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What is generative AI?

Generative AI is a class of artificial intelligence models and algorithms that have the capability to generate new content—such as images, text, audio, and other synthetic data—based on patterns and examples derived from existing data. These models use machine learning techniques to understand and replicate the underlying structure of the data on which they were trained. They can then create new content that's similar in style or distribution to that data.

Whereas traditional AI systems analyze data and make predictions (pattern recognition), generative AI creates new data similar to its training data (pattern creation).

Generative AI operates on the machine learning concept of neural networks. Like neurons in the human brain, neural network nodes receive input data, perform computations, and pass the results to the next layer of neurons. Some neural network models are ideal for generating text, images, and video. Examples include the following:

  • Large language models (LLMs) are a type of AI trained on vast amounts of data. They can understand and generate natural language, performing a wide range of tasks like answering questions, summarizing text, and even assisting in creative writing or generating code.
  • Generative pre-trained transformers (GPTs) are AI models that use a popular deep-learning model called transformer architecture. They’re pre-trained on large datasets of unlabelled text and can generate human-like content. They’re used in natural language processing tasks, including generative AI applications like ChatGPT.

Other neural network models are well-suited for generating data sequences and natural language processing. Some examples include the following:

  • Recurrent neural networks (RNNs) process sequential data using a feedback loop for generating text, machine translation, and speech recognition.
  • Long short-term memory (LSTM) networks handle long-term dependencies in sequences and are often used in natural language processing.
  • Feedforward neural networks (FNNs) are a simple type of neural network used for classification and regression tasks.
  • Transformer networks use self- and multi-head attention, encoding and decoding, and layer normalization, among other techniques, to weigh the importance of different words or tokens, understand their dependencies and relationships, and prevent information leakage during sequencing and natural language processing tasks.

In an IT setting, organizations can use a variety of neural network architectures for tasks such as generating responses to IT or security incidents or automating CI/CD pipelines.