Gen-ai 简明教程

Basics of Generative AI

生成式 AI (GenAI) 是 AI 的最新子类型，它广泛描述了 Machine Learning (ML) 模型或算法。GenAI 正在重塑创造力和创新的格局。你们都知道 ChatGPT ，但你们是否曾想过它是如何像人类一样聊天。这都要归功于生成式 AI。这项智能技术充当了 ChatGPT 的大脑，使它能够像真人一样生成响应。所以，当你们和 ChatGPT 聊天时，你们基本上就是在观察生成式 AI 的力量。

Generative AI (GenAI) is the latest subtype of AI that broadly describes Machine Learning (ML) models or algorithms. GenAI is reshaping the landscape of creativity and innovation. You all know about ChatGPT, but have you ever wondered how it chats just like humans do. All thanks to Generative AI. This smart technology serves as the brain of ChatGPT and enables it to generate responses like a real person. So, when you chat with ChatGPT, you are basically observing the power of Generative AI.

在本章中，我们将概述生成式 AI、它的演变、用例、好处和限制。

In this chapter, we will have an overview of generative AI, how it has evolved, its use cases, benefits, and limitations.

Difference Between Traditional AI and Generative AI

顾名思义，生成式 AI 会生成新的文本、音频、视频或任何其他类型的内容。它从现有训练数据中学习模式，并生成类似现实世界数据的新颖而独特的内容。

Generative AI, as name suggests, generates new text, audio, video, or any other type of content. It learns patterns from existing training data and produces new and unique output that resembles real-world data.

尽管 AI 和 GenAI 之间存在等级关系，但它们都有不同的目标和方法。下表描述了传统 AI 和生成式 AI 之间的差异−

Although there is hierarchical relationship between AI and GenAI, they both have different objectives and approaches. The below given table describes the differences between traditional AI and generative AI −

Traditional AI

Generative AI

AI is used to create intelligent systems that can perform those tasks which generally require human intelligence.

It generates new text, audio, video, or any other type of content by learning patterns from existing training data.

The purpose of AI algorithms or models are to mimic human intelligence across wide range of applications.

The purpose of generative AI algorithms or models is to generate new data having similar characteristics as data from the original dataset.

Use Cases of Generative AI

  1. Content Generation − It can create articles, blogs, and other kinds of content.

  2. Chatbots and Virtual Assistants − You can implement chatbots for customer support and service with generative AI.

  3. Deepfake Creation − Generative AI can be used for mimicking individuals by applying deepfakes.

  4. Text Summarization − You can use it to create concise and readable summaries of complex reports or any other kind of communications.

  5. Code Generation − Its capabilities can be used for writing, understanding, and debugging of any code.

  6. Unique Art Creation − You can use generative AI to create photorealistic art in specific styles.

  7. Music Composition − You can compose music with specific styles or tones using generative AI.

Benefits of Generative AI

我们上面已经看到生成式 AI 可用于诸多商业领域。让我们来看看实施生成式 AI 的部分好处 -

We have seen above that generative AI can be used in many areas of business. Let’s see some of the benefits of implementing generative AI −

  1. It automates the process of writing content.

  2. It reduces the effort of replying to emails.

  3. It improves the response to technical queries.

  4. It simplifies the content creation process.

  5. It creates a realistic representation of individuals.

Limitations of Generative AI

以下是生成式 AI 应用程序在使用过程中需要考虑的一些限制 -

Below are some limitations to consider during the use of generative AI applications −

  1. Lack of Source Verification − It does not always verify the source of content.

  2. Ethical Concerns − There can be misuse of generated AI content such as deepfakes.

  3. Coherence Overemphasis − It may generate biased outputs because of overemphasis on coherence.

  4. Creativity Issues − Sometimes it is difficult for generative AI to generate truly creative outputs.

  5. Fine-tuning Complexity − It may be challenging to fine-tune the models for specific tasks.

  6. Dependency on Training Data − The output is dependent on the quality of training data.