Edge AI starts with edge computing. Also called edge processing, edge computing is a network technology that positions servers locally near devices. This helps to reduce system processing load and resolve data transmission delays. These processes are performed at the location where the sensor or device generates the data, also called the edge.

Edge AI从边缘计算开始。 边缘计算也称为边缘处理，是一种将服务器本地放置在设备附近的网络技术。 这有助于减少系统处理负载并解决数据传输延迟。 这些过程在传感器或设备生成数据的位置(也称为边缘)执行。

Developments in edge computing mean that edge AI is becoming more important. This is true across a variety of industries, particularly when it comes to processing latency and data privacy. In this article we’ll look at the impact of Edge AI, why it’s important, and common use cases for it.

边缘计算的发展意味着边缘AI变得越来越重要。 在各个行业中都是如此，尤其是在处理延迟和数据隐私方面。 在本文中，我们将研究Edge AI的影响，重要性以及为何常见用例。

什么是Edge AI？ (What is Edge AI?)

Edge AI refers to AI algorithms that process locally on hardware devices, and can process data without a connection. This means operations such as data creation can occur without streaming or storing data in the cloud. This is important because there are an increasing number of cases where device data can’t be handled via the cloud. Factory robots and cars, for example, need high-speed processing with minimal latency.

Edge AI是指在硬件设备上本地处理并且无需连接即可处理数据的AI算法。 这意味着可以进行诸如数据创建之类的操作，而无需在云中流式传输或存储数据。 这很重要，因为越来越多的情况下无法通过云处理设备数据。 例如，工厂机器人和汽车需要高速处理且延迟最小。

To achieve these goals, edge computing can generate data through deep learning on the cloud to develop deductive and predictive models at the data origin point, i.e. the device itself (the edge).

为了实现这些目标，边缘计算可以通过在云上进行深度学习来生成数据，以在数据起点(即设备本身(边缘))开发演绎和预测模型。

We can see an example of this at work in factory robots. AI technology can be used here to visualize and assess vast amounts of multimodal data from surveillance cameras and sensors at speeds humans can’t process. We can also use it to detect faulty data on production lines that humans might miss. These kinds of IoT structures can store vast amounts of data generated from production lines and carry out analysis with machine learning. They are also at the heart of the deductive and predictive models that improve the smartification of factories.

我们可以在工厂机器人中看到一个例子。 AI技术可用于以可视化和可视化的方式评估和评估来自监控摄像头和传感器的大量多模式数据，而这些速度是人类无法处理的。 我们还可以使用它来检测人类可能会错过的生产线上的错误数据。 这类物联网结构可以存储从生产线生成的大量数据，并通过机器学习进行分析。 它们也是演绎和预测模型的核心，可以提高工厂的智能化水平。

Edge AI，物联网和5G： (Edge AI, the Internet of Things, and 5G:)

Edge AI is often talked about in relation to the Internet of Things (IoT) and 5G networks.

人们经常谈论Edge AI与物联网(IoT)和5G网络有关。

The term IoT refers to devices connected to each other through the internet, and includes smartphones, robotics, and electronic devices. As a platform that performs analysis with AI, edge AI can collect and store the vast amount of data generated by IoT, making it possible to use clouds with scalable characteristics. This allows for improved data processing and infrastructural flexibility.

术语IoT是指通过Internet相互连接的设备，包括智能手机，机器人和电子设备。 作为使用AI进行分析的平台，边缘AI可以收集和存储由IoT生成的大量数据，从而可以使用具有可扩展特性的云。 这可以改善数据处理和基础设施的灵活性。

5G networks can enhance the above-mentioned processes because their three major features — ultra-high speed, massive simultaneous connections, and ultra-low latency — clearly surpass that of 4G.

5G网络可以增强上述过程，因为它们的三个主要功能(超高速，大量同时连接和超低延迟)明显超过了4G。

5G is indispensable for the development of IoT and edge AI, because when IoT devices transmit data, data volume swells and impacts transfer speed. Drops in transfer speed can create latency, which is the biggest issue when it comes to real-time processing.

5G对于物联网和边缘AI的发展必不可少，因为当物联网设备传输数据时，数据量会膨胀并影响传输速度。 传输速度下降会导致延迟，这是实时处理中的最大问题。

边缘计算和边缘AI为什么如此重要？ (Why are edge computing and edge AI important?)

