A Framework for Self-Improving, Curiosity-Driven AI

A Low-Cost Approach to Autonomous Learning, Reasoning, and Real-World Interaction

Dayyan James

dayyan.james@gmail.com

Abstract

This paper presents a framework for designing a low-cost, self-improving AI model that develops natural curiosity, enhances its reasoning abilities, and interacts with the real world to refine its understanding. Unlike conventional AI systems, which passively process inputs based on predefined training sets, this model actively seeks knowledge, experiments with problem-solving, and autonomously improves itself over long durations. It employs a multi-agent cognitive structure, a hierarchical memory storage system, and quantum-inspired hybrid storage to achieve high efficiency while maintaining adaptability. The AI is initially trained through a combination of existing LLM models and live video and audio feeds captured from smart glasses, allowing it to learn from real-world human interactions. Over time, the AI transitions from passive learning to active engagement, refining its behaviour through an experimental feedback loop.

Inspiration

This framework has been inspired by numerous works in the field of AI and human cognition, with most of the concepts being drawn from human cognition. From Roger Wolcott Sperry’s Split Brain Experiments, Jeff Hawkins novel “A Thousand Brains”, Kevin Simler and Ronin Hanson’s “The Elephant in the Brain”, Dan Ariely’s “Predictably Irrational”, and the my own perception on how his ADHD brain functions.

Introduction

Problem Statement

Traditional AI models face several limitations, including, lack of true curiosity, struggles with self-improvement, limited error-driven learning and high computational costs. That is, they do not actively seek information beyond their training data, rely on periodic retraining instead of continuous learning, aren’t strong on efficiently identifying, correcting and learning from mistakes and are expensive to scale.

This paper proposes a framework for an AI model that develops natural curiosity about its environment and human interactions while learning and improving over time by refining its decision-making capabilities. It also aims to create an AI that collaborates with its human trainer benefiting both itself and the user all while operating on relatively low-cost hardware, making continuous learning feasible.

Key Innovations

• Multi-Brain AI Architecture:
  • Cognitive Brain (decision-making, external interactions, curiosity-driven questioning, does not need to know the internal workings of the other brains).
  • Logical & Reasoning Brain (fact verification, problem-solving, structured thinking, uses symbolic AI, probabilistic reasoning and logic-based systems).
  • Creative & Experimental Brain (innovation, novel ideas, hypothesis testing, alternative solutions. Uses generative models, transformers and exploratory learning).
  • Central Decision Making (each of the above AI brains communicates via a central decision-making mechanism but operates independently)
• Hierarchical Adaptive Memory Storage:
  • Retains high-level concepts in fast-access memory.
  • Archives detailed knowledge in a compressed, low-resource format.
  • Evaluates retrieval vs. relearning costs dynamically.
• Quantum-Inspired Hybrid Storage:
  • Stores data in parallel across two disks, simulating a superposition-like state for probabilistic reasoning.
• Autonomous Learning from Real-World Interactions:
  • Uses smart glasses to capture video & audio feeds.
  • Stores relevant snippets based on contextual importance.
  • Adapts from passive learning to active engagement over time.

AI System Architecture

Multi-Agent Cognitive Architecture

The AI functions like a self-improving organism, where:

1. The Cognitive Brain asks questions and directs curiosity.
2. The Logical Brain verifies facts and evaluates past mistakes.
3. The Creative Brain explores alternative problem-solving approaches.
4. Learnings are stored efficiently in hierarchical memory.

Training Strategy

Two-Stage Training Process

Live Human Interaction Data Collection Using Smart Glasses

• AI will be trained using live feeds from smart glasses, capturing video and audio from the day-to-day real-world interactions of its human trainer.
• Key learnings are stored in fast-access memory, while less relevant data is archived.
• The AI identifies patterns in human interactions to develop real-world reasoning abilities.
• Privacy mechanisms ensure sensitive data is filtered before storage.

Memory Optimization & Self-Improvement

• AI evaluates whether to retrieve past knowledge or relearn from new data.
• Uses cost-based decision-making to balance memory efficiency.
• Error-driven learning: AI flags mistakes, adjusts its logic, and refines its understanding.

