1.1. What’s generative AI? #

Generative AI is a class of artificial intelligence models designed with the specific goal of creating new data samples that resemble a given dataset.

Unlike discriminative models, which focus on classifying or distinguishing between existing data points, generative models aim to understand the underlying data distribution in order to produce novel instances of data. The concept originates from probability theory and statistics but has seen a vast expansion in scope and complexity due to advancements in machine learning techniques and computational resources.

The most prominent architectures in the realm of generative AI include generative adversarial networks, variational autoencoders, and more recently, diffusion models. These architectures serve various purposes: GANs are excellent at generating high-quality and realistic images; VAEs are well-suited for generating new samples while also offering a structured latent space; and diffusion models have found success in applications that require iterative refinement of generated data, like image denoising or text-to-image tasks.

The applications of generative AI are extensive and cross-disciplinary. In the creative industries, for instance, these models have been used for art creation, music composition, and even scriptwriting. In science and healthcare, they find applications in drug discovery, simulating molecular structures, and generating synthetic medical data for research. Generative AI plays a crucial role in data augmentation, particularly useful in scenarios where acquiring real-world data is challenging or ethically problematic.

While generative AI has seen tremendous success, it is essential to consider the implications of its capabilities. For example, it can generate deepfakes that convincingly replace a person’s likeness and voice, or produce synthetic data that may inadvertently encode and perpetuate existing biases. Alongside the technological advancements, there is a parallel track of ethical and governance considerations that guide how this technology should be used responsibly.