Everyone is aware of AI; today, it has become an integral part of our lives, be it in personal or professional settings. But artificial intelligence is a large umbrella term, and it encompasses various versions and types of this technology. You wouldn’t use a “needle where a sword is needed, or a sword when a needle will do the work.” Therefore, it is important to understand the difference between each to make the best out of these advancements. Keep reading to understand in detail how generative AI vs conversational AI are best used based on their differences.
What is Conversational AI?
The core of conversational AI is to facilitate communication between humans and computers. It focuses mainly on understanding user inputs, understanding the context, and generating responses through natural, human-like conversations. Basically, this AI enables machines to talk to you like another human, and this ability is what makes an AI conversation.
Examples: Apple Siri, Amazon’s Alexa, Google Voice Assistant, IBM Watson Assistant.
Technical base of conversational AI?
- Natural language processing (NLP) is the foundation of conversational systems that helps machines understand human language by breaking texts into tokens and also to probe the grammar, meaning, and intent of input.
- Natural language understanding (NLU) is a subpart of NLP that does the probing work to understand user intent and extract the task to be done.
- Dialogue management systems act as a brain deciding what to say next. It tracks the context of conversation and selects a response based on rules or ML models.
- Automatic speech recognition (ASR) is used for voice-based systems to convert spoken language into text for processing. Whereas context management and memory models are used to remember, track, and maintain the context across multiple turns of conversation.
- Integration frameworks are used to connect AI to backend systems to perform tasks like status checking or booking anything.
What is Generative AI?
The core purpose of generative AI is to create new content, such as text, visual, and audio, that did not exist before. You can make music, create art, write essays, blogs, poems, and more, and make a film from scratch. It makes something new from the vast data sets it is provided to learn from.
Example: ChatGPT, Gemini AI, Grok, Claude, Perplexity.
Technical base of generative AI?
Generative AI is built on advanced artificial intelligence models, and the primary foundations are LLMs and deep learning networks.
- Transformers are the backbone of modern-day AI systems that use a self-attention mechanism. Transformers are the ones responsible for giving you sequential responses as they read the entire input word by word, relate words to each other, and generate the next relevant word. And that’s what makes your conversations feel human.
- Large language models’ architecture is built on transformers and is trained on a huge amount of text. It learns patterns from that data and predicts the next word) step-by-step. It is a core driver behind text generation.
- Generative Adversarial Networks (GANs) are resposible highly realistic images and videos. It is a system of two neural networks called a generator (creates fake data) and a discriminator (responsible for checking for real or fake).
- Diffusion models are another key element of GenAI, creating high-resolution, realistic images by removing noise one step at a time based on a prompt.
- Variational Autoencoders (VAEs) are useful to compress data into small and simple versions for better understanding of computers, and then that small version is used to create new, similar data. This process makes computers create new outputs that mimic the original ones.
Is ChatGPT generative AI or conversational AI?
This is the most debatable topic when it comes to discussion on new-day AI applications. OpenAI’s ChatGPT rapidly captured the market and has become an inevitable option among AI platforms. But what type of AI is it actually?
To answer this question in one word and straightforwardly, without any ifs, ChatGPT is a generative AI at its core. It is just presented as a conversational AI. Their core capabilities and foundation are generative, but what makes an AI conversational is the way we interact with it and not the fundamental functionalities. For example, we generally talk to ChatGPT as if discussing things or taking assistance or suggestions from another person.
Types of Artificial Intelligence Comparison
| Classification | Type | Description | Examples of Tasks | Example of Systems |
|---|---|---|---|---|
| By Means of Capability | Artificial Narrow Intelligence (ANI) | It is often known as ‘Weak AI’ and is used to perform a particular task, and can’t think beyond its designated task. | Generating texts, images, voice commands, and playing chess or driving a car. | ChatGPT, Siri, AlphaGo |
| Artificial General Intelligence (AGI) | Called to be ‘Strong AI,’ though it only exists in theories and is yet to become a reality. This AI is seen as capable of performing any intellectual task a human is capable of. | Execute intellect across diverse domains and solve beyond assigned tasks or something that is not explicitly trained for. | Not yet developed | |
| Artificial Superintelligence (ASI) | It is just a hypothetical form beyond AGI, something that is thought to surpass human intelligence in every aspect inlcuding creativity, wisdom, and social skills. | Solving global-scale problems and outperforming human cognitive abilities. | Doesn’t exist/ futuristic | |
| By means of functionality | Reactive Machines | The most basic and simple form of artificial intelligence, as the name suggest it only responds to current scenarios and does not store memories or use past experience to make patterns or decisions. | Chess moves and basic decisions in games. | |
| Limited Memory | These AI models use memory and past data to improve decision-making over time. | Automatic driving cars, customer support chatbots, and recommendation systems | ChatGPT, Self-driving car AI | |
| Theory of Mind | It is also just a theoretical concept thought to believe that AI can understand human emotions, beliefs, and thoughts, which will allow it to act socially. | Emotional intelligence, adaptive personal assistants and negotiation tasks. | Just in theories and not fully developed | |
| Self-Aware AI | It is the final and hypothetical level of AI where we think AI can have its own consciousness and can recognize its own inner state. | Ethical reasoning, self-improvement, and independent decision making. | Hypothetical and futuristic |
FAQs
Which are the best generative AIs?
Now the artificial intelligence market is flooded with AI tools and platforms but you should choose one as per your needs and requirements. This generative AI tools list will help you choose the best for you:
- Claude, Jasper, ChatGPT, and Writer for text, code, and writing purposes.
- DALL-E 3, Midjourney, Stable Diffusion, Leonardo AI, and Canva Magic Studio for realistic image and graphic generation.
- Lumen5/VEED.io, OpenAI’s Sora, Pictory, Gemini’s Veo 3, and Synthesia for ultra-realistic video content generation and editing purposes.
- Suno, Murf, Udio, and more can be used to create audio, voice, and music.
- Perplexity Pro, NotebookLM, and Elicit are great options for deep research and knowledge.
Are chatbots generative AI?
No. Chatbots fall under the category of conversational AI at their core. But you also need to understand that all generative AI chatbots are conversational AI, but not all chatbots can be called generative AI.
Bottom Line
In terms of types of artificial intelligence comparison, both GenAI and conversational AI are different; they are based on different technologies and made for different purposes. Though some parts of the technical foundations of both intelligences overlap and yet complement each other. We also learnt that different types of artificial intelligence are yet to be developed or are super futuristic. Each AI tool and platform serves a different purpose, the key to using them is understanding your needs and choosing the right option.
