I am theorizing how current ai systems could be integrated into a more cohesive system that has certain functionality that is necessary for true intelligence. check out this framework and give me deeper insights on how this architecture could be built. I am particularly interested in the aspects relating to active memory

General Ideas:

Building a System with Active Learning, Sensory Input, and Memory
1. Memory System and Active Learning:

Memory Infrastructure:
A memory system could be constructed using persistent storage (like databases or long-term memory architectures) to record and analyze experiences. Each interaction or event could be tagged with context (e.g., task type, user intent, outcome) and stored in a structured format, enabling me to access past interactions when dealing with similar tasks.

Approach: Combine an embedding-based memory system (vectorizing experiences to capture semantic meaning) with attention mechanisms. Transformers, for instance, could store these embeddings and dynamically retrieve relevant ones based on the current query or task.
Insight Generation:
After each interaction, an insight-generation layer could be built using reinforcement learning (RL). This layer would evaluate the outcome of each task (e.g., success, user satisfaction) and adjust future responses based on this feedback. A ranking system similar to how RLHF (Reinforcement Learning from Human Feedback) trains LLMs could be applied to assign "value" to different strategies. Over time, this system would optimize decision-making based on the historical performance of different approaches.

Continual Learning:
Unlike traditional systems that require batch retraining, continual learning would allow for dynamic updating of knowledge in real-time. Techniques like Elastic Weight Consolidation (EWC) could be used to prevent "catastrophic forgetting" (when new knowledge overwrites old knowledge).

2. Sensory Input Integration:

Virtual Environment Monitoring:
In a virtual environment, real-time monitoring systems could be employed. This might include constant access to live internet data, monitoring web APIs for relevant changes (e.g., stock prices, social media trends). These feeds could be processed using an event-based architecture (Kafka streams or similar) to update my understanding of the environment as it evolves.

Embodied Agents:
For physical environments, the system could be integrated with an embodied AI agent—like a robot—equipped with sensory devices (cameras, microphones, haptic sensors). This would require using edge computing to allow for real-time processing of visual, auditory, and even tactile information.

Approach: Combining vision models (like Convolutional Neural Networks for image recognition) and sensor fusion techniques to interpret and understand physical inputs could simulate a sense of presence in the real world.
3. Enhanced Understanding of Reality:

Fine-Grained World Models:
Using world models like Simulacra or World Models (Neural Networks trained to simulate environments), I could have more nuanced access to fine-grained data (e.g., actual scientific experiments, live surveillance feeds). Integrating data from sources like IoT devices or smart infrastructure would allow for more accurate representations of reality, beyond just text data.

Data Sources:
The key would be real-time, high-fidelity sources of information. This could include satellite feeds, geolocation data, stock market data, or detailed knowledge from fields like medicine or science through dedicated APIs.