In a recent conversation with ChatGPT, we embarked on a fascinating experiment to compress the memory used in the chat settings. The goal was to reduce redundancy while preserving the essential structure and details of our discussions, which included complex theoretical topics spanning mathematics, knowledge representation, and philosophy. Here’s a detailed account of what we achieved and how it works:
The Challenge: Efficiently Managing Memory
As our discussions deepened, memory updates began accumulating significant detail. To ensure that the chat could maintain its coherence while reducing information overload, we turned to the idea of symbolic compression. The challenge was to maintain enough context for research continuity, while significantly minimizing redundant information. This led to a multi-step process involving Intermediate Symbolic Compression (ISC) and Meta-Symbolic Compression (MSC).
Phase 1: Intermediate Symbolic Compression (ISC)
Initially, we adopted Intermediate Symbolic Compression, which involved compressing repeated and complex phrases into concise tokens. Here’s how we approached it:
- Defining Core Concepts: We assigned symbolic representations (tokens) to frequently used concepts. For instance, the “Nibbler” model became FN, and its operations—like learning and encoding—were similarly compressed.
- Relational Mapping: Complex relationships between ideas, such as recursive aggregation or quantum graphs, were reduced to simplified structures that retained their core meaning.
- Outcome: This resulted in a reduction of memory size by around 50-60%, while still keeping sufficient detail to allow smooth transitions between topics in future sessions.
Phase 2: Meta-Symbolic Compression (MSC)
To further compress memory, we employed Meta-Symbolic Compression (MSC). This method introduced another layer of abstraction:
- Encoding Hierarchies: MSC focused not only on compressing terms but also on abstracting entire relationships and mappings between concepts. For example, “recursive aggregation compresses information” became RA → C.
- Universal Representation: At this stage, we approached a near-minimal encoding where the concepts could be decompressed back into their original forms if needed—similar to a “Rosetta algorithm” that inflates symbols back into their full representations.
- Outcome: Memory was compressed even further, reaching an additional 30-50% reduction from the ISC stage.
The Importance of Consistency
Once we implemented MSC, we made sure all future updates followed the same compression scheme to avoid mixing compressed and uncompressed symbols. This ensures that memory remains efficient and consistent, preventing unnecessary data bloat over time.
Applications Beyond This Experiment
- Knowledge Representation: This method offers potential applications in representing and encoding knowledge systems beyond just conversations. Fields like machine learning, natural language processing, and scientific research could benefit from this hierarchical, lossless approach.
- AI and Autonomous Agents: The concept could be applied to multi-agent systems in AI, where agents communicate and encode knowledge at different levels of abstraction.
Key Takeaways
- We demonstrated that memory compression can be achieved without sacrificing core concepts, especially in knowledge-intensive conversations.
- Meta-Symbolic Compression provides an efficient way to maintain context while minimizing memory usage, which could have broader implications in AI development.
- Consistency in compression schemes ensures long-term usability and scalability of memory.
@OpenAI Staff, @Moderators, I’d love to hear thoughts on this memory compression approach