Dive deeper: The difference between conversational AI and generative AI

Artificial Intelligence (AI) has two (2) types that change how we interact with machines and the world around us. Generative AI and conversational AI have garnered immense attention and have found their indelible presence across various industries. In this article, we delve into the defining features of these advanced AI systems, explore their importance and applications, and shed light on the key differences that set generative AI apart from conversational AI.

What is generative AI?

Generative AI, also known as ‘creative AI’, is capable of creating a wide variety of materials, such as images, videos, audio, text, 3D models, or even other data. This AI learns from existing patterns in human-created work, and using this knowledge, it can generate new and unique output using advanced algorithms. Take a look below at the key features and capabilities of generative AI:

Key features of generative AI

1. Content creation - Generative AI uses patterns and structures in the learning process to create a wide variety of new content relevant to the learning data, including texts, images, music, and even videos.
2. Creative application - It can generate unique and original outputs based on the data it has been trained on. The capacity to generate original content makes generative AI a strong tool in various creative disciplines.
3. Learning from data - To improve the quality and diversity of created outputs, generative AI algorithms learn from wide datasets. By training on large and diverse datasets, generative AI models can better understand the underlying patterns and provide more realistic results.

Generative AI examples in industries: Where can it be used?

• Health care - Generative AI can be utilised in health care to improve the MRI, CT, and PET scans for faster rending, image quality, and speeding up the results response times to injuries.
• Finance - In this industry, generative AI can help analyse financial data and prepare insights and financial plans to consider while investing.
• ChatGPT - It’s used by many people and is mostly used in any aspect and field of work. Actively using this AI solution for their different requirements. Its incredible features have made content creation easier and faster.
• Google Bard - It is designed to function similarly to ChatGPT, with the main distinction being that Google's service will obtain its data from the internet.

What is conversational AI?

Chatbots, virtual assistants, and other AI-powered systems may utilise this technology to engage and communicate with people in a human-like manner. Natural language processing (NLP) and machine learning (ML) are used in conversational AI to interpret and reply to user inquiries, delivering personalised support and increasing consumer experiences. Learn more about the features of conversational AI below:

Key features of conversational AI

1. Natural language processing - This AI converts spoken language into text, processes user voice commands and uses Natural Language Understanding (NLU) to analyse context and intent. It then generates meaningful responses in real-time using Natural Language Generation (NLG) techniques like templates, machine learning, or neural networks.
2. Conversational AI application - It is made by using natural language processing (NLP), natural language generation, machine language (ML), and a wide range of learning data. Here are the most common areas where conversational AI is used in applications:

• Virtual assistants: Well-known virtual assistants that provide personalised assistance to perform tasks based on user commands. These are Apple’s Siri, Amazon’s Alexa, and Google Assistant.
• Customer support: Chatbots that handle common queries and guidance for user’s self-service options.
• Language translation: A real-time translation between several languages can help to break down language barriers and enable global communication.
• Voice-controlled interfaces: Devices and systems integrate voice commands, offering hands-free control and better accessibility.

3. Dialogue management: This technique keeps discussions cohesive and context-aware. These algorithms allow the AI system to recognise and respond to user input in a natural, human-like manner.

Conversational AI examples in industries: Where can it be used?

• Health care - Conversational AI can be utilised in health care by helping patients schedule appointments and providing information about their next visit. It can also assist patients in organising their papers ahead of time to simplify medical processes.
• Finance - It assists you in resolving client queries by automating repeated activities that might take longer if an individual directly handles them. Automation enables greater efficiency and speedier resolution, improving your trustworthiness, which is critical in the banking industry.
• Customer service - A consumer can talk with a virtual assistant or virtual agent (AI assistant) while reviewing their billing details on the company's website in Billing Support and Complaint Management. With an offer, the virtual assistant can also upsell packages. 
• Internet of things (IoT) devices - Devices that use speech recognition to connect with users, such as managing home appliances via Google Home or Amazon Alexa, are becoming quite useful in modern houses.

Comparing conversational AI and generative AI: what sets them apart?

When comparing generative AI vs conversational AI, their differences become apparent, spanning from their primary objectives to their diverse applications. At the core of this is the fact that conversational AI is designed to imitate human conversations, while generative AI excels in creating new and diverse content. See more of their differences below:

Generative AI		Conversation AI
The user inputs data and generates new content as output	Input/Output	User language as input and human-like language response as output
Create new content	Purpose	Communication between humans and machines
Generate a wide range of content type	Capabilities	Understand and respond to human language only
Focused on analysing patterns in the data	Training Data	To understand and respond to human language accurately
Art, music, video, and even fashion design	Use Cases	Customer service, language translation, and personal assistant
Designed to be creative and generate original content	Realism vs. Creativity	Aimed to be as realistic and human-like as possible in its responses
Improve its output through reinforcement learning and other techniques	Feedback Loop	Enhance its accuracy and effectiveness through feedback from users

QWERTYLABS: Bridging the gap between generative AI and conversational AI

The main purpose of Generative AI is to create new content such as text, graphics, and even music depending on patterns and data inputs. Conversational AI, on the other hand, uses natural language processing (NLP) and machine learning (ML) to enable human-like interactions with users. 

When comparing generative AI vs conversational AI, assessing their distinct use cases, strengths, and limitations is essential, especially if you have specific areas you want to integrate them into. Analyse their unique purpose, capabilities, and application of creative output, as well as customised interactions when businesses seek to optimise customer engagement and streamline content generation processes.

Here at QWERTYLABS, we will help you evaluate user behaviour, identify patterns, interpret user intent, and enhance your website for better SEO. Contact us today to unlock the full potential of your SEO strategy!

Frequently asked questions

How does generative AI make content?

Generative AI relies on deep learning models, such as GPT-3, trained on vast text data. These models learn to generate text by predicting the next word in a sequence, resulting in coherent and contextually relevant content.

How does conversational AI benefit businesses?

Conversational AI can enhance task efficiency by handling routine customer inquiries, reducing response times, and providing consistent support, ultimately improving customer satisfaction and loyalty.

What industries are likely to benefit the most from conversational AI?

Industries such as healthcare, e-commerce, and customer service are poised to benefit significantly from conversational AI due to its ability to streamline processes and enhance user experiences.