There are an increasing number of cases in which device data can’t be handled via the cloud. This is often the case with factory robots and cars, which require high-speed processing because of issues that can arise when increased data flow creates latency.

在越来越多的情况下，无法通过云处理设备数据。 工厂机器人和汽车通常是这种情况，由于增加的数据流会导致延迟，因此可能会出现问题，因此需要进行高速处理。

For example, imagine a self-driving car suffering from cloud latency while detecting objects on the road, or operating the brakes or steering wheel. Any slowdown in data processing will result in a slower response from the vehicle. If the slowdown is such that the vehicle does not respond in time, this could result in an accident. Lives are literally at risk.

例如，假设有一辆自动驾驶汽车在检测道路上的物体​​或操作制动器或方向盘时遭受云延迟。 数据处理的任何减慢都会导致车辆的响应变慢。 如果减速导致车辆无法及时响应，则可能导致事故。 生命实际上有危险。

For these IoT devices, a real-time response is a necessity. This means the ability for devices to analyze and assess images/data on the spot without relying on cloud AI.

对于这些物联网设备，实时响应是必要的。 这意味着设备无需依靠云AI即可现场分析和评估图像/数据的能力。

By entrusting edge devices with information processing usually entrusted to the cloud, we can achieve real-time processing without transmission latency. In addition, by limiting cloud data transmissions to only vital information, it is possible to reduce data volume and minimize communication interruptions.

通过将通常委托给云的信息处理委托给边缘设备，我们可以实现实时处理而没有传输延迟。 另外，通过将云数据传输限制为仅重要信息，可以减少数据量并最大程度地减少通信中断。

Edge AI用例 (Edge AI Use Cases)

The edge AI market is chiefly comprised of two areas: industrial machinery, and consumer devices. We’re seeing progress with demonstration tests in areas including controlling and optimizing equipment, and automating skilled labor techniques.

边缘AI市场主要包括两个领域：工业机械和消费类设备。 在包括控制和优化设备以及自动化熟练劳动力技术在内的领域进行的示范测试中，我们看到了进步。

Progress is also being made with consumer devices that have cameras with AI that automatically recognize photographic subjects. Because the number of devices is larger than industrial machines, the consumer device market is expected to rise drastically from 2021 onwards.

带有自动识别摄影对象的AI相机的消费类设备也正在取得进步。 由于设备的数量大于工业机器的数量，因此预计从2021年起消费设备市场将急剧增长。

We’ve put some common use cases for edge AI below:

我们在下面介绍了边缘AI的一些常见用例：

无人驾驶汽车 (Self-Driving Cars)

Self-driving cars are the most anticipated area of applied edge computing. There are many cases where self-driving cars have to make instantaneous assessments of a situation, and this requires real-time data processing. In December of 2019, revisions to the Road Traffic Act and Road Transportation Vehicle Law in Japan made it easier to get level 3 self-driving cars on the road. These include the safety standards that autonomous vehicles are held to, and the areas in which they can operate. As a result, car manufacturers are working on self-driving cars that adhere to these standards. Toyota, for example, is already testing full automation (level 4) with the TRI-P4.

无人驾驶汽车是应用边缘计算最令人期待的领域。 在很多情况下，自动驾驶汽车必须即时评估情况，这需要实时数据处理。 2019年12月，日本对《道路交通法》和《道路运输车辆法》进行了修订，使得在道路上安装3级自动驾驶汽车变得更加容易。 这些包括自动驾驶汽车所遵守的安全标准以及它们可以在其中运行的区域。 结果，汽车制造商正在研究符合这些标准的自动驾驶汽车。 例如，丰田公司已经在用TRI-P4测试全自动化(4级)。

自主无人机 (Autonomous Drones)

There’s been an increase of news about drones losing control and going missing while on remote flight experiments. This has even resulted in accidents. Depending on where the drone lands, a crash can be catastrophic.

关于无人机在远程飞行实验中失去控制并失踪的消息越来越多。 这甚至导致了事故。 根据无人机着陆的位置，坠机可能是灾难性的。

With autonomous drones, the pilot is not actively involved in the drone’s flight. They monitor the operation remotely, and only pilot the drone when absolutely necessary. The best known example of this is Amazon Prime Air, a drone delivery service which is developing self-piloting drones to deliver packages.

