Architecting for Evolving AI Workloads + Genie | Two 30 Min Talks

This will be a journal club event
Two Talks:
1. Architecting for Evolving AI Workloads
2. Genie, link to paper

Speakers

1. Ben Sander, Corporate Fellow at AMD
2. Rakshak Talwar, Head of AI Research Engineering at Sima Labs

Abstract
1. N/A

We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless variety of action-controllable virtual worlds described through text, synthetic images, photographs, and even sketches. At 11B parameters, Genie can be considered a foundation world model. It is comprised of a spatiotemporal video tokenizer, an autoregressive dynamics model, and a simple and scalable latent action model. Genie enables users to act in the generated environments on a frame-by-frame basis despite training without any ground-truth action labels or other domain-specific requirements typically found in the world model literature. Further the resulting learned latent action space facilitates training agents to imitate behaviors from unseen videos, opening the path for training generalist agents of the future.

Info
Austin Deep Learning Journal Club is group for committed machine learning practitioners and researchers alike. The group typically meets every first Tuesday of each month to discuss research publications. The publications are usually the ones that laid foundation to ML/DL or explore novel promising ideas and are selected by a vote. Participants are expected to read the publications to be able to contribute to discussion and learn from others. This is also a great opportunity to showcase your implementations to get feedback from other experts.

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