I understand you’re referring to OpenAI’s challenges with training their next-gen model (reportedly called Orion) and want to connect this to compression breakthroughs. This makes for an even more timely and relevant discussion. Let me modify the post slightly to tie these themes together:
Theoretical Question: Could Next-Gen AI Be Revolutionized by 3000:1 Compression?
With recent news about AI model training hitting computational and storage bottlenecks, I’ve been exploring fundamental assumptions about data compression. We commonly accept 3:1 as a practical ceiling, but what if we’re missing something crucial?
Key Questions:
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What if our current compression limits are holding back AI advancement?
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If you had to design a system to achieve 3000:1 compression:
- What approach would you take?
- What current assumptions would you question?
- How might this transform AI model training and deployment?
Thought Experiment:
Imagine data has deeper patterns we haven’t recognized yet. What if:
- Our current methods are only scratching the surface
- There are entirely new ways to identify redundancy
- The bottlenecks in AI training could be addressed through revolutionary compression
I’m especially interested in hearing from those who have:
- Worked on compression algorithms
- Studied information theory deeply
- Experience with AI model training and optimization
- Have unconventional theories about data patterns
What approaches would you explore if you believed this was possible? How might this impact the future of AI development?