Towards LifeSpan Cognitive Systems (2409.13265v2)

Abstract: Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central to this is enabling continuous, high-frequency interactions, where the interactions are termed experiences. We refer to this envisioned system as the LifeSpan Cognitive System (LSCS). A critical feature of LSCS is its ability to engage in incremental and rapid updates while retaining and accurately recalling past experiences. In this paper we focus on the domain of LLMs, where we identify two major challenges: (1) Abstraction and Experience Merging, and (2) Long-term Retention with Accurate Recall. These properties are essential for storing new experiences, organizing past experiences, and responding to the environment in ways that leverage relevant historical data. Unlike LLMs with continual learning, which typically rely on large corpora for fine-tuning and focus on improving performance within specific domains or tasks, LSCS must rapidly and incrementally update with new information from its environment at a high frequency. Existing technologies with the potential of solving the above two major challenges can be classified into four classes based on a conceptual metric called Storage Complexity, which measures the relative space required to store past experiences. Each of these four classes of technologies has its own strengths and limitations while we argue none of them alone can achieve LSCS alone. To this end, we propose a potential instantiation for LSCS that can integrate all four classes of technologies. The new instantiation, serving as a conjecture, operates through two core processes: Absorbing Experiences and Generating Responses.

• The paper introduces a LifeSpan Cognitive System that integrates continuous abstraction and experience merging to adapt in complex environments.
• It combines techniques from model parameters, explicit memory, and structured knowledge bases to address catastrophic forgetting.
• The framework emphasizes long-term retention and accurate recall, paving the way for resilient AI in real-world applications.

Towards LifeSpan Cognitive Systems

The paper "Towards LifeSpan Cognitive Systems" proposes the development of a LifeSpan Cognitive System (LSCS), a novel class of artificial intelligence designed to emulate human-like continuous interaction and learning within complex environments. This system aims to handle incremental learning from varied environments—whether they are virtual worlds like Minecraft or the intricate dynamics of human society. The paper identifies two primary challenges that must be addressed: (1) Abstraction and Experience Merging, and (2) Long-term Retention with Accurate Recalling.

Core Challenges

Abstraction and Experience Merging: LSCS needs to abstract significant information from continuous interactions and efficiently merge these abstractions with existing knowledge. Unlike traditional models, which often process redundant data, LSCS should filter out non-essential information and integrate new, abstracted experiences into its memory, resolving potential conflicts and facilitating continual evolution.

Long-term Retention and Accurate Recalling: For LSCS to function effectively, it must recall relevant historical information in response to current environmental queries. This capability requires robust memory architecture that allows recall of pertinent data from past experiences without significant latency or degradation over time.

Technological Approaches

The paper evaluates existing technologies based on a conceptual framework termed "Storage Complexity," which measures the space required to store past experiences. It categorizes these technologies into four distinct classes:

1. Saving into Model Parameters: This involves encoding experiences directly in the model parameters through techniques like model editing and continual learning. While efficient in storage space (zero storage complexity), these methods are prone to catastrophic forgetting and struggle with sequential updates.
2. Saving into Explicit Memory: These methods utilize a memory module with variable sizes to store experiences. Fixed-size memory solutions provide high compression but may lack flexibility, while flexible-sized memory modules offer dynamic storage at the cost of added storage complexity.
3. Saving into Knowledge Bases: This approach involves storing experiences in structured formats such as organized text or knowledge graphs, enabling efficient retrieval through retrieval-augmented generation (RAG). This method is marked by its ability to maintain large volumes of structured knowledge, albeit with a moderate storage requirement.
4. Saving as Raw Text in Context: Involves storing all experiences as raw text accessible in context, thus having a linear storage complexity. This approach faces challenges of processing efficiency over extremely long contexts due to computational and memory constraints.

Proposed Framework

The authors propose a hybrid framework for LSCS, incorporating elements from all four technological classes to achieve the necessary capabilities for abstraction, retention, and recall. This framework includes:

• A layered memory architecture that captures experiences at varying levels of abstraction, from raw details to deeply encoded knowledge in model parameters.
• A retrieval process that combines semantic and non-semantic data extraction from knowledge bases and explicit memory for informed response generation.
• Utilization of advanced LLMs capable of handling long contexts efficiently, ensuring comprehensive engagement with stored experiences.

The system must efficiently absorb new experiences while maintaining relevant knowledge without incurring catastrophic forgetting. This integration promises an AI system capable of lifelong learning akin to human cognitive processes, paving the way for more adaptive and resilient AI applications.

Implications and Future Directions

The proposed LSCS framework has significant implications for AI applications in dynamic, real-world environments where adaptability and long-term learning are vital. The ability to amalgamate experiences and recall relevant knowledge accurately could enhance AI-driven decision-making systems in sectors like autonomous vehicles, virtual assistants, and smart environments.

Future research could focus on refining the balance between abstraction and detail retention, enhancing techniques for experience merging, and reducing time-complexity in retrieval operations. Further exploration of hybrid models that integrate symbolic reasoning with statistical learning may lead to breakthroughs in the comprehension of complex narratives and decision-making tasks. This advancement in AI cognitive architecture may also inform the development of general AI, contributing to systems that possess a more holistic understanding and interaction capability akin to human intelligence.