Generative AI (GenAI) is the latest subtype of AI that broadly describes models or algorithms. GenAI is reshaping the landscape of creativity and innovation. You all know about , 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.
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
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.
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
- Content Generation − It can create articles, blogs, and other kinds of content.
- Chatbots and Virtual Assistants − You can implement chatbots for customer support and service with generative AI.
- Deepfake Creation − Generative AI can be used for mimicking individuals by applying deepfakes.
- Text Summarization − You can use it to create concise and readable summaries of complex reports or any other kind of communications.
- Code Generation − Its capabilities can be used for writing, understanding, and debugging of any code.
- Unique Art Creation − You can use generative AI to create photorealistic art in specific styles.
- Music Composition − You can compose music with specific styles or tones using generative AI.
Benefits of Generative AI
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 −
- It automates the process of writing content.
- It reduces the effort of replying to emails.
- It improves the response to technical queries.
- It simplifies the content creation process.
- It creates a realistic representation of individuals.
Limitations of Generative AI
Below are some limitations to consider during the use of generative AI applications −
- Lack of Source Verification − It does not always verify the source of content.
- Ethical Concerns − There can be misuse of generated AI content such as deepfakes.
- Coherence Overemphasis − It may generate biased outputs because of overemphasis on coherence.
- Creativity Issues − Sometimes it is difficult for generative AI to generate truly creative outputs.
- Fine-tuning Complexity − It may be challenging to fine-tune the models for specific tasks.
- Dependency on Training Data − The output is dependent on the quality of training data.