The Fractured Entangled Representation Hypothesis (Intro)
Published:Jul 5, 2025 23:55
•1 min read
•ML Street Talk Pod
Analysis
This article discusses a critical perspective on current AI, suggesting that its impressive performance is superficial. It introduces the "Fractured Entangled Representation Hypothesis," arguing that current AI's internal understanding is disorganized and lacks true structural coherence, akin to a "total spaghetti." The article contrasts this with a more intuitive and powerful approach, referencing Kenneth Stanley's "Picbreeder" experiment, which generates AI with a deeper, bottom-up understanding of the world. The core argument centers on the difference between memorization and genuine understanding, advocating for methods that prioritize internal model clarity over brute-force training.
Key Takeaways
- •Current AI's impressive performance may be superficial, lacking true internal understanding.
- •The article proposes an alternative approach that prioritizes a clear and intuitive internal model.
- •The core difference lies between memorization and genuine understanding in AI development.
Reference
“While AI today produces amazing results on the surface, its internal understanding is a complete mess, described as "total spaghetti".”