Million Minds - A New AI Architecture Based on Neuroscience

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 TBT and neuroscience legit although we did keep some things simple at…
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…

Last Updated: Nov 15, 2025

✅ 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

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

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