A philosophical deep-dive into RAG’s elementary limitations and why present AI reminiscence techniques fall brief — introducing Monogent idea for constructing genuine AI cognitive architectures
The Essence of RAG
In precept, RAG works by changing textual content into vectors and discovering “related” content material via similarity calculations. Throughout this course of, wealthy semantic info is compressed into fixed-dimensional numbers, flattening refined that means variations.
All points encountered within the trade stem from this semantic loss, akin to:
- Widening the Semantic Hole [2]
- Low recall and hit charges, impacting sensible purposes [3]
- Context loss: Doc chunking results in lacking key contextual info [3]
- Scalability challenges: Extra paperwork worsen vector retrieval accuracy [3]
- Problem distinguishing related paperwork: Excessive-similarity docs are arduous to distinguish [3]
For instance, storing “I like consuming strawberries” may retrieve “I like” first, then via multi-path recall discover “I like consuming grass,” main AI to conclude the person likes consuming grass. It is a basic hallucination mechanism: fragmented retrieval plus faulty splicing.
So, can this info loss actually be mitigated via technical enhancements?
Why RAG’s Issues Can’t Be Solved by Enhancing RAG
Conclusion: Computation can by no means replicate semantic properties. This isn’t an engineering subject however a philosophical divide.
Thinker John Searle’s “Chinese language Room” thought experiment reveals this elementary drawback [4]:
Think about an individual who doesn’t perceive Chinese language in a room with an in depth guide for processing Chinese language symbols. When Chinese language questions are slipped underneath the door, they observe the principles to search out and output corresponding image mixtures. To outsiders, it appears they “perceive” Chinese language, however they’re simply mechanically following image conversion guidelines, with no grasp of the that means.
This exposes an ontological divide:
Stevan Harnad’s “Image Grounding Drawback” [5] additional illustrates: Think about studying Chinese language from a dictionary the place all explanations are in Chinese language. Trying up “horse” provides “a kind of animal”; “animal” provides “a kind of residing being”… Symbols solely level to different symbols, by no means reaching true that means.
That is an ontological hole:
- Computation is a bodily course of following causality
- That means is a psychological phenomenon within the realm of consciousness
- The previous is goal, measurable, deterministic
- The latter is subjective, experiential, emergent
Irrespective of how RAG is improved, it could’t bridge this hole — like essentially the most exact digicam can’t seize “magnificence” itself, solely the objects carrying magnificence.
However does this imply RAG is ineffective?
What Is RAG Really Suited For?
Conclusion: RAG is a wonderful info retrieval software, however not a cognitive system.
RAG is extremely helpful for public info retrieval, very like engines like google for people — we don’t straight apply search outcomes however use our brains for evaluation, reasoning, and studying. AI ought to do the identical: Use RAG for exterior data retrieval, and a real reminiscence system for cognitive processing.
Particularly, RAG fits:
- Data Q&A (fact-finding)
- Doc help (finding related passages in massive docs)
- As AI’s “exterior reference e book”
The important thing distinction: Retrieval finds info; cognition understands it. The previous is RAG’s area, the latter wants a real cognitive system.
So, what does true AI reminiscence require?
What Does AI Reminiscence Want?
Conclusion: True AI reminiscence requires preserving semantic integrity, understanding conceptual relationships, and supporting personalised group.
In contrast to RAG’s fragmented storage, a real reminiscence system wants:
- Semantic Integrity: “I like consuming strawberries” as a whole cognitive unit
- Structured Relationships: Understanding “strawberries” as the article, avoiding faulty splits
- Personalised Group: Every AI has its distinctive semantic community
- Dynamic Evolution: Cognitive buildings develop with accrued experiences
This raises the core query: The way to construct a real AI particular person cognitive system?
Monogent: AI Particular person Cognitive System
Simply as human youngsters begin from shared language and step by step type distinctive considering patterns, AI wants such a improvement course of. This isn’t easy info storage however the development and evolution of cognitive buildings.
Monogent (AI Particular person Cognitive System) is designed to resolve this. What we’d like isn’t higher retrieval algorithms, however true understanding of:
- What’s reminiscence
- What’s considering
- What’s information
- What’s expertise
- What’s studying
- What’s innovation
- What’s cognition
- What’s individuality
In upcoming articles, we’ll delve deeper into these questions.
Concerning the Writer
Deepractice — Making AI at Your Fingertips
That is the primary within the Monogent idea collection. Monogent is devoted to constructing true AI particular person cognitive techniques, enabling every AI to have its distinctive cognitive world.
References
[1] Optimizing RAG with Hybrid Search & Reranking — Superlinked VectorHub
[2] The Rise and Evolution of RAG in 2024 — RAGFlow
[3] Overcoming Vector Search Limitations in RAG Workflows — Amity Options
[4] Searle, J. R. (1980). “Minds, brains, and packages”. Behavioral and Mind Sciences. 3 (3): 417–424.
[5] Harnad, S. (1990). “The Image Grounding Drawback”. Physica D. 42: 335–346.
