Perhaps you’ve heard about it? Two of the most disruptive technologies in recent decades. You may probably hear of them separately.

P erhaps你听说过吗 ？ 近几十年来最具破坏性的两项技术。 您可能会分别听到它们。

让我们合并它们！ 5G +人工智能 (Let’s merge them! 5G + Artificial Intelligence)

Mmhh.. that doesn’t seem right. Are you sure?

嗯..这似乎不正确。 你确定吗？

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如果您想了解更多，请访问 oscargarciaramos.com

AI可以在网络和设备上增强5G。 (AI enhances 5G, on the network and on the device.)

“5G is the fifth generation of wireless communication technologies and standards”. Be left behind the first mobile phones where we could only speak (1G), the first SMS (2G), our first internet connections (3G) and lastly mobile bandwidth(4G).

“ 5G是第五代无线通信技术和标准” 。 被抛在第一部只能说语音的手机，第一部短信(2G)，第一部互联网连接(3G)和移动带宽(4G)之后。

We are talking about something further, where the most significant advance will come from the hand of speed, where 5G will allow us to navigate up to 10GBps, 10x faster than today.

我们正在谈论的是更多东西，其中最重要的进步将来自速度之手，其中5G将使我们能够导航到10GBps，比今天快10倍。

What will 5G mean? The proliferation of an even more massive IoT (Internet of Things) ecosystem, where wireless networks will satisfy the communication needs of hundreds of billions of connected devices, without prejudice to their speed, latency and cost.

5G是什么意思？ 更加庞大的IoT(物联网)生态系统的扩散，其中无线网络将在不影响其速度，延迟和成本的情况下满足数千亿个互联设备的通信需求。

Among other advantages:

其他优势包括：

• Up to 10Gbps data rate -> 10 to 100 times better than 4G and 4.5G networks.高达10Gbps的数据速率->比4G和4.5G网络提高10到100倍。
• 10x years of battery life on low-power IoT devices.低功耗IoT设备的电池寿命长达10倍​​。
• 90% reduction in energy consumption & 100% coverage.能耗降低90％，覆盖率降低100％。
• 1 millisecond latency &1000 times faster broadband per unit area.1毫秒的延迟，每单位面积的宽带速度快1000倍。
• Up to 100 more connected devices per unit area (compared to 4G LTE networks).每单位面积最多可连接100个设备(与4G LTE网络相比)。

This is where the concept of Edge Analytics arises, or broadly meant, a “new” model of data analysis where data stream processing occurs at a non-central point of the system, such as a device or a sensor.

这是Edge Analytics概念的出现，或广义上讲，是数据分析的“新”模型，其中数据流处理发生在系统的非中心点(例如设备或传感器)上。

As we’ve seen, 5G will cause a proliferation of sensors around us, and each of those sensors opens up new opportunities for analyzing data and creating Machine Learning models.

如我们所见，5G将导致我们周围的传感器激增，而这些传感器中的每一个都为分析数据和创建机器学习模型提供了新的机会。

也就是说，我们可以将这些传感器用作AI输入。 (That is, we can use those sensors as AI inputs.)

And not only that. We are no longer talking about moving significant amounts of data from its source to a centralized repository where we can process it.

而且不仅如此 。 我们不再谈论将大量数据从其源转移到集中式存储库中，以便在其中进行处理。

We are talking about bringing processing closer to the source, the device itself, offering crucial benefits such as privacy and reliability, in addition to helping scale intelligence.

我们正在谈论的是使处理过程更接近设备本身的源头，除了帮助扩展智能之外，还提供诸如隐私和可靠性之类的关键优势 。

新挑战，新机遇 (New challenges, new opportunities)

More efficient wireless connections, longer battery life, and therefore better user experiences.

更有效的无线连接，更长的电池寿命，因此更好的用户体验。

Imagine using 5G and AI to recreate a virtual human who can interact with a client in real time, advising him remotely or simply as a companion. Sensors that are responsible for making decisions in critical situations or that require an immediate response. We are not simply talking about processing information near the data source, but about adding more context by sharing and receiving new data at high speed, taking advantage of the low latency between them.

