Generative AI

What is Generative AI

Generative AI is a subset of artificial intelligence that focuses on creating new content. Unlike traditional AI, which typically analyzes or classifies existing data, generative AI models produce new data that mimics the characteristics of the training data. These systems can produce text, images, and audio, simulating human creativity and extending AI’s capabilities into areas requiring imaginative and contextual understanding.

Key Concepts of Generative AI

Generative Models

Generative models are algorithms that learn the probability distribution of the training data and generate new samples from this distribution. They are the foundation of generative AI. 

Latent Space

Latent space is the representation of complex data in a more compact form. It is often referred to as a lower-dimensional space. Generative models often map input data to latent space, allowing for the creation of new samples by sampling from it. Latent space representations enable the disentanglement of underlying features and facilitate the manipulation and synthesis of data. Stated another way, latent space captures the underlying structure to gather similarities in that data. This enables it to combine these similarities when creating new information.

Training Data

High-quality and diverse training data is crucial for the performance of generative AI models. Models learn patterns and structures from training data, so a varied and representative dataset leads to better generalization and production of new content. High-quality training data ensures that the generated samples capture the underlying characteristics of the data distribution.

Loss Functions

Loss functions train generative AI models by quantifying the difference between the generated samples and the actual data. Different loss functions are employed based on the generative model type and the generated samples’ desired characteristics. Common loss functions include adversarial loss, reconstruction loss, and regularization terms.

Sampling Techniques

Sampling techniques help create new data samples from generative models. These techniques vary depending on the model architecture and the type of data being generated, and they play a crucial role in controlling the diversity and quality of the generated samples. Common sampling techniques include random sampling, greedy sampling, and temperature scaling.

Evaluation Metrics

Evaluating the performance of generative AI models is challenging due to the subjective nature of generated content. Various evaluation metrics, such as inception score, Frechet Inception Distance (FID), and perceptual similarity metrics, are used to assess the quality and diversity of generated samples. These metrics provide quantitative measures of the fidelity and realism of the generated content.

Conditional Generation

Conditional generation involves spawning data samples conditioned on specific input variables or attributes. Conditional generative models, such as conditional Generative Adversarial Networks (GANs) and conditional Variational Autoencoders (VAEs), allow for the generation of highly customizable and contextually relevant content. Conditional generation enables fine-grained control over the generated outputs and facilitates tasks like image-to-image translation and text-to-image synthesis.

Types of Generative AI

Generative AI encompasses a variety of techniques and architectures for creating new data. Some of the most prominent types of generative AI include:

  • Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that compete in a game-theoretic framework. The generator learns to produce realistic samples, while the discriminator learns to distinguish between original and generated samples. GANs have been successfully applied to tasks like image generation, style transfer, and image-to-image translation.
  • Variational Autoencoders (VAEs):VAEs are probabilistic generative models that learn to encode input data into a low-dimensional latent space and then decode it back to reconstruct the original data. VAEs can generate new samples that resemble the training data by sampling from the learned latent space.
  • Autoregressive Models:Autoregressive models are probabilistic generative models that learn to predict the probability distribution of each element in a sequence given the previous elements. These models generate new samples by iterative sampling from the learned probability distribution. Autoregressive models are commonly used for text generation, speech synthesis, and time series prediction.
  • Transformer Models:Transformer models are a class of neural network architectures that use self-attention mechanisms to capture long-range dependencies in sequential data.

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Generative AI
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