Jeffrey Hawke Questions World Model Coherency Research Focus
Key takeaways
- Jeffrey Hawke questions the current focus on long-term coherency in world models.
- He identifies coherent pixel and audio streams as a more fundamental unsolved problem.
- This issue is compared to the importance of tokenizers for long context models.
- The opinion suggests a need to re-prioritize foundational research in multimodal AI.
Who benefits
Summary
Jeffrey Hawke of OdysseyML suggests that current AI research on long-term coherency in world models might be misdirected. He argues the more fundamental challenge lies in achieving coherent pixel and audio streams, likening it to the foundational work on tokenizers for long context models.
Why it matters
This perspective challenges current AI research paradigms, potentially redirecting efforts towards more fundamental problems that could unlock significant advancements in multimodal AI and world model development.
How to implement this in your domain
- 1Re-evaluate current AI research roadmaps to prioritize foundational multimodal coherence.
- 2Investigate new approaches for generating synchronized and consistent pixel and audio data.
- 3Allocate resources to research into fundamental building blocks for world models, similar to tokenizer development.
- 4Foster collaboration between teams working on different modalities (vision, audio) to address integration challenges.
- 5Stay informed on discussions and findings related to foundational AI challenges.
Original post by @nathanbenaich
"Focusing on long-term coherency in world models might be the wrong research question says @jeffrey_hawke at @odysseyml. The unsolved problem is achieving coherent pixel and audio streams -a fundamental issue analogous to working on long context before settling on a tokenizer."
View on XOriginally posted by @nathanbenaich on X · view source
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