Do Not Use LLM or Generative AI For These Use Cases
请勿在这些使用案例中使用 LLM 或生成式人工智能

Choose correct AI techniques for the right use case families
为正确的用例家庭选择正确的人工智能技术

The recent hype for Gen AI (Generative AI), especially LLM (Large Language Model), has become extremely high. More and more organizations have already jumped or are planning to jump into this controversial zone. Of course, it is understandable that no one wants to fall behind and lose this revolutionary opportunity. However, as one of the professionals with a Data Science background and who has experienced AI/ML growth for the last 10+ years, I want to write this article to alert decision-makers to think twice about simply starting to implement their AI solutions.
最近，Gen AI（生成式人工智能），尤其是 LLM（大型语言模型）的热度极高。越来越多的组织已经或正计划跳入这个充满争议的领域。当然，谁都不想落后，不想失去这个革命性的机遇，这是可以理解的。不过，作为拥有数据科学背景、在过去 10 多年里经历过人工智能/ML 发展的专业人士之一，我想写这篇文章来提醒决策者们，在简单地开始实施人工智能解决方案时，一定要三思而后行。

Don’t get me wrong. I’ve always been an advocate of AI and one of those optimistic people. This article has borrowed from an insightful work from Gartner, which aims to educate people about various AI techniques and Use Case families. LLM is not everything about AI, and not all the AI use cases are suitable for Generative AI.
别误会我的意思。我一直是人工智能的拥护者，也是那种乐观主义者。本文借鉴了 Gartner 的一篇颇有见地的著作，旨在向人们介绍各种人工智能技术和用例系列。LLM 并不是人工智能的全部，也不是所有的人工智能用例都适合生成式人工智能。

Twelve Use Case Families
十二个用例系列

Firstly, let’s explore the typical use case families in practice that are expecting AI techniques to address them.
首先，让我们来探讨一下实践中期待人工智能技术来解决的典型用例系列。

1. Prediction / Forecasting
1.预测/预报

This is one of the most common use cases. Suppose we are selling some products, and we collected all the sales data as well as the other features that may relate to the selling activities. Then, we can use machine learning algorithms to predict future sales.
这是最常见的用例之一。假设我们正在销售某些产品，我们收集了所有销售数据以及可能与销售活动相关的其他特征。然后，我们可以使用机器学习算法来预测未来的销售情况。

2. Autonomous System
2.自治系统

We can also use AI to build such an autonomous system to intelligently do something for us.
我们还可以利用人工智能建立这样一个自主系统，智能地为我们做一些事情。

One of the examples is using a drone with embedded AI technology to perform routine inspections of power transmission lines.
其中一个例子是使用嵌入人工智能技术的无人机对输电线路进行例行检查。

3. Planning
3.规划

Sometimes, we may have to work in a complex system that has many interdependencies and constraints. The planning use cases are to find out the best plan to make sure we have maximised the benefits and/or minimised the impact.
有时，我们可能需要在一个复杂的系统中工作，该系统有许多相互依存和制约因素。规划用例就是要找出最佳方案，确保我们能将利益最大化和/或影响最小化。

For example, we may use AI to analyse the traffic data in urban areas. Then, suppose we are onboarding a road project, and the algorithm can tell us when and where to work for a minimum impact on the daily traffic.
例如，我们可以使用人工智能来分析城市地区的交通数据。然后，假设我们正在上马一个道路项目，算法可以告诉我们何时何地进行施工，以将对日常交通的影响降到最低。

4. Decision Intelligence
4.决策智能

This type of use case aims to enhance the decision-making process by providing insights and predictions.
这类用例旨在通过提供见解和预测来加强决策过程。

The difference between this one and the Autonomous System is that this use case family is to help humans make decisions rather than automatically doing it. This is usually because the decision has to take certain preferences or Entrepreneurship into account.
它与 "自主系统 "的区别在于，这个用例系列是帮助人类做出决定，而不是自动做出决定。这通常是因为决策必须考虑到某些偏好或企业家精神。

5. Recommender System
5.推荐系统

A widely applied and mature use case type. If you ever use eBay or Spotify, this use case is to recommend the products you may buy or music you may like.
广泛应用且成熟的用例类型。如果您使用过 eBay 或 Spotify，这种用例就是向您推荐可能购买的商品或可能喜欢的音乐。

6. Segmentation / Classification
6.分段/分类

Sometimes, we don’t know the characteristics of the objects we are dealing with. So, we want to classify the entities into different categories or levels to see if we can get some insights.
有时，我们并不知道所处理对象的特征。因此，我们希望将实体划分为不同的类别或级别，看看能否获得一些启示。

For example, we can use AI to classify loan applicants into Low, Medium and High-risk levels based on their attributes.
例如，我们可以利用人工智能，根据贷款申请人的属性将其分为低、中和高风险级别。

