Key Concepts & Terms

Generative AI builds on key ideas from machine learning, where computers are trained to recognize patterns in data. One important technique in this process is called deep learning, which uses networks of interconnected nodes, similar to how our brains work, to understand and generate complex content. These networks, known as neural networks, help the AI learn and make decisions. 

Another key concept in generative AI is reinforcement learning, where the AI improves its output over time by receiving feedback, much like a student who gets better by learning from their mistakes and successes.

Generative AI finds applications across multiple domains. In the realm of language, Large Language Models (LLMs) like GPT-4 are employed in developing chatbots, content creation tools, and language translation systems. Diffusion models, another branch of generative AI, are used to create high-quality images and other types of content from textual descriptions, exemplified by tools like DALL-E. Meanwhile, Generative Adversarial Networks (GANs) are particularly effective in generating highly realistic images, such as human faces, and are widely used in creative industries and research. Check the video and the diagram for more.