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使用GPT-3进行物联网自动化

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Waylay是一个低代码平台,允许开发人员在任何地方应用企业级自动化。连接传感器,推动数据并开始享受低代码自动化的好处。

自动化规则是Waylay平台的核心。开发人员编写小型代码片段(或使用现有的代码段),并将其与逻辑运营商一起链接以定义自动化规则。将规则视为可以使您可以打开水洒水器的东西,如果它晴天没有降雨3天,或者如果在其许多传感器之一上检测到异常情况,请安排对工业机器的检查。通过将这些规则融合在一起,我们可以创建任意复杂的自动化软件。

使每个人都可以访问此自动化技术是Waylay的核心价值之一。想象一下,如果我们可以简单地通过自然方式与语音或文本控制与此自动化引擎进行简单互动。这是NLP进来的地方。我们可以预见出工厂工人的机器“烤箱5的温度是多少?”,而不必以典型的方式与计算机进行交互。或告诉它“如果冰箱的温度升高到-10度以上并且门开放”,请“提出关键警告”。

当然,正确地做到这一点并不容易。人说的规则可以带来很多歧义,需要大量的智能才能正确解析并转化为Waylay自动化规则。

__解决方案__

如果我们想基于“传统”方法建立深度学习解决方案,我们有一些问题要解决。首先,我们正在处理缺乏数据。为了强有力解析人的话语并捕获必要的信息以将其转化为Waylay系统可以理解的东西,我们需要大量数据涵盖不同的说话方式和不同类型的Waylay规则。此数据目前尚不可用。即使我们有这些数据,每当我们希望它提供一种新的说话方式或新型的Waylay规则时,我们的模型也需要进行重新训练。

我们转向促使工程解决问题。如果我们可以使用GPT3为我们做艰苦的工作,我们可以构建一个高度数据效率的系统,该系统无需重新处理以处理新案例。那有多好?

现在的问题变成了“我们如何利用GPT3的能力为我们做肮脏的工作?”。不幸的是,很难教GPT3根据自然语言输入来输出正确的内部数据结构。幸运的是,我们可以用巧妙的骇客来解决这个问题(为此,我们必须在微软[3]上归功于聪明人。在我们的解决方案中,我们将让GPT3输出一个规范的句子。这句话的信息与我们的自然语言输入相同,但以更具结构化的方式。例如,这些话语“向大卫发出一条消息,告诉他在巴黎下雨时开车安全”和“只有当巴黎的天气下雨时,才告诉大卫“安全!”如果巴黎的天气下雨,可以通过SMS“都可以简化为规范的句子”,然后将SMS发送给David,并带有消息“ Drive Safe!”。

__结论__

通过将我们的语义解析任务重新介绍到翻译任务中,我们能够利用大型的预训练的语言模型(GPT3)为我们做所有艰苦的工作。我们的解决方案与极少的数据点起作用,可以轻松地适应新情况,而无需重新培训,我们甚至不需要自己托管深度学习模型。由于GPT3具有很强的功能,我们的解决方案表现出对看不见的场景(甚至是看不见的语言!)的显着概括。

在https://www.waylay.io/articles/nlp-case-study-by-waylay上阅读完整的博客文章
作者:[karel-d'oosterlinck](https://twitter.com/kareldoostrlnck)

原文:

Waylay is a low-code platform that allows developers to apply enterprise level automation anywhere. Hook up sensors, push data and start enjoying the benefits of low-code automation.

Automation rules are the core of the Waylay platform. Developers write small code snippets (or use pre-existing ones) and chain these together with logical operators to define automation rules. Think about rules as something that allow you to turn on the water sprinklers if it has been sunny without rain for 3 days, or schedule an inspection for an industrial machine if an anomaly is detected on one of its many sensors. By chaining these rules together, we can create arbitrarily complex automation software.

Making this automation technology accessible to everyone is one of Waylay's core values. Imagine if we could simple interact with this automation engine over voice or text control, in a natural fashion. This is where NLP comes in. Instead of having to interact with a computer in the typical manner, we can foresee a factory worker asking their machine "What is the temperature of oven 5?" or telling it "Raise a critical warning if the temperature of the freezer rises above -10 degrees and the door is open".

Getting this right certainly isn't easy. Human-spoken rules can carry a lot of ambiguity and require a lot of intelligence to correctly parse and translate into Waylay automation rules.

__The solution__

If we want to build a deep learning solution based on 'traditional' methods, we have a few problems to deal with. Primarily, we are dealing with a lack of data. In order to robustly parse human utterances and capture the necessary information to translate them into something the Waylay system can understand, we would need a large amount of data spanning different ways of speaking and different types of Waylay rules. This data is currently not available. Even if we would have this data, our model would need to be retrained every time we want it to serve a new manner of speaking or a new type of Waylay rule.

We turn to prompt engineering to solve our problems. If we can use GPT3 to do the hard work for us, we can build a highly data efficient system which does not need to be retrained to deal with new cases. How nice would that be?

The question now becomes "How can we leverage the capabilities of GPT3 to do the dirty work for us?". Unfortunately, it is very hard to teach GPT3 to output the correct internal data structure Waylay requires based on a natural language input. Luckily, we can work our way around this with a clever hack (for which we have to credit the smart folks over at Microsoft [3]). In our solution, we will let GPT3 output a canonical sentence. This sentence holds the same information as our natural language input, but in a more structured fashion. For example, the utterances 'send David a message telling him to drive safe when it is raining in Paris' and 'only when the weather in Paris is raining tell David "Drive safe!" via sms' can both be reduced to the canonical sentence 'if weather in Paris is raining, then send SMS to David with message "Drive safe!"'.

__Conclusion__

By rephrasing our semantic parsing task to a translation task, we were able to leverage large pre-trained language models (GPT3) to do all the hard work for us. Our solution works with extremely few data points, can easily be adapted towards new situations without retraining and we don't even need to host the deep learning model ourselves. Because of the strong capabilities of GPT3, our solution shows remarkable generalization towards unseen scenarios (and even unseen languages!).

Read the full blog post at https://www.waylay.io/articles/nlp-case-study-by-waylay
Author: [Karel-D'Oosterlinck](https://twitter.com/KarelDoostrlnck)

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