Status

Accepted

Context

Ben Goertzel presented a two-part talk. Part one: origin of life simulations. Part two: algorithmic chemistry as an AGI paradigm.

D1: Anthropic-Principle Simulation Approach

Standard origin-of-life simulations start from a disordered chemical brew and let it run. This fails because autocatalysis is statistically improbable without massive scale.

The new approach: take initial condition (disordered brew, no autocatalysis) and terminal condition (autocatalysis emerged). Find the shortest path between them. Simulations show this path is not hard to find.

The issue is not self-organization difficulty. The issue is scale. You need the scale of a whole primordial ocean.

D2: Morphic Resonance + Anti-Resonance

Morphic resonance: patterns that have occurred are more likely to occur again. Plain vanilla chemistry explains this. No exotic physics needed.

Added morphic anti-resonance: once a pattern occurs too much it gets inhibited. Pattern occurring a bit gets stronger. Pattern occurring too much gets weaker.

Properly tuned morphic resonance + anti-resonance makes the distribution of near-autocatalytic sets much healthier. New autocatalytic sets pop up easier and become more prevalent.

D3: Algorithmic Chemistry as AGI Paradigm

Algorithmic chemistry: programs rewrite other programs. Catalytic programs make rewrites easier. Inter-rewriting program soup.

Fontana (1980s): Lisp codelets rewriting each other in simulated primordial soup. Kristinn Thórisson: RepliCode rewrite rules, ERA cognitive architecture controlling humanoid robots. Never rolled out at GPT-2 scale or above. Until now.

D4: Hyperon as Algorithmic Chemistry Platform

Hyperon infrastructure now supports:

• Atom spaces as algorithmic chemistry soups
• Distributed Atom space across multiple machines
• SingularityNET/ASI chain coordination without central owner

Infrastructure ready for large-scale algorithmic chemistry experiments.

D5: Autocatalysis in Cognitive Context

An autocatalytic set of thought patterns is a self-reinforcing belief system or set of procedures that help each other do things better.

Metabolism: system keeps itself going, absorbs energy, builds itself. Reproduction: system spawns new systems.

In cognitive context:

• Morphic resonance: grab what worked and keep doing it
• Morphic anti-resonance: if pattern becomes too prevalent, de-reinforce it to let other things get a chance
• Reproduction: knowledge subnetwork that works in one context spawns copies that try similar things in other contexts

D6: Priority Ordering in Hyperon

Priority order in Hyperon development: 1. Evolutionary learning 2. Logical uncertain reasoning 3. Less speculative items 4. Algorithmic chemistry (planned next)

Also: neurosymbolic deep neural nets integration.

Consequences

Autocatalysis, morphic resonance, and reproduction dynamics from origin-of-life research map directly to cognitive architectures.

Algorithmic chemistry may have more creative power than abductive reasoning or evolutionary learning alone.

LLMs enable running these simulations in spare time. Ideas come from humans. LLMs structure code and analyze results. LLMs not yet smart enough to generate the core idea (shortest path from non-life to proto-life) but are essential amplifiers.

Unique time: humans still need to drive the creative discovery. AGI not yet better at this. After singularity, humans will not be the best problem solvers. The work matters now while humans can still contribute meaningfully.

References

• Eigen-Schuster hypercycle (biochemistry)
• Walter Fontana algorithmic chemistry (1980s)
• Kristinn Thórisson RepliCode and ERA
• Oparin model of origin of life (water droplets as scaffolding)
• Anthropic principle (physics)
• Rupert Sheldrake morphic resonance
• Hyperon system with Meta language and MM2
• SingularityNET platform