Generative AI

Generative AI is a subset of Artificial Intelligence focused on creating content, such as text, images, audio, or code, by learning patterns and structures from existing data. Whereas Discriminative Models primarily predict labels or other targets from inputs, generative systems learn to produce samples resembling their training data. Common approaches include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and generative models based on Transformers. Recent growth has been supported by advances in Deep Learning, especially Large Language Models (LLMs) and Diffusion Models—the latter particularly influential in image generation—alongside large training datasets and increased computing capacity. Prominent systems include OpenAI’s ChatGPT, Google DeepMind’s Gemini, and Anthropic’s Claude. Many modern generative models, especially LLMs, are pretrained using Self-Supervised Learning and may undergo additional training using Reinforcement Learning from Human Feedback (RLHF) to improve output quality and AI Alignment; neither method is universal, and RLHF does not guarantee these outcomes. Important hardware for training includes Graphics Processing Units (GPUs), supplied by companies such as NVIDIA, and Tensor Processing Units (TPUs), designed by Google.