The evolution of artificial intelligence has led to a transition from traditional Machine Learning (ML) to the new Generative AI. Here are some of the main differences between the two:

Focus and Application.

Traditional ML: Specialized and designed for specific tasks. For example, a model trained to recognize images of cats cannot recognize cars without new training.

Generative AI: Generalized and adaptive. It can handle a variety of tasks with the same model, thanks to its extensive training on diverse databases.

Nature of the Output.

Traditional ML: Descriptive, based on interpreting or categorizing existing information. For example, labeling an image as “cat” or “not cat.”

Generative AI: Creative, has the ability to produce new and original information such as writing a paragraph about cats or generating a new image of a cat.

Interactivity.

Traditional ML: Requires technical interaction, often through a specific interface or programming language.

Generative AI: Friendly and natural, allows interaction using colloquial language, making the technology more accessible to the average user.

Training and Data.

Traditional ML: Trained with specific and often limited datasets designed for concrete tasks.

Generative AI: Benefits from large datasets, often encompassing vast amounts of general information, which gives it a broad knowledge base and adaptability.

Typical Applications.

Traditional ML: Recommendation systems, predictive analysis, image classification, among others.

Generative AI: Advanced chatbots, virtual assistants, content generation (text, images, music), and simulations, among others.

In summary, while traditional ML has been a powerful tool for specific and well-defined tasks, the new Generative AI expands possibilities, allowing for more flexible, creative, and adaptive solutions. Interaction becomes more natural, and potential applications multiply, opening a new world of opportunities in the field of artificial intelligence.