什么是边缘人工智能
Edge AI is one of the most notable new sectors of artificial intelligence, and it aims to let people run AI processes without having to be concerned about privacy or slowdowns due to data transmission. Edge AI is enabling greater, more widespread use of AI, letting smart devices react quickly to inputs without access to a cloud. While that’s a quick definition of Edge AI, let’s take a moment to better understand Edge AI by exploring the technologies that make it possible and seeing some use cases for Edge AI.
边缘人工智能（Edge AI）是人工智能最引人注目的新领域之一，其目的是让人工智能流程运行在更靠近用户的设备上，而不必担心隐私或数据传输较慢带来的影响。边缘人工智能可以使人工智能技术得到更广泛的应用，使智能设备在无需接入云平台的情况下对输入做出快速反应。虽然这已经让我们快速了解到边缘人工智能的定义，但还是值得花点时间了解一些边缘人工智能的应用案例，以 便更好地理解和探索边缘人工智能。
什么是边缘计算？
In order to truly understand Edge AI, we need to first understand Edge computing, and the best way to understand Edge computing is to contrast it with cloud computing. Cloud computing is the delivery of computing services over the internet. In contrast, Edge computing systems are not connected to a cloud, instead of operating on local devices. These local devices can be a dedicated edge computing server, a local device, or an Internet of Things (IoT). There are a number of advantages to using Edge computing. For instance, internet/cloud-based computation is limited by latency and bandwidth, while Edge computing is not limited by these parameters.
为了真正理解边缘人工智能，首先需要理解边缘计算，而理解边缘计算的最佳方法就是将其与云计算进行对比。云计算是通过公共互联网提供计算服务，相比之下，边缘计算系统并不连接到云计算平台，而是在内部部署设备上运行。这些设备可能是专用边缘计算服务器、内部部署设备或物联网（IoT）设备。使用边缘计算有许多优点。例如，基于互联网/云平台的计算处理会受到网络延迟和带宽的限制，而边缘计算则不受这些因素的限制。
什么是边缘人工智能？
Now that we understand Edge computing we can take a look at Edge AI. Edge AI combines Artificial Intelligence and edge computing. The AI algorithms are run on devices capable of edge computing. The advantage of this is that the data can be processed in real-time, without having to connect to a cloud.
在了解了什么是边缘计算之后，需要了解边缘人工智能。边缘人工智能将人工智能技术和边缘计算技术相结合，使人工智能算法运行在能够进行边缘计算的设备上。这样做的好处是可以实时处理数据，而不必连接到云平台。
Most cutting edge AI processes are carried out in a cloud as they mandate a large amount of computing power. The result is that these AI processes can be vulnerable to downtime. Because Edge AI systems operate on an edge computing device, the necessary data operations can occur locally, being sent when an internet connection is established, which saves time. The deep learning algorithms can operate on the device itself, the origin point of the data.
大多数先进的人工智能流程都是在云中进行的，因为它们需要大量的计算能力。其结果是这些人工智能流程很容易受到网络延迟或停机的影响。因为边缘人工智能系统在边缘计算设备上运行，所以其必要的数据操作可以在本地进行，并通过公共互联网发送，这节省了大量时间。而深度学习算法可以在设备本身（数据的起点）上运行。
Edge AI is becoming increasingly important due to the fact that more and more devices need to employ AI in situations where they cannot access the cloud. Consider how many factory robots or how many cars these days come with computer vision algorithms. A lag time in the transmission of data in these situations could be catastrophic. Self-driving cars cannot suffer from latency while detecting objects on the street. Since a quick response time is so important, the device itself must have an Edge AI system that allows it to analyze and classify images without relying on a cloud connection.
边缘人工智能变得越来越重要，这是因为越来越多的设备需要在无法访问云平台的情况下使用人工智能技术。在自动化机器人或配备计算机视觉算法的智能汽车的应用中，数据传输的滞后可能是灾难性的。自动驾驶汽车在检测道路的人员或障碍时不能受到延迟的影响，由于快速响应时间是如此重要，必须采用边缘人工智能系统，允许实时分析和分类图像，而不依赖云计算连接。
When edge computers are entrusted with the information processing tasks usually carried out on the cloud, the result is real-time low latency, real-time processing. Additionally, by restricting the transmission of data to just the most vital information, the data volume itself can be reduced and communication interruptions can be minimized.
当边缘计算设备被赋予通常在云端进行的信息处理任务时，其结果是低延迟或实时进行处理。此外，通过传输最重要的信息，可以减少传输的数据量，并最大程度地减少通信中断。
边缘人工智能与物联网
Edge AI meshes with other digital technologies like 5G and the Internet of Things (IoT). IoT can generate data for Edge AI systems to make use of, while 5G technology is essential for the continued advancement of both Edge AI and IoT.
