Unlocking the Future of Language Models with the Ainex Law
As the landscape of artificial intelligence evolves, Large Language Models (LLMs) are becoming omnipresent. However, this increase brings significant challenges, particularly the risk of these models training on synthetic, self-generated data.
Key Findings:
- Ainex Law: A mathematical principle outlining the limits of semantic integrity in recursive self-learning systems.
- Experiments Using GPT-2: We show that without human-driven data, semantic diversity drops by 66% within 20 generations.
- Model Collapse: This phenomenon is not just a decline in quality—it’s a geometric inevitability, reminiscent of thermodynamic entropy.
- Ainex Score (A): A revolutionary metric designed to quantify the decay in semantic integrity.
This groundbreaking research sheds light on the urgency of maintaining human-grounded data in AI development. Discover how these insights pave the way for sustainable AI innovation!
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