7. Intelligent Automation
7.智能自动化

This type of use case aims to combine AI with automation technologies to enhance some business processes, such as manufacturing. One of the hot topics, “Digital Twin”, belongs to this one.
这类用例旨在将人工智能与自动化技术相结合，以改进某些业务流程，如制造。热门话题之一的 "数字孪生 "就属于这一类。

For example, we may train a model with all the sensor data from a manufacturing plant and alert when a trip is about to happen several days in advance.
例如，我们可以利用一家制造厂的所有传感器数据来训练一个模型，并提前几天在即将发生行程时发出警报。

8. Perception
8.感知

This use case type usually refers to those relying on sensory data such as vision, sound and other environmental data.
这种用例类型通常是指依赖视觉、声音和其他环境数据等感官数据的用例。

For example, a camera on the road to detect violating driving activities is one of this use case family.
例如，在道路上安装摄像头来检测违规驾驶行为就是这种用例系列中的一种。

9. Anomaly Detection
9.异常检测

Sometimes, our process goes wrong, but we don’t even know it. For example, an electricity grid may source the energy from hundreds of generators. When one of them goes down, it’s easy to realise. However, if there are 10 of them fluctuating coincidentally, it will be difficult to be aware. AI can detect such subtle events and alert the Control Room.
有时，我们的流程会出错，但我们却浑然不知。例如，电网可能从数百台发电机中获取能源。当其中一台发生故障时，我们很容易意识到。但是，如果有 10 台发电机不约而同地出现波动，我们就很难察觉。人工智能可以检测到这种微妙的事件，并向控制室发出警报。

10. Conversational User Interfaces
10.对话式用户界面

Since this use case family, you might become familiar with it because these are hot topics recently due to the growth of Generative AI technology.
由于生成式人工智能技术的发展，这些都是最近的热门话题，因此您可能会对这个使用案例家族感到熟悉。

This use case itself can be well defined by the Chatbot for a call centre or a customer support centre. The AI will be trained using lots of product documentation, and it will answer the questions from the customers when they encounter problems. This usually doesn’t require human intervention for simple support tickets.
呼叫中心或客户支持中心的聊天机器人可以很好地定义这种用例本身。人工智能将使用大量产品文档进行训练，并在客户遇到问题时回答他们的问题。对于简单的支持单，通常不需要人工干预。

11. Content Generation
11.内容生成

Easy to explain. ChatGPT is an application of this use case. However, just need to highlight that the “content” is not limited to text. It can also be an image, video or soundtrack.
简单明了。ChatGPT 就是这种用例的一种应用。不过，需要强调的是，"内容 "并不局限于文本。它也可以是图片、视频或背景音乐。

12. Knowledge Discovery
12.知识发现

Sometimes, we have lots of data in chaos. We don’t know what we don’t know, but some patterns may hide in this chaos. In this case, we can use AI to try to discover some insights, such as relationships or correlations from a large dataset.
有时，我们会在混乱中获得大量数据。我们不知道我们不知道什么，但一些模式可能隐藏在这种混乱中。在这种情况下，我们可以使用人工智能来尝试发现一些洞察力，比如从大型数据集中发现关系或相关性。

For example, we can use AI to analyse patient data and clinical records to find out some patterns that may help the health industry develop new treatment strategies or effective medicine.
例如，我们可以利用人工智能分析病人数据和临床记录，找出一些规律，帮助医疗行业开发新的治疗策略或有效的药物。

Six Common AI Techniques
六种常见的人工智能技术

Now, let me briefly introduce the 6 common AI techniques.
现在，让我简要介绍一下 6 种常见的人工智能技术。

1. Non-Generative Machine Learning
1.非生成机器学习

In fact, this category shouldn’t have even existed before Generative AI became popular in recent years. When I started to use Data Science in industries, people liked to talk about “Classic Machine Learning” and “Deep Learning”. Now, because of the Gen AI hype, they all become “Normal Machine Learning” techniques.
事实上，在生成式人工智能（Generative AI）近几年流行起来之前，这个类别根本就不应该存在。当我开始在行业中使用数据科学时，人们喜欢谈论 "经典机器学习 "和 "深度学习"。现在，由于 Gen AI 的炒作，它们都变成了 "普通机器学习 "技术。

This category includes basic linear regression, clustering, classification, decision trees, etc.
这一类包括基本线性回归、聚类、分类、决策树等。

2. Simulation
2.模拟

This AI technique allows us to create a model for a process or a system in the real world. Then, we can input a lot of combinations of parameters into it. So, it will be able to answer lots of “what-if” questions.
通过这种人工智能技术，我们可以为现实世界中的某个流程或系统创建一个模型。然后，我们可以向其输入大量的参数组合。这样，它就能回答许多 "假设 "问题。