想象一下使用5G和AI重新创建一个虚拟的人，该人可以与客户进行实时交互，向客户提供远程建议或只是作为陪同对象。 负责在紧急情况下做出决策或需要立即做出响应的传感器。 我们不仅仅是谈论在数据源附近处理信息，而是谈论通过利用它们之间的低延迟来高速共享和接收新数据来添加更多上下文。

Nowadays, when we ask our AI-powered voice assistant any question, it takes a few seconds to respond because it goes to the cloud to process the request and get a response. This can happen when we ask what the weather is going to be like, it’s not crucial. But what if you require a response in milliseconds, like an emergency? We are sold. We better be patient.

如今，当我们问我们的人工智能语音助手有任何问题时，需要花几秒钟的时间进行响应，因为它要通过云来处理请求并获得响应。 当我们询问天气如何时，可能会发生这种情况，这并不重要。 但是，如果您需要毫秒级的响应(如紧急情况)怎么办？ 我们被卖了 。 我们最好耐心点。

The low latency and high capacity and availability of 5G will also allow AI processing to be distributed among the device or the sensor, the edge cloud and the common central cloud, allowing the development of much more flexible solutions than those created today.

5G的低延迟，高容量和高可用性还将使AI处理能够分布在设备或传感器，边缘云和公共中央云之间，从而允许开发比当今创建的解决方案更加灵活的解决方案。

Sectors such as Gaming or Industry 4.X could also benefit from these capabilities for complex Use Cases that can benefit from this distributed wireless processing.

对于复杂用例，游戏或行业4.X等行业也可以从这些功能中受益，这些用例可以从此分布式无线处理中受益。

一个新的范例：通过无线传感器网络进行分布式学习 (A new paradigm: Distributed Learning over Wireless Sensor Networks)

We have already taken the first step: use the on-device processing power to save energy and refine the data before passing it to the cloud for much more aggregated analysis.

我们已经迈出了第一步 ：使用设备上的处理功能来节省能源并优化数据，然后再将其传递到云中进行更汇总的分析。

The next step goes a little further: we will not settle for simply inferring on the device itself, but training the model right there, that is, Distributed Learning.

下一步要走得更远：我们不会满足于简单地推断设备本身，而是在此处训练模型，即分布式学习。

The theory is as follows:

理论如下：

1. The central cloud sends a model to each device.中央云将模型发送到每个设备。
2. Each device collects and stores local data samples on-device.每个设备都在设备上收集和存储本地数据样本。
3. Finally, each device performs or executes on-device training.最后，每个设备都执行或执行设备上的培训。

We went from “large scale training” to “small scalable distributed training”.

我们从“ 大规模培训” 过渡到“小型可扩展的分布式培训” 。

设备上的培训使我们能够： (On-device training allows us to:)

• Scale: extending the processing to as many devices as we want, significantly reducing the necessary computational power.

  规模 ：将处理扩展到我们想要的尽可能多的设备，从而大大降低了必要的计算能力。

• Customization: local data training, where the model adapts to the casuistry of each device (eg its own smartphone).

  定制 ：本地数据培训，其中模型适应每种设备(例如其自己的智能手机)的规格。

• Privacy: where the raw data never leaves your own device to be processed, as it currently does. Extracting value while preserving privacy.

  隐私 ：原始数据永远不会像现在这样离开您的设备进行处理。 在保护隐私的同时获取价值。

下一个是什么？ (So what’s next?)

As you can see, the doors of an exciting world open to us.

如您所见，令人兴奋的世界之门向我们敞开。

Without limitations, where AI can not only benefit from off-device data, but from new ones such as on-device data (calendar, messages, apps, etc.) as well as sensor data (ambient light, microphone, temperature, eye tracking, pulse, among many others).

没有限制，人工智能不仅可以从设备外数据中受益，而且还可以从设备数据(日历，消息，应用等)以及传感器数据(环境光，麦克风，温度，眼睛跟踪)等新数据中受益，脉冲等)。

让游戏开始吧！ (Let the game begin!)

If you want to continue reading, go check the 2nd Part:

如果要继续阅读，请检查第二部分：

物联网：智能，互联。 (AIoT: Be SMART, Be CONNECTED.)

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