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Open AI launched DALL-E, an AI platform
2021 designed to generate images from textual
descriptions
Two notable AI image-generating tools,
the open-source Stable Diffusion and the
proprietary Midjourney were introduced and 2022
ChatGPT was introduced
OpenAI released GPT-4, an advanced version of its
GPT series. Also Microsoft Copilot (previously Bing
2023 Chat), Google Gemini (previously Google Bard),
Adobe Firefly, Meta Llama were introduced
Generative AI vs Conventional AI
Generative AI and Conventional AI represent two different approaches in the field of artificial intelligence. The
difference between them is given in the following table:
Generative AI Conventional AI
Goal Generative AI creates new content that Conventional AI analyses, processes, and
mimics the original content. This content classifies data. It works to improve the
includes images, text, music, or other accuracy, precision, recall, and speed within
forms of media. the scope of the defined task.
Training Generative AI models are often trained Conventional AI models are typically
using techniques such as generative trained using supervised, unsupervised, or
adversarial networks (GANs), variational reinforcement learning techniques.
autoencoders (VAEs), or autoregressive
models.
Dataset Generative AI models typically Conventional AI models rely on smaller,
require large amounts of diverse and more curated datasets that are tailored to
representative data to learn effectively. the task at hand.
These datasets often contain thousands or
even millions of examples across various
categories or classes.
Output Generative AI output is fresh, innovative, Conventional AI produces more predictable
and often unexpected. output based on existing data.
Applications Generative AI is used in the fields of art, Conventional AI is used in banking,
music, literature, gaming, and design. healthcare, image recognition, and language
processing.
Introduction to Generative AI 331