3. Optimisation
3.优化

This technique helps us find the best parameters in a formula or equation. For example, we know that discounts will attract customers to buy more products from a supermarket. However, if the price is too low, the profit may disappear. Therefore, optimisation techniques can help us to find the “balance point” to maximise our profit.
这种技术可以帮助我们找到公式或方程中的最佳参数。例如，我们知道折扣会吸引顾客购买更多超市产品。但是，如果价格太低，利润可能会消失。因此，优化技术可以帮助我们找到 "平衡点"，实现利润最大化。

4. Rules / Heuristics
4.规则/启发式

This AI technique is not necessarily machine learning. We may create a set of pre-defined rules based on scientific evidence or even rules of thumb from the domain experts. Such a “rule-based system” will help us make better decisions.
这种人工智能技术不一定是机器学习。我们可以根据科学证据，甚至是领域专家的经验法则，创建一套预定义的规则。这种 "基于规则的系统 "将帮助我们做出更好的决策。

5. Graphs
5.图表

Graphs do not refer to any particular algorithms of machine learning but a data structure that represents objects and their relationships in a different way.
图并不指任何特定的机器学习算法，而是一种以不同方式表示对象及其关系的数据结构。

For example, a node in a graph stands for a data point, and edges represent the connections between this node and other nodes. Such a data structure could help other processes work more accurately than a tabular-based data structure.
例如，图中的节点代表一个数据点，边代表该节点与其他节点之间的连接。与基于表格的数据结构相比，这种数据结构可以帮助其他流程更准确地工作。

6. Generative Models
6.生成模型

I know you are looking for this one :)
我知道你在找这个:)

This is the AI technique that is able to generate something. It could be text, images, videos, or whatever other artifacts. ChatGPT is definitely using this technique.
这是一种能够生成某些东西的人工智能技术。它可以是文本、图像、视频或其他任何人工制品。ChatGPT 肯定使用了这种技术。

The Matrix: Use Case Family × AI Technique
矩阵用例家族 × 人工智能技术

Here is the key takeaway of this article. As I mentioned in the introduction, not all the use case families can be resolved by Generative Models.
以下是本文的主要内容。正如我在引言中提到的，并非所有的用例系列都能通过生成模型来解决。

The matrix below shows that certain use case families are more suitable for certain AI techniques. The Low (L), Medium (M) and High (H) refer to the stability and reliability of such AI techniques utilised in the corresponding use case families.
下表显示，某些用例系列更适合采用某些人工智能技术。低（L）、中（M）和高（H）指的是相应用例系列中使用的人工智能技术的稳定性和可靠性。

Based on the above matrix, we should always use the “High” suitable AI techniques on the corresponding use case families. If it is “Medium”, think twice if you really need to use this technique. When it is “Low”, please never use the techniques for the use cases. Sometimes, you will find that it’s not only not suitable but also not feasible.
根据上述矩阵，我们应始终在相应的用例系列中使用 "高 "合适的人工智能技术。如果是 "中"，请三思是否真的需要使用这种技术。如果是 "低"，请永远不要在用例中使用该技术。有时，你会发现它不仅不合适，而且也不可行。

Generative Models
生成模型

Since this AI technique is related to Generative AI such as ChatGPT, let’s have a deep dive into it.
既然这项人工智能技术与 ChatGPT 等生成式人工智能相关，那就让我们来深入了解一下吧。

According to the matrix, we should never use LLM to help us forecast something that does not exist in the world, nor should we use it for planning.
根据矩阵，我们绝不能用 LLM 来帮助我们预测世界上不存在的东西，也不能用它来进行规划。

For example, when we have the sales data for a particular product, it doesn’t make sense to give that to ChatGPT to ask what will be my sales for the next day. However, you may ask it to write some code for you, and the code will use some Non-Generative ML techniques to forecast the data. Therefore, we still use ChatGPT on the “Content Generation” use case.
例如，当我们有了某一特定产品的销售数据后，把这些数据交给 ChatGPT 来询问第二天的销售情况是没有意义的。不过，你可以让它为你写一些代码，代码会使用一些非生成式 ML 技术来预测数据。因此，我们仍然在 "内容生成 "用例中使用 ChatGPT。

Summary
摘要

In this article, I have introduced six common AI techniques and 12 typical use case families. Each of them comes with a brief description and an example. I hope this helped to form the AI application scenarios in a better way.
在本文中，我介绍了六种常见的人工智能技术和 12 个典型的用例系列。每种技术都附有简要说明和示例。我希望这有助于更好地形成人工智能应用场景。

Then, based on the Gartner research result that I have learned from the conference, the Matrix of AI Techniques × Use Case Families was demonstrated. I hope this will provide some useful references for the organizations that are going to invest in AI due to the recent hype.
然后，根据我在会议上了解到的 Gartner 研究成果，展示了 "人工智能技术×用例家族矩阵"。我希望这能为那些因近期人工智能热潮而准备投资人工智能的组织提供一些有用的参考。