The mission of the nonprofit WPS (Wildlife Conservation Program) is to use technology to protect endangered species and ecosystems. To this end, the wpsWatch platform is being built using artificial intelligence on remote camera images. Support the installation of more cameras in critical wildlife habitats around the world and expand their work from identifying threats, classifying species and assisting with anti-poaching to preventing human-wildlife conflicts.
The organization captures more than 25,000 photos from cameras every day. “No one can look at every image and immediately understand what’s in it, but it’s critical to our operations,” said Matt Hron, director of product and accounts at Wildlife Solutions. “That’s what AI can do. arrive.
Beyond the scope of this image analysis work, the organization is expanding the use of the technology into other application areas. It requires the ability to quickly build and deploy new AI models to meet the varying needs of these efforts.
The organization's wpsWatch platform analyzes and monitors massive amounts of images from remote cameras located at more than 100 sites in nearly 20 regions. It is powered by Microsoft Azure VMs (virtual machines) and NVIDIA GPUs (graphics processing units) and is initially focused on security and anti-poaching goals within the organization's mission.
To this end, WPS worked with the Microsoft AI for Earth team to provide images for MegaDetector, an AI model developed by the AI for Earth team to accelerate the detection of surveillance camera images. deal with. It’s a mutually beneficial relationship, with WPS using MegaDetector to help enhance and refine its wpsWatch monitoring solution, which provides image injection that contributes to the continuous improvement of the model. WPS provides its services and platform to protected areas free of charge.
Use MegaDetector, a computer vision anomaly detection model, to detect animals, people, and vehicles in your camera. It is designed in a way that supports the organization's desire to achieve new goals. “Because it is a standards-based application, many of our field users are able to choose the right hardware based on their specific needs,” said WPS executive director Eric Schmidt. “It gives us the flexibility to use a variety of smart way to adapt third-party systems so that we can work with those systems to make them more like a closed ecosystem."
MegaDetector A new version (V5) was released last year, and WPS saw improvements in accuracy immediately after implementing the new version.
One performance metric of the wpsWatch platform is the time it takes from receiving an image to identifying what triggered the image. Once the image is received, it takes seconds to obtain the AI inference data to understand what is in the photo. MegaDetector v5 runs on the infrastructure and image analysis runs 50% to 60% faster than before. Specifically, the average processing time using MegaDetector v4 is about 2 seconds. The average time for new versions is between 500 and 700 milliseconds. "This is a big improvement, especially as we dramatically increase the number of images analyzed," said James Goodheart, software developer at WPS. Another enhancement using v5 is to improve accuracy. "We put out some older images that may not have been detected or were flagged for retraining. Some have been successfully detected in newer versions of the AI," Goodheart said.
In addition to image analysis, WPS uses other Microsoft infrastructure elements in its platform. For example, when the remote camera starts up, image data is emailed from the remote camera via the SendGrid service and then parsed using the WPS API. (The time required to transfer images varies depending on available communication services. Most use local mobile services, while some cameras connect over Wi-Fi.)
Photos use Microsoft Azure Blob storage , the metadata is delivered to WPS by Microsoft SQL Server. The photos are then forwarded to various AI image recognition solutions to determine what is in the photo, such as a vehicle, a person or an animal species of interest, which can then alert relevant teams on site based on what is in the image.
Expand into new domains
One area where WPS hopes to leverage its AI infrastructure is to support its efforts in preventing human-wildlife conflicts. This requires the ability to look for species in images and then recognize that, for example, elephants may be traveling along corridors toward human settlements, where they may be damaging crops. Or look for lions or wolves approaching livestock areas and alert locals to take precautions.
Additionally, WPS is conducting more detections of invasive species. What is needed is the ability to monitor rats, cats, dogs, goats or any local invasive species, combined with appropriate methods to ensure that there are no further invasions in the area. In each case, WPS uses the same app and camera to find the threat, whether it's a human or wildlife.
WPS wants to do all it can to encourage people to engage with global wildlife issues. “One of the really exciting things is that people around the world are able to directly participate in international wildlife conservation. With the tools we provide, anyone can monitor our data as a volunteer and become a contributor to poaching incidents and wildlife conservation around the world. First Responders to Animal Crime." Through cloud-based technology and data flows, individuals can have a global impact. Everyone can make an impact for nature conservation around the world.
The above is the detailed content of Artificial intelligence helps protect endangered species. For more information, please follow other related articles on the PHP Chinese website!

