Emad Mostaque (Stability AI): Democratizing AI, Stable Diffusion & Generative Models

The speaker discusses the democratization of AI and the importance of diversity in data sets to ensure aligned artificial intelligence. They argue for the need to build smaller, more democratized models that impact a broader set of people and allow for adaptation to social issues. The speaker also emphasizes the importance of transparency in the development of AI models and the need for human feedback in reinforcement learning.

• AI democratization and diversity in data sets are crucial for aligned artificial intelligence
• Smaller, more democratized models are needed to impact a broader set of people and adapt to social issues
• Transparency in AI model development is necessary
• Human feedback is crucial in reinforcement learning

The speaker shares a vision of every country having their own data sets and models, with every race being represented and diverse. They argue that this is a necessary but not sufficient condition for aligned artificial intelligence. The speaker also shares a project where tablets are being used to teach literacy and numeracy to refugee children in 13 months on one hour a day, with plans to scale it to tens of millions of kids. They emphasize the potential for AI in education and reinforcement learning with human feedback.

Stable Diffusion disrupted the deep learning scene when it was released in August, advancing the field of text-to-image models because of its ability to generate photo-realistic images given any text input. And unlike other models, Stable Diffusion makes its source code available, further advancing the democratization of AI. Stable Diffusion was created by Stability AI, in collaboration with EleutherAI and LAION. Stability AI is on a mission to design and implement solutions using collective intelligence and augmented technology and has developer communities with over 20,000 members who are building AI for the future. In this fireside chat, Emad Mostaque, CEO of Stability AI, will discuss emerging trends for open source AI infrastructure, the importance of data for real-world applications of AI, and predictions on the development of “text-to-everything” in artificial intelligence.