GENERATIVE AI: AN EXPERT GUIDE

Practically speaking, generative AI encompasses a a category of artificial intelligence focused on using machine learning models to generate new content, rather than simply analyze existing content. The recently viral chatbot ChatGPT is one example of this technology.

On a base level, generative AI mimics human creativity to produce outputs like text, code, images, videos, sounds and more that are completely original yet often indistinguishable from content created by humans. That differs from most AI today which is designed to classify or extract insights from data that humans have provided to it.

Powerful generative models are trained on massive datasets of diverse, high-quality examples of outputs ranging from text to images. By exposing the system to such datasets, spanning hundreds of gigabytes to even petabytes of data, the generative model learns the patterns and structures of how we humans construct everything from sentences to sketches.

From this foundation, the technology then utilizes self-learning algorithms to build the capability to autonomously generate brand new outputs by predicting sequences of words, pixel placements, notes of music and more. With sufficient data and compute power, generated content can reach remarkable levels of coherency and realism.

Leading techniques in generative AI include auto-regressive models, generative adversarial networks, diffusion models and models based on the transforming autoencoder architecture. Companies invested heavily in advancing the state-of-the-art in generative AI include OpenAI, Google, Nvidia and more.

The applications of generative AI span content creation, creative workflows, programming assistance, drug discovery, data augmentation and much more.  Ethical concerns remain around topics like bias, misinformation, copyright and automation impacting jobs, though.

Going forward, generative AI appears poised to expand human creativity by assisting where needed rather than replacing it outright. With thoughtful coordination across public and private sector stakeholders, responsible development of these creative AI technologies could provide enormous social and economic benefit.