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AI Landscape
Artificial Intelligence (AI) is an integral part of our technology landscape today, offering diverse applications under its broad umbrella. Two distinct branches have emerged: Conversational AI and Generative AI. Both AIs will support their own set of capabilities. In this article, we will talk about the same in detail.
Details about Conversational AI
Conversational AI is a technology marvellously designed to enable seamless interactions between humans and machines. It works in a way that is suitable for human conversations and finds applications in chatbots, virtual assistants, and messaging apps. Notable examples of conversational AI will also include widely used platforms such as Google Assistant, Alexa, and Siri.
How does conversational AI work?
Conversational AI models are trained on human dialogue datasets to understand language patterns. Leveraging natural language processing and machine learning, these models formulate appropriate responses by translating human conversations into machine-understandable languages. Each company’s unique knowledge bases form the backbone, which are constantly enriched through human interactions and updates.
Details about generative artificial intelligence
Generative AI enables users to create new content, images, text, animations and extended sounds through machine learning algorithms – a mathematical way of mapping methods used to learn underlying patterns embedded in data.
There are two technologies behind generative AI: deep learning and neural networks, which allow it to generate outputs autonomously. Also, notable examples are Google Bard, ChatGPT, and Jasper AI.
How do we navigate generative AI processes?
Generative AI relies on neural networks to identify patterns within training data while generating new content based on predictions derived from these learned patterns.
Various learning processes, including supervised learning, involve human response and feedback to enhance the accuracy of the generated content. Basic models such as GPT-4 and PaLM 2 are versatile bases for multiple AI tasks.
The 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 functions:
Goals and objectives of conversational AI
- It focuses on conducting 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 containing human queries, inputs, responses and conversations.
- It mainly initiates conversations and prompts the creation of responses.
The goal and objectives of generative artificial intelligence
- Focuses on creating content independently in various formats.
- Used to create fiction, marketing content, and meta descriptions.
- Trained on various datasets to learn content creation patterns with predictive capabilities.
- Uses input and learned patterns to create new content independently.
Coexistence of Conversational AI and Generative AI
- Conversational AI and generative AI are not mutually exclusive when discussing shared content.
- Platforms like ChatGPT demonstrate the work of both Conversational AI, being a chatbot, and Generative AI, which contributes to content creation.
- Conversational AI represents a specific application within the broader scope of generative AI, which also showcases the diversity and collaborative potential within the future AI landscape.
Read also: Are you looking for a cheaper Samsung foldable smartphone? Here’s what the Korean giant has to say
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