Generative AI

Generative AI, also known as Artificial Intelligence Generated Content (AIGC)

The advances are likely to increase, and generative design techniques are likely to come into the core curricula of data science, creative, and engineering professions globally. IBM Research

Al can not only boost our analytic and decision-making abilities but also heighten creativity. Harvard Business Review

Generative Al, which creates original artifacts or reconstructed content and data, is the next frontier. Gartner

Search for 'Generative AI' on Google

Up until recently, machines had no chance of competing with humans at creative work—they were relegated to analysis and rote cognitive labor. But machines are just starting to get good at creating sensical and beautiful things. This new category is called “Generative AI,” meaning the machine is generating, using machine learning/deep learning mainly, something new rather than analyzing something that already exists.

Every industry that requires humans to create original work—from social media to gaming, advertising to architecture, coding to graphic design, product design to law, marketing to sales—is up for reinvention.

Certain functions may be completely replaced by generative AI, while others are more likely to thrive from a tight iterative creative cycle between human and machine—but generative AI should unlock better, faster and cheaper creation across a wide range of end markets.

The current Generative AI landscape

The always difficult task of predicting the future

A general view of current Generative AI possibilities and main actors

OpenAI created and introduced GPT-3 (Generative Pre-trained Transformer 3) in 2020. This LLM leverages deep learning to generate text, code, images, etc.

DALL-E, an AI image generator by Open AI is a “neural network that creates images from text captions for a wide range of concepts expressible in natural language. DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text–image pairs.”

Microsoft invested $1B in Open AI and their new Microsoft Designer uses AI-generated images by DALL-E 2. Recently, other $10B.

GitHub Copilot, essentially autocomplete for coders is another application of this technology. GitHub Copilot uses the “OpenAI Codex to suggest code and entire functions in real-time, right from your editor.

Stable Diffusion is a “latent text-to-image diffusion model capable of generating photo-realistic images given any text input, cultivates autonomous freedom to produce incredible imagery, empowers billions of people to create stunning art within seconds.”

Stability AI just raised $101M funding round and has over 5,000 A100 graphic processors, making it one of the biggest supercomputers in the world, trained with 2B images. Stability AI is open source and is made up of a developer community “with over 200,00 members who are building AI for the future.”