From Recognition to Creation: Exploring the Differences between Traditional AI and Generative AI

I have been exploring the differences between traditonal AI vs generative AI and thought to document and share here in case it’s helpful for anyone else.

Traditional AI Recognizes, While Generative AI Creates

Traditional AI, also known as discriminative AI, is designed to classify or recognize patterns in existing data. For example, image recognition, speech recognition, or sentiment analysis are all examples of usual AI tasks. These systems use labeled data to learn patterns and make predictions about new, unseen data.

Generative AI, on the other hand, is designed to create new data that resembles existing data. It’s a type of AI that can generate original content that it has never seen before. Generative AI uses unsupervised learning to learn the underlying patterns of a dataset, and then uses that knowledge to generate new data points that have similar characteristics. Examples of generative AI include image synthesis, text generation, music composition, and video synthesis.

Understanding the Key Differences and Applications for Generative AI vs. Traditional AI

Traditional AI follows the rules, while Generative AI breaks the mold.

Traditional AI is typically designed to follow predefined rules or patterns, while generative AI is capable of creating something new and unique by going beyond those rules. Generative AI has the potential to be more innovative and creative than traditional AI, which is focused on recognizing existing patterns and structures.

Here are some examples of how generative AI goes beyond the traditional AI capabilities.

Image recognition vs. image synthesis: Usual AI is often used for image recognition tasks, such as identifying objects or faces within an image. In contrast, generative AI can be used for image synthesis, which involves creating new images from scratch based on learned patterns.

Sentiment analysis vs. text generation: Sentiment analysis is a usual AI task that involves classifying text as positive, negative, or neutral. Generative AI, on the other hand, can be used for text generation, which involves creating new text that resembles existing data.

Speech recognition vs. speech synthesis: Speech recognition is a usual AI task that involves transcribing speech into text. In contrast, generative AI can be used for speech synthesis, which involves creating new speech that sounds like a human voice.

Personalization vs. creativity: Usual AI can be used for personalization tasks, such as recommending products based on past behavior. Generative AI, however, can be used for creative tasks, such as generating new and unique content that has never been seen before.

These examples demonstrate how usual AI and generative AI can be used for different types of tasks, with usual traditional AI focusing on classification and recognition, while generative AI focuses on creating new content based on learned patterns. By using concrete examples like these, you can help people understand the key differences between these two types of AI.

A Collection of Real-World Examples of Generative AI

DeepDream: DeepDream is a computer vision project developed by Google that uses a generative neural network to find and enhance patterns in images. The results can be surreal and dreamlike, and often resemble hallucinatory or psychedelic images.

GPT-3: GPT-3 (Generative Pre-trained Transformer 3) is a language model developed by OpenAI that can generate human-like text with a high degree of coherence and accuracy. It can be used for a variety of natural language processing tasks, such as language translation, summarization, and even creative writing.

Aiva: Aiva is an AI-based music composer that uses a generative neural network to create original compositions. Users can input parameters such as mood and genre, and Aiva will generate a piece of music that fits those parameters. Aiva has been used in several commercial projects, including video games and short films.

StyleGAN: StyleGAN (Style-based Generative Adversarial Networks) is a generative model that can create realistic images of faces, animals, and other objects. The model uses a combination of unsupervised learning and adversarial training to generate new images that are both diverse and realistic.

Salesforce Einstein GPT: Einstein GPT (the world’s first generative AI for CRM) allows you to generate trusted content from your CRM data. That means that every piece of content generated, whether it’s an email, a report, a knowledge article or a piece of code, is highly relevant to your customer. Ref:

The following article highlights that combining Einstein GPT with Flow, users can create and modify automations using a conversational interface, which radically simplifies the flow creation process and significantly lowers barriers for non-technical users:


The main difference between usual AI and generative AI is that usual traditional AI is used to classify or recognize patterns in existing data, while generative AI is used to create new data that resembles existing data.

While traditional AI has made great strides in enabling machines to learn and solve problems, generative AI represents a major breakthrough in the ability of machines to create new content and ideas.

By providing machines with the ability to generate complex and sophisticated outputs, generative AI opens up new possibilities for creativity, innovation, and human-machine collaboration.

Whether it is in the realm of art, music, literature, or scientific discovery, generative AI has the potential to transform the way we think about and approach problem-solving.

Fun Question: Check if you can distinguish between music created by AI vs humans: