Million Minds

Creating a novel AI framework from the ground up based on neuroscience principles, exploring biologically-inspired intelligence systems

Concept

Million Minds is an ambitious project aimed at developing a novel AI framework inspired by the fundamental principles of neuroscience. Unlike traditional AI systems that rely heavily on deep learning and large datasets, Million Minds seeks to emulate the brain's architecture and functionality to create more efficient, adaptable, and intelligent systems.

First Principles

  • Brain is a sensorimotor, space processor
  • Brain operates in analog; no bits, no clocks, no messages
  • Brain has a repeating cortical structure
  • Each structure is running the same algorithm
  • Neurons don’t understand what they are doing, they leverage:
    • Sparse encoding
    • Homeostasis
    • Co-firing and association (Hebbian learning)
    • Predictive state
    • Oscillation
    • Layering

Why It Matters

By grounding AI development in the principles that govern biological intelligence, Million Minds will overcome the limitations of current AI technologies, such as their lack of generalization, adaptability, and energy efficiency. This approach has the potential to achieve breakthroughs leading the way to AGI and beyond.

Latest Notes

  • Nov 16, 2025
    When this system is up and running, because it can learn continuously (unlike current AI), it will be just like Johnny 5 from Short Circuit movie.…
  • Nov 15, 2025
    Finally we have a sensor, a thalamus, a column and a voting module. It is all neuroscience legit although we did keep some things simple at the…
  • Nov 14, 2025
    I see the current AI as the next evolution of search.. It brings search into the context where i'm working.. therefore it can build more context…

Roadmap

Last Updated: Nov 15, 2025

Implement Basics

  • ✅ Sensor (Text Retina)
    Convert character-based text into multi-scale feature SDRs using biologically inspired “retina” patches
  • ✅ Thalamus
    Gate sensor SDRs and mark landmarks in the input text
  • ✅ Pose System
    Add sensorimotor grounding via 1-D grid-cell–like modules, internal to the cortical column
  • ✅ TransitionPool
    Build an associative memory with sparse projections
  • ✅ Cortical Column
    Implement a model that learns temporal transitions and pools stable features
  • ✅ Lateral Bus
    Calculate consensus between neighboring columns
  • Demo
    Run the system end-to-end and demonstrate learning and recall

More Advanced Features

  • Real synapse/segment-like TM
    Represent sub-patterns (dendritic segments) that detect specific combinations of bits on context
  • Homeostasis + sparsity control
    Adaptations per column based on usage so it doesn’t saturate or go silent.
  • Voting
    Use consensus to influence object SDR calculation in the column
  • Pose alignment between columns
    When object consensus is high, adjust pose slightly so pose overlap between columns increases

System-level Behavior

  • Inference mode
    Interact with the system, i.e. ask questions, get answers
  • Hierarchy / higher regions
    Higher-level columns to chunk smaller features into larger concepts
  • Replay / consolidation (offline pass)
    Keep the system from becoming a junkyard while still letting it learn long-term structure
  • A real motor system
    Necessary for mental simulation/planning and language generation

Other Modalities

  • Images
  • Videos
  • 3D Virtual Environment
  • Hardware sensors