Conversational AI and generative AI have both been growing in adoption and popularity:
However, although these tools might often have overlapping functionalities with each other, they are distinct tools that should be differentiated. Given that 60%1 of organizations are concurrently implementing four or more hyperautomation initiatives, business leaders must understand the technologies they are investing in to avoid time and monetary waste.
In this article, we will:
- Explain the top 3 differences between conversational AI vs generative AI
- Discuss how they can be jointly used
- Provide a list of tools that leverage both conversational AI and generative AI
What is conversational AI?
Conversational AI (or conversational artificial intelligence) creates intelligent systems capable of engaging in natural language conversations by leveraging
Conversational AI’s use cases are numerous, and it’s used for creating:
- AI chatbot
- Rule-based chatbot
- Hybrid chatbot
- Voice bots/assistants
- Intelligent virtual assistants
The chatbots and virtual assistants can be deployed on websites, messaging platforms, and smart devices – such as smartphones and smart speakers – for providing accurate, contextual, and human-like responses.
What is generative AI?
Generative AI focuses on creative content generation, such as texts, images, music, or videos by using:
Because of its flexibility and adaptability to different situations and contexts, generative AI’s use cases span across more number of functions and industries than conversational AI.
What’s the difference between conversational AI vs generative AI?
|Purpose & creativity
|Engages in human-like conversations in a rule-based manner by understanding queries and pulling answers from its knowledge base
|Learns from previous interactions and is trained on vast datasets to create new, unique outputs
|Leverages NLU, NLP, and NLG to understand and generate answers
|Uses ML, GAN, transformer models, and training data to create new content
|Limited to natural language interactions via voice or text to facilitate human-machine interaction
|Can create new content as video, images, or sound
How can conversational AI and generative AI be used together?
Conversational AI systems can leverage generative AI models for enhanced speech recognition and natural language generation. So instead of only relying on rule-based templates, conversational AI models can use the generative AI technology for more dynamic and context-aware responses.
For instance, a conversational AI system may use NLP techniques to understand the customer’s query and identify the intent behind it. It can then pass this information to a generative model that creates a response based on the context. The generated response is then returned to the user by the conversational agent for enhanced customer satisfaction.
Examples of tools that use both conversational and generative AI are:
Haptik is a conversational AI tool with generative AI capabilities for holding human-like conversations.
Haptik is powered by natural language processing (NLP), large language models (LLMs) and machine learning capabilities (ML). It can be used to create virtual assistants and intelligent chatbots, as well as for language translation.
This is a generative artificial intelligence chatbot used for generating human-like text based on the input provided. Its transformer model, GPT-4, to learn from large text datasets and produce realistic outputs.
DALL-E creates images from text descriptions. The DALL-E model learns from large datasets of images and text and general novel and diverse outputs. DALL-E is able to create images, artwork, product designs, or illustrations (Figure 1).
It’s a conversational AI voice assistant that can hold natural language conversations. Thanks to NLP, NLU, and NLG, it understands user inputs, extracts insights, and generates a relevant response. Siri makes phone calls, sends messages, sets reminders, and play music.
Jasper is an open-source platform used for music generation. Its deep learning model can compose original music in various genres and styles based on the input data, such as keywords, topic, tone, or emotion.
For more on conversational AI and generative AI
To learn more about conversational AI and generative AI, read:
And if you are looking to invest in one these solutions, visit our data-driven lists of:
If you have more questions, don’t hesitate to contact us:
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This post originally appeared on TechToday.