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The Future of Image Models Is Multimodal

Jun 7, 2024

Ideogram CEO Mohammad Norouzi discusses the evolution of transformer models, diffusion models, and the impact on AI technology. He shares insights on transitioning from research to startup CEO, user-centric product development, and fostering creativity with AI in image models.

37:17

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Mohammad Norouzi

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Quick takeaways

Transformer models paved the way for multimodal AI applications beyond language translation.

Diffusion models offer a holistic approach to generating high-dimensional objects, fostering creativity and design advancements.

Deep dives

Mohammad Nauruzi's AI Journey from Childhood to Founding Ideogram

Mohammad Nauruzi, co-founder and CEO of Ideogram, shares his fascinating journey from childhood drawing in Iran to competitive programming in college, culminating in his work at Google on text-to-image models. His experience in AI research and building neural networks led to the development of Google's image and video generation projects. The transition from academia to practical applications drove him to explore cognitive science, machine learning, and computer vision, ultimately shaping his focus on innovative technologies for image creation.

Two Leading Ways to Generate High-Dimensional Sequences of Bits

01:32

Balancing Accuracy and Creativity in Text-to-Image Generation

01:53

Intro

5min

Exploring Technology Shifts and Diffusion Models in AI

15min

Lessons Learned from Google and Contrasts in Product Development

3min

Enhancing Creativity with Ideogram and AI

8min

Evolution of Image Models and User Experience in AI

6min

In this episode, Ideogram CEO Mohammad Norouzi joins a16z General Partner Jennifer Li, as well as Derrick Harris, to share his story of growing up in Iran, helping build influential text-to-image models at Google, and ultimately cofounding and running Ideogram. He also breaks down the differences between transformer models and diffusion models, as well as the transition from researcher to startup CEO.

Here's an excerpt where Mohammad discusses the reaction to the original transformer architecture paper, "Attention Is All You Need," within Google's AI team:

"I think [lead author Asish Vaswani] knew right after the paper was submitted that this is a very important piece of the technology. And he was telling me in the hallway how it works and how much improvement it gives to translation. Translation was a testbed for the transformer paper at the time, and it helped in two ways. One is the speed of training and the other is the quality of translation.

"To be fair, I don't think anybody had a very crystal clear idea of how big this would become. And I guess the interesting thing is, now, it's the founding architecture for computer vision, too, not only for language. And then we also went far beyond language translation as a task, and we are talking about general-purpose assistants and the idea of building general-purpose intelligent machines. And it's really humbling to see how big of a role the transformer is playing into this."

Learn more:
Investing in Ideogram

Imagen

Denoising Diffusion Probabilistic Models

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Mohammad Norouzi

Jennifer Li

Derrick Harris

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