机器学习是一个不断发展的学科,一直在创造新的想法和技术。本文罗列了2023年机器学习的十大概念和技术。 本文罗列了2023年机器学习的十大概念和技术。2023年机器学习的十大概念和技术是一个教计算机从数据中学习的过程,无需明确的编程。机器学习是一个不断发展的学科,一直在创造新的想法和技术。为了保持领先,数据科学家应该关注其中一些网站,以跟上最新的发展。这将有助于了解机器学习中的技术如何在实践中使用,并为自己的业务或工作领域中的可能应用提供想法。2023年机器学习的十大概念和技术:1. 深度神经网

实现自我完善的过程是“机器学习”。机器学习是人工智能核心,是使计算机具有智能的根本途径;它使计算机能模拟人的学习行为,自动地通过学习来获取知识和技能,不断改善性能,实现自我完善。机器学习主要研究三方面问题:1、学习机理,人类获取知识、技能和抽象概念的天赋能力;2、学习方法,对生物学习机理进行简化的基础上,用计算的方法进行再现;3、学习系统,能够在一定程度上实现机器学习的系统。

本文将详细介绍用来提高机器学习效果的最常见的超参数优化方法。 译者 | 朱先忠审校 | 孙淑娟简介通常,在尝试改进机器学习模型时,人们首先想到的解决方案是添加更多的训练数据。额外的数据通常是有帮助(在某些情况下除外)的,但生成高质量的数据可能非常昂贵。通过使用现有数据获得最佳模型性能,超参数优化可以节省我们的时间和资源。顾名思义,超参数优化是为机器学习模型确定最佳超参数组合以满足优化函数(即,给定研究中的数据集,最大化模型的性能)的过程。换句话说,每个模型都会提供多个有关选项的调整“按钮

截至3月20日的数据显示,自微软2月7日推出其人工智能版本以来,必应搜索引擎的页面访问量增加了15.8%,而Alphabet旗下的谷歌搜索引擎则下降了近1%。 3月23日消息,外媒报道称,分析公司Similarweb的数据显示,在整合了OpenAI的技术后,微软旗下的必应在页面访问量方面实现了更多的增长。截至3月20日的数据显示,自微软2月7日推出其人工智能版本以来,必应搜索引擎的页面访问量增加了15.8%,而Alphabet旗下的谷歌搜索引擎则下降了近1%。这些数据是微软在与谷歌争夺生

荣耀的人工智能助手叫“YOYO”,也即悠悠;YOYO除了能够实现语音操控等基本功能之外,还拥有智慧视觉、智慧识屏、情景智能、智慧搜索等功能,可以在系统设置页面中的智慧助手里进行相关的设置。

人工智能在教育领域的应用主要有个性化学习、虚拟导师、教育机器人和场景式教育。人工智能在教育领域的应用目前还处于早期探索阶段,但是潜力却是巨大的。

阅读论文可以说是我们的日常工作之一,论文的数量太多,我们如何快速阅读归纳呢?自从ChatGPT出现以后,有很多阅读论文的服务可以使用。其实使用ChatGPT API非常简单,我们只用30行python代码就可以在本地搭建一个自己的应用。 阅读论文可以说是我们的日常工作之一,论文的数量太多,我们如何快速阅读归纳呢?自从ChatGPT出现以后,有很多阅读论文的服务可以使用。其实使用ChatGPT API非常简单,我们只用30行python代码就可以在本地搭建一个自己的应用。使用 Python 和 C

人工智能在生活中的应用有:1、虚拟个人助理,使用者可通过声控、文字输入的方式,来完成一些日常生活的小事;2、语音评测,利用云计算技术,将自动口语评测服务放在云端,并开放API接口供客户远程使用;3、无人汽车,主要依靠车内的以计算机系统为主的智能驾驶仪来实现无人驾驶的目标;4、天气预测,通过手机GPRS系统,定位到用户所处的位置,在利用算法,对覆盖全国的雷达图进行数据分析并预测。


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Dreamweaver Mac version
Visual web development tools

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

Notepad++7.3.1
Easy-to-use and free code editor

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

SublimeText3 Mac version
God-level code editing software (SublimeText3)
