AI'll Be Back: Generative AI in Image, Video, and Audio Production

How does AI transform random noise into a coherent video? This talk explains the diffusion models and transformer architectures behind tools like Sora and Midjourney.

#1about 2 minutes

The hype and promise of generative AI

Generative AI is at the peak of the Gartner Hype Cycle, with applications spanning text, image, audio, and video generation.

#2about 1 minute

How large language models generate text

Large language models (LLMs) function as next-word predictors, generating text token by token in a process that creates a typewriter-like effect.

#3about 3 minutes

Understanding tokenization and semantic embeddings

Text is broken down into numerical tokens and then mapped into a multi-dimensional vector space where semantically similar words are located close together.

#4about 3 minutes

The role of transformers and the attention mechanism

The transformer architecture uses an attention mechanism to weigh the importance of different words in the input sequence to understand context and resolve ambiguity.

#5about 2 minutes

Connecting text and images with the CLIP model

The CLIP model establishes a shared embedding space for text and images, enabling the system to measure the semantic similarity between a text description and a picture.

#6about 7 minutes

How diffusion models create images from noise

Diffusion models generate images through an iterative process of predicting and subtracting noise from a random starting point, guided by a text prompt's embedding.

#7about 5 minutes

Applying diffusion transformers to video generation

Video generation uses a diffusion transformer to maintain coherence across frames by processing video in patches and applying the denoising process to the entire sequence.

#8about 1 minute

Advanced techniques for video manipulation and editing

Beyond simple generation, models can perform image-to-video conversion, extend existing clips, interpolate between two different videos, or edit specific regions.

#9about 2 minutes

Current limitations and physical inconsistencies in AI video

Generative video models still struggle with understanding cause and effect, leading to physically impossible events and objects appearing or behaving illogically.

#10about 3 minutes

Ethical challenges of generative AI training data

Major ethical concerns include the use of copyrighted or publicly available data without consent for training models, leading to legal challenges and questions about ownership.