Implementation Details

Hardware & Processing Setup

• Smart Glasses: Capturing real-world interactions in video and audio.
• Local AI Server: High-performance workstation (Ryzen 9 / i9, RTX 4090, 128GB RAM, NVMe SSDs) for initial processing.
• FPGA-Based Storage Controller: Efficiently manages adaptive hierarchical memory.
• Cloud Storage (Optional): Secure backups and scalable long-term memory archiving.

AI Processing Pipeline

1. Data Collection: Smart glasses capture and transmit key moments.
2. Real-Time Categorization: AI filters and prioritizes learnings.
3. Memory Optimization: Important concepts stay in fast memory; detailed learnings are archived.
4. Self-Improvement: AI continuously tests, refines, and updates its models.

AI Experimentation & Testing

• Virtual Desktop Interface (VDI): AI tests new software, learns programming, and simulates experiments.
• Self-Directed Learning: AI compares different approaches, refining its skills autonomously.
• Benchmarking & Feedback: AI reports progress, ensuring optimal self-improvement cycles.

Expected Outcomes & Future Applications

• An AI system capable of developing true curiosity and continuously improving itself.
• A low-cost alternative to expensive deep learning models, making self-improving AI accessible.
• Scalable applications in robotics, automation, and AI-assisted research.
• A foundation for AI-human collaboration, where the AI evolves in sync with its human partner.

Implementation Strategy

Multi Brain Architecture

A. Multi-Agent AI System (MAS)

Using a multi-agent system, each brain is an independent AI model communicating through a message-passing system.

Logical Brain: A rule-based system or logic-driven model (e.g., Prolog, Bayesian networks for structured reasoning).

Creative Brain: A generative AI model that explores new ideas (diffusion models for image/video processing and reinforcement learning models trained for innovation).

Cognitive Brain: A controller AI that acts as a mediator, selecting which outputs to use.

These agents will communicate asynchronously, allowing for dynamic collaboration.

B. Communication Layer Between Brains

Using message queue system (e.g., RabbitMQ, Kafka) where:

1. The Cognitive Brain submits a query to both the Logical Brain and Creative Brain.
2. Each brain processes the query separately and submits its response.
3. The Cognitive Brain chooses the best action from their outputs.
   1. Accepting one of the outcomes
   2. Passing outcomes between logical and creative brains for logical verification or further experimentation respectively.

Implementation Example:

• Use LangChain or AutoGPT for multi-agent communication.
• Implement each brain as a separate API microservice.

C. Cognitive Brain: Decision-Making Framework

The Cognitive Brain uses:

• Weighted Scoring Model: Assigns weights to Logical vs. Creative responses.
• Reinforcement Learning (RL): Uses past outcomes to refine decision-making.
• Multi-Armed Bandit Algorithm: Optimizes choices over time based on feedback.

Example Logic

• If Logical Brain gives high-confidence structured reasoning, Cognitive Brain favours its decision.
• If Creative Brain presents a novel, high-impact solution, it may override Logical Brain.
• If both agree, the decision is executed immediately.
• If both conflict, the Cognitive Brain generates a meta-query for re-evaluation.

D. Logical & Reasoning Brain: Fact-Checking & Structured Thinking

This brain ensures that the AI is grounded in reality.

• Uses Bayesian Inference, Symbolic AI, and Chain-of-Thought Prompting.
• Can be implemented with OpenAI’s GPT-4 with step-by-step logical reasoning fine-tuned.
• Uses external fact-checking APIs (e.g., Wolfram Alpha, Google Scholar) to verify claims.

Implementation Example

• Fine-tune a GPT model to always provide a structured, step-by-step breakdown of its reasoning.
• Implement a truth-verification layer using symbolic logic.

E. Creative & Experimental Brain: Out-of-the-Box Thinking

• Uses generative models (e.g., GPT, Stable Diffusion for images, evolutionary algorithms for problem-solving).
• Can leverage GANs (Generative Adversarial Networks) or Transformer models to simulate creativity.
• Uses self-play reinforcement learning to create novel solutions.
• If a task is ambiguous, this brain generates multiple potential answers.

Implementation Example

• Fine-tune LLM to generate multiple hypotheses per query.
• Use Reinforcement Learning with Curiosity-Driven Exploration to experiment with new answers.

Final Integration: How the Three Brains Work Together

1. User Input → Cognitive Brain

   • Example: "What’s the best way to create an alcohol-free perfume with a unique scent profile?"

