What is Generative AI

Definition:

Generative AI is a branch of artificial intelligence that can create content, such as text, images, audio, video, code or synthetic data, from patterns learned during training.

Models generate outputs based on the instructions and information they receive. The fact that content is generated does not guarantee that it is original, accurate or suitable for use without review.

Origins and evolution of generative AI

Generative AI has its roots in probabilistic models, machine learning and neural networks. It did not begin with today’s conversational assistants.

In 2014, generative adversarial networks, known as GANs, introduced a method that pits two networks against each other: one generates samples and the other tries to distinguish them from real data. This process contributed to the development of image generation.

The transformer architecture, introduced in 2017 in Attention Is All You Need, enabled advances in language processing and subsequently in generative models. Diffusion models also advanced image generation through processes that learn to reverse the addition of noise.

From 2022 onwards, the growing popularity of conversational interfaces and visual tools brought these capabilities to users without programming skills.

How generative AI works

During training, a model adjusts its parameters to learn relationships within the data. When used, it applies those relationships to the input it receives to produce an output. This usage phase is called inference and does not, by itself, involve retraining the model.

The mechanism depends on the type of model. Many language models generate text by successively selecting AI tokens, units that can represent words, parts of words or punctuation. Diffusion models can generate images by progressively refining an initially noisy representation.

An instruction, a reference image or a document can guide the output. The available information and generation settings influence the response; the same request can produce variations.

The model does not simply work like a search engine retrieving a stored answer. However, it can reproduce fragments of its training data, so not every output should be assumed to be new.

What it can generate

Capabilities depend on the model and application used. Content types include the following:

• Text: drafts, summaries, translations and answers to questions.
• Images: illustrations, visual compositions and edits to existing images.
• Audio: synthetic speech, music and other sounds.
• Video: sequences generated or transformed from instructions and references.
• Code: proposed functions, queries and changes to programs.
• Synthetic data: artificial samples used for testing or training, whose usefulness must be verified.

Multimodal systems work with more than one type of information. They may, for example, receive an image and respond with text, although not all support the same combinations of inputs and outputs.

Differences from classification and prediction

The difference lies in the task being addressed. A classifier assigns a category, such as identifying an email as spam. A prediction system may estimate a quantity, such as demand for a product. A generative system produces content, such as a draft response.

These functions are not incompatible. A generative model can also be used to classify documents, and an application can combine generation with predictive models.

Generation and prediction are not opposing concepts: many language models produce text through successive predictions of the next token. The distinction describes the system’s use and output, not an absolute separation between technologies.

Applications and limitations

Generative AI can support content preparation, programming, document transformation and the creation of visual materials. Its usefulness depends on whether the output meets a specific need and can be reviewed against clear criteria.

In digital marketing, it can help prepare message variations or initial drafts. It does not replace knowledge of the audience, verification of claims or editorial decisions about what is worth publishing.

A fluent response can contain false information. Models can invent plausible facts, references or explanations, a problem known as hallucination. They can also reproduce biases, omit nuances or generate inconsistent results.

Review must be adapted to the content: checking claims, testing code and verifying the rights and terms of use of materials. Connecting the system to external sources can provide context, but does not automatically eliminate errors.