AI + a16z

ARCHIVE: Open Models (with Arthur Mensch) and Video Models (with Stefano Ermon)

May 24, 2024

Guests Arthur Mensch and Stefano Ermon discuss open foundation models and video models, emphasizing the importance of neutrality in technology and the regulation of AI applications. They explore the evolution of language models, the advantages of open-source collaboration, and the future prospects of specialized AI models. Additionally, they delve into the challenges and advancements in generating longer videos, showcasing how physics aids in accurate predictions and data compression in 3D models.

01:05:43

Creator website

Episode guests

Stefano Ermon

Arthur Mensch

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Highlights

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Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Open foundation models are crucial in advancing AI research through collaboration.

Model size, not just data, is integral in AI training development and efficiency.

Utilizing transformer architectures for video processing enhances efficiency and content generation.

Deep dives

Evolution of Open Models in AI Research

Companies initially valued open communication and collaboration in advancing AI research, with academic and industry labs sharing results. However, with the emergence of GPT-3 in 2020, companies turned more secretive due to market potential. Researchers regret this shift, advocating for community collaboration to further AI advancements.

Efficient Model with Spass Mixture of Experts

01:21

Maximizing Efficiency in Model Development and Usage

01:06

Maximizing Model Capabilities and Community Involvement

02:01

Innovations in Architecture and Latent Representations

01:48

Introduction

3min

Exploring the Relationship Between Model Size and Data in Language Models

2min

Formation of Mistroll by Former DeepMind and Meta Members

2min

Exploring Lama Research Project and Mistral 7B Model

6min

Embracing Open Source in AI for Community-Driven Innovation

16min

Future Prospects of Language Modeling

22min

Exploring the Capabilities of a Model for Generating Longer Videos

13min

Exploring Breakthroughs and Challenges in Technology Advancements

3min

For this holiday weekend (in the United States) episode, we've stitched together two archived episodes from the a16z Podcast, both featuring General Partner Anjney Midha. In the first half, from December, he speaks with Mistral cofounder and CEO Arthur Mensch about the importance of open foundation models, as well as Mistral's approach to building them. In the second half (at 34:40), from February, he speaks with Stanford's Stefano Ermon about the state of the art in video models, including how OpenAI's Sora might work under the hood.

Here's a sample of what Arthur had to say about the debate over how to regulate AI models:

"I think the battle is for the neutrality of the technology. Like a technology, by a sense, is something neutral. You can use it for bad purposes. You can use it for good purposes. If you look at what an LLM does, it's not really different from a programming language. . . .

"So we should regulate the function, the mathematics behind it. But, really, you never use a large language model itself. You always use it in an application, in a way, with a user interface. And so, that's the one thing you want to regulate. And what it means is that companies like us, like foundational model companies, will obviously make the model as controllable as possible so that the applications on top of it can be compliant, can be safe. We'll also build the tools that allow you to measure the compliance and the safety of the application, because that's super useful for the application makers. It's actually needed.

"But there's no point in regulating something that is neutral in itself, that is just a mathematical tool. I think that's the one thing that we've been hammering a lot, which is good, but there's still a lot of effort in making this strong distinction, which is super important to understand what's going on."

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Anjney Midha

Arthur Mensch

Stefano Ermon

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