Conversational AI vs. generative AI: What's the difference?
As AI continues to evolve, understanding the differences and collaborative potential of conversational AI and generative AI is vital to their role in shaping the digital landscape.
AI is a large umbrella with various applications underneath. Two prominent branches have emerged under this umbrella -- conversational AI and generative AI.
While conversational AI and generative AI may work together, they have distinct differences and capabilities. Artificial intelligence (AI) changed the way humans interact with machines by offering benefits such as automating mundane tasks and generating content. AI has ushered in a new era of human-computer collaboration as businesses embrace this technology to improve processes and efficiency.
Learn the differences between conversational AI and generative AI, and how they work together.
What is conversational AI?
Conversational AI is a technology that helps machines interact and engage with humans in a more natural way. The interactions are like a conversation with back-and-forth communication. This technology is used in applications such as chatbots, messaging apps and virtual assistants. Examples of popular conversational AI applications include Alexa, Google Assistant and Siri.
What is generative AI?
Generative AI enables users to create new content -- such as animation, text, images and sounds -- using machine learning algorithms and the data the technology is trained on. Generative AI uses deep learning and neural networks to create outputs. Examples of popular generative AI applications include ChatGPT, Google Bard and Jasper AI.
Learn more about different AI content generators.
How does conversational AI work?
Conversational AI models are trained on data sets with human dialogue to help understand language patterns. They use natural language processing and machine learning technology to create appropriate responses to inquiries by translating human conversations into languages machines understand.
The knowledge bases where conversational AI applications draw their responses are unique to each company. Business AI software learns from interactions and adds new information to the knowledge database as it consistently trains with each interaction. Humans also update these knowledge bases.
Conversational AI may also use predefined responses -- or rule-based systems -- for initial responses.
Learn more about the dos and don'ts of training a chatbot using conversational AI.
How does generative AI work?
Generative AI uses neural networks to identify patterns and other structures in its training data. It then generates new content based on predictions from these learned patterns. There are various learning approaches to train generative AI such as supervised learning, which uses human response and feedback to help generate more accurate content.
Organizations can create foundation models as a base for the AI systems to perform multiple tasks. Foundation models are AI neural networks or machine learning models that have been trained on large quantities of data. They can perform many tasks, such as text translation, content creation and image analysis because of their generality and adaptability. Examples of foundation models include GPT-4 and PaLM 2.
Main differences between conversational AI and generative AI functionality
Conversational AI and generative AI have different goals, applications, use cases, training and outputs. Both technologies have unique capabilities and features and play a big role in the future of AI.
Here is a breakdown of the differences between the two:
- Objectives and goals. Conversational AI focuses on human conversations while generative AI focuses on creating content in various forms.
- Use cases and applications. Conversational AI is used for applications such as customer service, virtual assistants and chatbots. Generative AI can be used to write works of fiction, marketing content and meta descriptions.
- Learning and training data. Conversational AI is trained on large data sets with human input, conversations, user queries and responses. Generative AI is trained on different sets of data to learn patterns to create content with predictive patterns.
- Input and output. Users input information in a conversation when using conversational AI. Conversational AI uses this interaction to create responses. Generative AI uses input and data to generate new content by using learned patterns.
Are conversational AI and generative AI mutually exclusive?
While each technology has its own application and function, they are not mutually exclusive. Consider an application such as ChatGPT -- this application is conversational AI because it is a chatbot and is generative AI due to its content creation. While conversational AI is a specific application of generative AI, generative AI encompasses a broader set of tasks beyond conversations such as writing code, drafting articles or creating images.
