📌 What’s next for AI video?

AI case studies: #2024-06 | How AI is transforming the world?

Feb 09, 2024

In 2023, AI video saw a significant breakthrough, with dozens of video generation products in use worldwide:

Despite being limited in quality and character consistency, the progress suggests a massive transformation in video generation.
Video models today are diffusion-based, generating frames and creating temporally consistent animations between them.
However, they lack an intrinsic understanding of 3D space, causing warping and morphing.

Why is AI video important?

Some believe that video models don't need an understanding of 3D space, but if trained on quality data, they can learn relationships between objects and represent scenes from different angles.

Quality training data for video models comes from studios or production companies with long-form videos shot from multiple angles.

The use cases for video models will likely segment between platforms and models, with different models excelling at different styles, types of movement, and scene composition.

Products built around these models will likely diverge in workflow and serve different end markets.

What's next for AI video?

Improvements in text-to-video models and offshoots like image-to-video and video-to-video are gaining steam.

This explosion in innovation highlights the importance of monitoring companies and addressing underlying questions in this space that would help extend the possibility of visual effects and address significant challenges (healthcare).

Companies are focused on tackling core issues such as control, temporal coherence, and length. While some products have added features to allow users more control pre-generation, the ultimate goal is to create professional-grade clips that match user prompts.

Most big tech companies have chosen to keep their video generation products private; instead, they publish papers and drop demo videos without announcing when their models will ever be public.

These companies have massive distribution advantages and billions of users on their products, so dropping their video models could allow them to capture meaningful market share in this emerging category.

However, most companies still haven't needed to release text-to-image products, and legal, safety, and copyright concerns often make it challenging to convert research into products.

AI video is still in the early stages of development, with much to be improved before it is ready for primetime.

Although video generation platforms like Genmo, Runway, and Pika Labs focus on video generation, they also incorporate editing features.

Some platforms, like Pika, now enable upscale video on their site, while others are advocating for AI-native editing platforms that make it easy to generate content across modalities from different models in one place.

Please share your thoughts; what do you think? Does that make sense?

Explore more

Google’s latest AI video generator can render cute animals in implausible situations on Arstechnica
Fake Biden video prompts call for Meta to label posts on BBC

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🚀 Unbundling AI

Workbook: Building a sustainable data-driven organization, part 1

Yael Rozencwajg

Jan 1

Workbook: Building a sustainable data-driven organization, part 1

Hello, This workbook is based on conversations with AI startup founders and engineers. We present it as an evolving planner to help founders and leaders integrate the central notions of building a sustainable data-driven organization. There are many ways to build business solutions and tools with AI, including open-source models, LLMs, training models, fine-tuning, or using hosted APIs, all from scratch. But we also see a surge of interest in data-driven business acquisitions among large companies. It entails buying an existing solution, not starting it from zero, with the potential for more autonomy and ownership.

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