• Reference Frames
• Road to AGI
• References

A Thousand Brains - Jeff Hawkins

Reference Frames

Identifies so called reference frames as key concept unifying all neural network processing for robust / invariant sequence prediction (allowing sequence prediction in changing environments by using relative positions).

Key points:

• Grid cells: Each cortical column uses grid cell-like mechanisms (from the entorhinal cortex) to create reference frames anchored to objects, not just spatial locations.
• Object-centered coordinates: When you touch a coffee cup, each column builds a model in the cup’s own reference frame - tracking features relative to the object itself, not your body or the room.
• Voting mechanism: Thousands of columns process different inputs (different fingers touching, different viewpoints) in parallel. They "vote" to reach consensus on what object is present and its pose.
• Location + feature: Each column stores what features exist at specific locations within an object’s reference frame. Moving your sensor updates the location signal.
• Compositional models: Reference frames allow hierarchical object models - a wheel has its own frame nested within a car’s frame.

Road to AGI

Hawkins is sure that deep learning (including LLM) will not lead to AGI. Instead he identified the following properties for AGI.

• continuous learning
• learning via movement
• compositional world model of objects
• knowledge stored in reference frames

References

• https://samiranama.com/From-Cortical-Columns-to-Transformers-A-Thousand-Brains-in-Modern-AI/

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