Generative AI: A Primer
ChatGTP. DALL·E 2. Stable Diffusion. With media headlines across tech, business, and popular culture making household names of bleeding-edge research in generative AI, I thought it might be useful to put together a quick technical primer.
Generative AI is a subfield of artificial intelligence (AI) that focuses on developing algorithms to generate new, original content based on a set of inputs. Though relatively nascent, this capability is already impacting a wide range of fields, including art and design, media and communications, marketing and more. In this post I’ll try to outline the most important technical components of generative AI and discuss some of the leading techniques used to develop and train these systems.
Generative Adversarial Networks
Generative adversarial networks (GANs) are one of the more popular techniques for generative AI. GANs consist of two neural networks: a generator and a discriminator. The generator network produces synthetic content, while the discriminator network attempts to distinguish between real and fake content. The two networks are trained together, with the generator trying to produce increasingly realistic content and the discriminator trying to identify genuine and fake content correctly.
