Conversational AI vs. generative AI: What's the difference?
As the world is getting used to artificial intelligence, there are multiple landscapes which are making lives easy. We will talk about conversational AI and generative AI, which are majorly used across the world today, and it is being used to work in various fields.
AI Landscape
Artificial intelligence (AI) is an integral part of our technological landscape in today’s time, which offers diverse applications under its broad umbrella. There are two distinctive branches which emerged conversational AI and generative AI. Both artificial intelligence will support their own set of capabilities. In this article, we will be talking about the same in detail.
Details about Conversational AI
Conversational AI is a technology which marvels at the design for enabling seamless interactions between humans and machines. Operating in a manner for human conversations, it finds applications in chatbots, virtual assistants and messaging apps. Prominent examples of conversational AI will further include widely used platforms like Google Assistant, Alexa and Siri.
How does conversational AI work?
Conversational AI models undergo training on datasets comprising human dialogues to comprehend language patterns. Leveraging natural language processing and machine learning, these models craft appropriate responses by translating human conversations into machine-understandable languages. Knowledge bases unique to each company form the backbone, continually enriched by human interactions and updates.
Details about the Generative AI
Generative AI empowers the users to generate new content, images, text, spanning animation and sounds, through machine learning algorithms- a mathematical way to map methods which are used to learn the underlying patterns which have been embedded in the data.
There are two technologies behind generative AI- deep learning and neural networks, enabling it to create the outputs independently. Notable examples further include Google Bard, ChatGPT and Jasper AI.
How to navigate generative AI processes?
Generative AI depends on neural networks to identify the pattern within the training data, with the generating of new content which is based on predictions derived from these learned patterns.
Various learnings including supervised learning, further involve human response and feedback to enhance the accuracy of the generated content. The foundation models like GPT-4 and PaLM 2 serve as versatile bases for multiple AI tasks.
Difference between Conversational AI and Generative AI
While both conversational AI and generative AI contribute to the AI landscape, they serve different purposes with distinct functionalities:
Objectives and goals for Conversational AI
- It focuses on making and enhancing human-like conversations- verbally.
- It allows the user to experience customer service, chatbots and virtual assistants.
- It has been trained on large datasets with human queries, input, responses and conversations.
- It majorly does the conversations and drives the creation of responses.
Objective and goals of Generative AI
- It concentrates on creating content autonomously in various forms.
- Utilized for generating works of fiction, marketing content, and meta descriptions.
- Trained on diverse datasets to learn patterns for creating content with predictive capabilities.
- Utilizes input and learned patterns to autonomously generate new content.
Coexistence of Conversational AI and generative AI
- Conversational AI and generative AI are not mutually exclusive when we talk about the content which they share.
- Platforms like ChatGPT illustrate the functioning of both conversational AI, being a chatbot, and generative AI, contributing to creating the content respectively.
- Conversational AI represents a specific application within the broader scope of generative AI, which further showcases the versatility and collaborative potential within the AI landscape of the future.
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