就可以监控伤害我们所有人的非法活动。在亚马逊河上的盗猎、非法野生动物走私、非法采伐或采矿 – 这种活动最终会伤害到绝大多数人和地球，而技术可以帮助预防。”
详细了解 Grizzly Systems 与黄石国家公园
之间的合作
。
查看有关
狼保护
和
解码狼语
的其他报道。