边缘人工智能可以与 5G 和物联网（IoT）等其他数字技术相结合。物联网为边缘人工智能系统生成数据以供使用，而 5G 技术对于边缘人工智能和物联网的持续发展至关重要。
The Internet of Things refers to a variety of smart devices connected to one another through the internet. All of these devices generate data, which can be fed into the Edge AI device, which can also act as a temporary storage unit for the data until it is synced with the cloud. The method of data processing allows for greater flexibility.
物联网是指通过公共互联网相互连接的各种智能设备。所有这些设备都会生成数据，这些数据可以输入到边缘人工智能设备中，这些设备也可以充当数据的临时存储单元。而数据处理方法具有更大的灵活性。
The fifth generation of the mobile network, 5G, is critical for the development of both Edge AI and the Internet of Things. 5G is capable of transferring data at much higher speeds, up to 20Gbps, whereas 4G is capable of delivering data at only 1Gbps. 5G also supports far more simultaneous connections than 4G (1,000,000 per square kilometer vs. 100,000) and a better latency speed (1ms vs. 10ms). These advantages over 4G are important because as the IoT grows, data volume grows as well and transfer speed is impacted. 5G enables more interactions between a wider range of devices, many of which can be equipped with Edge AI.
5G 技术对于边缘人工智能和物联网的发展至关重要。5G 能够以高达 20Gbps 的更高速度传输数据，而 4G 只能以 1Gbps 的速度传输 数据。5G 还比 4G 支持更多的并发连接和更短的延迟。与 4G 相比，这些优势非常重要，因为随着物联网的发展，数据量也将增长，并且传输速度也会受到影响。5G 使更多设备之间可以进行更多交互，其中许多设备都可以采用边缘人工智能技术。
边缘人工智能用例
Use cases for Edge AI include just about any instance where data processing would be done more efficiently on a local device than when done through a cloud. However, some of the most common use cases for Edge AI include self-driving cars, autonomous drones, [facial recognition](https://www.aaeon.com/en/ai/facial-recognition-application-story#:~:text=Cities and communities around the,to many of their needs.&text=One way in which AI,and security in these communities.), and digital assistants.
边缘人工智能的用例包括几乎所有在本地设备上进行数据处理比通过云平台更有效的实例。边缘人工智能的一些常见用例包括自动驾驶汽车、无人机、面部识别和数字助理。
Self-driving cars are one of the most relevant use cases for Edge AI. Self-driving cars must constantly be scanning the surrounding environment and assessing the situation, making corrections to its trajectory based on nearby events. Real-time data processing is critical for these cases, and as a result, their onboard Edge AI systems are in charge of the data storage, manipulation, and analysis. The edge AI systems are necessary to bring level 3 and level 4 (fully autonomous) vehicles to the market.
自动驾驶汽车是边缘人工智能的典型用例之一。自动驾驶汽车必须不断地扫描周围的环境并评估行驶情况，根据突发事件对其行进轨迹进行校正。在这些情况下，实时数据处理至关重要，其车载的边缘人工智能系统将负责数据的存储、处理和分析。因此，边缘人工智能技术对于将三级和四级（完全自主）车辆推向市场是必不可少的技术。
Because autonomous drones are not piloted by human operators, they have very similar requirements for autonomous cars. If a drone loses control or malfunctions while flying, it can crash and damage property or life. Drones may fly far out of range of an internet access point, and they must have Edge AI capabilities. Edge AI systems will be indispensable for services like Amazon Prime Air, which aims to deliver packages via drone.
因为无人机不是由人类操作员控制或驾驶的，所以它们其实与自动驾驶汽车的要求非常相似。如果无人机在飞行中失去控制或发生故障，则可能坠毁并损坏公共财产或威胁人身安全。此外，无人机可能会飞出互联网网络服务范围之外，并且它们必须具有边缘人工智能功能。边缘人工智能系统对于亚马逊 Prime Air 等旨在通过无人机交付包裹的服务来说是不可或缺的。
Another use case for Edge AI is facial recognition systems. Facial recognition systems rely on computer vision algorithms, analyzing data collected by the camera. Facial recognition apps that operate for the purposes of tasks like security need to operate reliably even if they are not connected to a cloud.
边缘人工智能的另一个用例是面部识别系统。面部识别系统依靠计算机视觉算法来分析摄像头收集的数据。即使没有连接到云平台，为安全等任务而运行的面部识别应用程序也需要可靠地运行。
Digital assistants are another common use case for Edge AI. Digital assistants like Google Assistant, Alexa, and Siri must be able to operate on smartphones and other digital devices even when they are not connected to the internet. When data is processed on the device there’s no need to deliver it to the cloud, which helps reduce traffic and ensure privacy.
数字助理是边缘人工智能的另一个常见用例。Google Assistant、Alexa和Siri等数字助理必须能够在智能手机和其他数字设备上运行，即使它们没有连接到公共互联网也是如此。在数字设备上处理数据后，无需将其交付到云中，这有助于减少流量并确保隐私安全。
原文链接：What Edge AI & Edge Computing?