Ashley Edwards - Genie Paper (DeepMind/Runway)
Machine Learning Street Talk (MLST)
Intro
This chapter delves into the innovative Genie system that uses generative models to create real-time interactive experiences based on video inputs. It highlights the VQ model's ability to predict future frames and how users can actively modify outputs to explore different scenarios.
Ashley Edwards, who was working at DeepMind when she co-authored the Genie paper and is now at Runway, covered several key aspects of the Genie AI system and its applications in video generation, robotics, and game creation.
MLST is sponsored by Brave:
The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.
Genie's approach to learning interactive environments, balancing compression and fidelity.
The use of latent action models and VQE models for video processing and tokenization.
Challenges in maintaining action consistency across frames and integrating text-to-image models.
Evaluation metrics for AI-generated content, such as FID and PS&R diff metrics.
The discussion also explored broader implications and applications:
The potential impact of AI video generation on content creation jobs.
Applications of Genie in game generation and robotics.
The use of foundation models in robotics and the differences between internet video data and specialized robotics data.
Challenges in mapping AI-generated actions to real-world robotic actions.
Ashley Edwards: https://ashedwards.github.io/
TOC (*) are best bits
00:00:00 1. Intro to Genie & Brave Search API: Trade-offs & limitations *
00:02:26 2. Genie's Architecture: Latent action, VQE, video processing *
00:05:06 3. Genie's Constraints: Frame consistency & image model integration
00:07:26 4. Evaluation: FID, PS&R diff metrics & latent induction methods
00:09:44 5. AI Video Gen: Content creation impact, depth & parallax effects
00:11:39 6. Model Scaling: Training data impact & computational trade-offs
00:13:50 7. Game & Robotics Apps: Gamification & action mapping challenges *
00:16:16 8. Robotics Foundation Models: Action space & data considerations *
00:19:18 9. Mask-GPT & Video Frames: Real-time optimization, RL from videos
00:20:34 10. Research Challenges: AI value, efficiency vs. quality, safety
00:24:20 11. Future Dev: Efficiency improvements & fine-tuning strategies
Refs:
1. Genie (learning interactive environments from videos) / Ashley and DM collegues [00:01]
https://arxiv.org/abs/2402.15391
2. VQ-VAE (Vector Quantized Variational Autoencoder) / Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu [02:43]
https://arxiv.org/abs/1711.00937
3. FID (Fréchet Inception Distance) metric / Martin Heusel et al. [07:37]
https://arxiv.org/abs/1706.08500
4. PS&R (Precision and Recall) metric / Mehdi S. M. Sajjadi et al. [08:02]
https://arxiv.org/abs/1806.00035
5. Vision Transformer (ViT) architecture / Alexey Dosovitskiy et al. [12:14]
https://arxiv.org/abs/2010.11929
6. Genie (robotics foundation models) / Google DeepMind [17:34]
https://deepmind.google/research/publications/60474/
7. Chelsea Finn's lab work on robotics datasets / Chelsea Finn [17:38]
https://ai.stanford.edu/~cbfinn/
8. Imitation from observation in reinforcement learning / YuXuan Liu [20:58]
https://arxiv.org/abs/1707.03374
9. Waymo's autonomous driving technology / Waymo [22:38]
https://waymo.com/
10. Gen3 model release by Runway / Runway [23:48]
https://runwayml.com/
11. Classifier-free guidance technique / Jonathan Ho and Tim Salimans [24:43]
https://arxiv.org/abs/2207.12598
Remember Everything You Learn from Podcasts