2. Cognitive Brain sends the request to both:

   • Logical Brain → "Analyses industry standards, chemical stability, and safety considerations."
   • Creative Brain → "Proposes wild experimental scent combinations and new formulation ideas."

3. Logical Brain outputs: "MCT oil is a stable base, but diffusion longevity is reduced. Consider using jojoba oil."

4. Creative Brain outputs: "Try pairing ozone notes with blue cypress and white musk for an ethereal scent."

5. Cognitive Brain: Evaluates both responses, assigns a weighted score, and decides to experiment with both approaches.

6. Final Output: A hybrid recommendation that blends structured formulation with creative scent combinations.

Tools & Tech Stack for Building This System

AI Models & Frameworks

• Cognitive Brain: OpenAI’s GPT-4 (for language and reasoning) + RLHF (for optimizing choices).
• Logical & Reasoning Brain: Fine-tuned GPT model, Prolog (for symbolic logic), or Bayesian Networks.
• Creative Brain: GPT-4 for idea generation, Stable Diffusion for creative assets, RL-curiosity models.

Communication & Processing

• LangChain / AutoGPT / Agent-Based Frameworks for handling multi-agent interactions.
• RabbitMQ / Kafka / Redis for message passing between the three brains.
• Vector Databases (e.g., Pinecone, FAISS) for memory and knowledge storage.

Decision Optimization

• Multi-Armed Bandit Algorithms for dynamic decision-making.
• RLHF (Reinforcement Learning with Human Feedback) to refine decision-making over time.

Quantum Inspired Storage

The goal is to allow AI to store, manipulate and reason with superposition-like states on classical hardware with the ultimate goal that the Logic and Reasoning brain can use it for storing fuzzy logic, Q-bit data for uncertainty models; the Creative brain can experiment with Q-bit states to determine how different configurations impact decision making and the Cognitive brain can read the results from both logical and creative brains (making the final decision in Binary from the Q-bit uncertainty). This may be achieved through a dual disk system controlled via a custom FPGA, with the disks simultaneously reading and writing to the same address, encoding:

1. Classic Binary States (0 and 1)
   1. 1 is written as 1 on both disks
   2. 0 is written as 0 on both disks
2. Quantum Superposition Equivalent (q-bit state)
   1. Q-bit state written as 0 on Disk A and 1 on Disk B

The system interprets a q-bit state though a third logic state (Q) where:

1. Q = 0 is classical binary mode
2. Q= 1 is superpostion-like mode

Read operations must check the Q-bit first to determine the correct interpretation. This is to avoid situation where there is an error or a redundancy check.

Hardware

FPGA
FPGA (Field-Programmable Gate Array) will act as an interface between the AI system and this Quantum inspired storage. The FPGA will handle simultaneous reads/writes to both disks, interpret Q states correctly and communicate with AI over PCIe (or ethernet for scalability later)

Possible hardware options are Xilinx Zynq UltraScale+, Intel Stratix 10, Lattice ECP5.

Disks

High-speed RAMDisks with custom RAM-based buffer storage for short-term dynamic Q-bit states

Integration Layer

PCIe (or ethernet for scalability) for AI to connect to the FPGA and a Neural Processing Unit to offload AI’s encoding and retrieval of Q-bit states.

Logic Design

1. Encode data onto two disks
   1. 1 written as 1 on both disks
   2. 0 written as 0 on both disks
   3. Q written as 1 on Disk A and 0 on Disk B
2. Reads data from both disks
   1. If both values match, return classical binary
   2. If mismatched interpret as Q-bit

Logic Diagram

              AI <-> FPGA <-> {Disk A, Disk B}

FPGA acts as a data coordinator where AI can query the superposition-like storage, and process the output using probabilistic algorithms.

Adaptive Hierarchical Memory Storage

Multi-Level Memory Architecture

Intelligent Memory Compression

Archived learnings are compressed with key metadata tags for faster retrieval, vector embedding indexing used to enable associative search for past experiences and incremental updates are made to the archive ensuring relevance.

Decision Model

Decision Factors

1. Task Relevance: If memory closely matches current needs, retrieve detailed data. If marginally relevant, weigh the cost of retrieval vs. relearning.

   1. Retrieval vs. Relearning Cost: estimate computational expense of fetching archived data and time priority, if the relearning costs less or offers improvement learn anew.

   2. Knowledge Expiry: validate whether achieved knowledge is outdated and update relearning if so.

Example