使用自动驾驶无人机，飞行员不会积极参与无人机的飞行。 他们远程监控操作，仅在绝对必要时才驾驶无人机。 最著名的例子是亚马逊Prime Air，这是一种无人机交付服务，正在开发自动驾驶无人机以交付包裹。

面部识别 (Facial Recognition)

Facial recognition systems are a development in surveillance cameras, which can learn to recognize people by their faces. In November 2019, WDS Co., Ltd began supplying Eeye, an AI camera module that analyzes facial features in real-time through edge AI computing processes. Eeye recognizes faces quickly and accurately, and is suited for marketing tools that target characteristics such as gender and age, and face identification for unlocking devices.

面部识别系统是监视相机的一项发展，可以学习识别人脸。 在2019年11月，WDS Co.，Ltd.开始提供Eeye，这是一个AI摄像头模块，可通过边缘AI计算流程实时分析面部特征。 Eeye可以快速准确地识别人脸，适合用于针对性别和年龄等特征的营销工具以及用于解锁设备的人脸识别。

智能手机 (Smartphones)

This edge AI device is the one we’re all most familiar with. Siri and Google Assistant are good examples of edge AI on smartphones, as the technology drives their vocal user interfaces. With on-device AI, processing happens on the device (edge) side, meaning there is no need to deliver device data to the cloud. This helps secure privacy and reduce traffic.

这款边缘AI设备是我们最熟悉的设备。 Siri和Google Assistant是智能手机上的边缘AI的很好的例子，因为该技术驱动了他们的人声用户界面。 使用设备上的AI，处理发生在设备(边缘)端，这意味着无需将设备数据传递到云。 这有助于保护隐私并减少流量。

Edge AI的未来 (The Future of Edge AI)

Edge AI is growing, and we’ve seen big investments in the technology. Companies like Konduit AI are making it a key part of their AI strategy in Southeast Asia. Another example was in January of 2020, when it was reported that Apple invested 200 million dollars to acquire the Seattle-based AI enterprise, Xnor.ai. Xnor.ai’s AI tech processes data on the user’s smartphone with edge processing. With built-in AI on the smartphone itself, we’ll likely see advancements in voice processing, facial recognition technology, and enhanced privacy.

Edge AI不断发展，我们已经看到了对该技术的大量投资。 诸如Konduit AI之类的公司正在将其作为其AI战略在东南亚的重要组成部分。 另一个例子是在2020年1月，当时有报道称苹果投资2亿美元收购了总部位于西雅图的AI企业Xnor.ai. Xnor.ai的AI技术通过边缘处理功能在用户的智能手机上处理数据。 通过智能手机本身的内置AI，我们可能会看到语音处理，面部识别技术和增强的隐私性方面的进步。

According to the “2019 AI Business Aggregate Survey” published by Fuji Keizai Group, the edge AI computing market in Japan had a forecast market size of 11 billion yen in the 2018 fiscal year. The survey predicts the market to expand to 66.4 billion yen in the 2030 fiscal year.

根据Fuji Keizai Group发布的“ 2019 AI商业综合调查”，日本边缘AI计算市场在2018财年的预测市场规模为110亿日元。 调查预测，到2030财年，该市场将增长到664亿日元。

And with the spread of 5G, we’ll also likely see decreasing costs and increasing demand for edge AI services across the world.

随着5G的普及，我们还可能看到成本下降，并且对全球范围内的边缘AI服务的需求也在增加。

作者简介 (Author Profile)

From self-employed field engineer to PHP programmer, Tatsuo Kurita is now a UX director working mainly as a technical director to support corporate products. His expertise covers a wide range of areas, including certification in applied information technology, information security management, mental health management grade II, HTML, general deep learning, and AI implementation.

从个体经营的现场工程师到PHP程序员，栗田达男(Tatsuo Kurita)现在是一名UX总监，主要担任支持公司产品的技术总监。 他的专业知识涵盖了广泛的领域，包括应用信息技术，信息安全管理，II级心理健康管理，HTML，通用深度学习和AI实施的认证。

关于Lionbridge AI (About Lionbridge AI)

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