Farmers Letters

Saturday, June 20, 2026

AI's Application to Creating Cellular LLMs based upon CRISPR Mods to RNA

Talk to Me, Baby!

Daddy Needs a Cure!

The Mechanical Decision Link: Biological Collective Intelligence (BCI) vs Artificial Collective Intelligence (ACI)

Paper: Decision Making in Light-Trapped Slime Molds Involves Active Mechanical Processes

Abstract: Decision-making is the process of selecting an action among alternatives, allowing biological and artificial systems to navigate complex environments and optimize behavior. While neural systems rely on neuron-based sensory processing and evaluation, decision-making also occurs in organisms without a centralized organizing unit, such as the unicellular slime mold Physarum polycephalum. Unlike neural systems, P. polycephalum relies on rhythmic peristaltic contractions to drive internal flows and redistribute mass, allowing it to adapt to its environment. However, while previous studies have focused on the outcomes of these decisions, the underlying mechanical principles that govern this mass relocation remain unknown. Here, we investigate the exploration process of P. polycephalum confined by blue light into polygonal shapes up to its escape. While the escape occurs along the longest axis of the polygons, independent of confinement shape, the exploration process prior to escape extends protrusions almost everywhere around a shape boundary. We find protrusions to align with the direction of peristaltic contraction waves driving mass relocation. Mapping out contraction modes during exploration in detail we observe an ongoing switching between different dominant principle contraction modes. Only over the course of time does the organism ultimately settle on the contraction mode most efficient for transport, which coincides with the escape. Thus, we find that only harsh environmental confinement triggers optimal behavior which is reached by long time reorganization of the flow patterns. Our findings provide insights into the mechanics of decision-making in non-neuronal organisms, shedding light on how decentralized systems process environmental constraints to drive adaptive behavior

Additional Info from Google AI:

Biological collective intelligence emerges from decentralized, living agents (like ant colonies, neural networks, or human societies) collaborating to solve complex problems. By contrast, artificial collective intelligence relies on interconnected machines, algorithms, or human-machine swarms that process massive datasets using centralized processing or multi-agent systems. [1, 2]
Key Differences at a Glance
Feature [1, 2, 3, 4, 5] Biological Collective Intelligence Artificial Collective Intelligence
Origin Emerges naturally through evolution, social learning, and decentralized communication (e.g., pheromones, speech). Programmed or engineered using multi-agent systems, neural networks, and massive training datasets.
Adaptability Highly adaptable to unpredictable environments because control is distributed downward to the lowest level (e.g., individual cells or insects). Often relies on top-down, human-engineered structures. Adaptation usually requires retraining models or modifying algorithms.
Scalability Naturally scalable with low overhead, as each agent is largely autonomous and self-sufficient. Highly scalable in computational power, but requires immense energy resources and specialized hardware infrastructure.
Efficiency Excellent at physical tasks, pattern recognition, and real-time environmental navigation despite limited individual cognitive power. Unmatched in processing speed, data aggregation, and structured logic, but often lacks real-world physical embodiment and context.
How They Compare
Adaptability & Resilience: Biological systems feature bottom-up architecture where individual parts are competent, self-organizing agents. This makes them resilient to localized failures, as there is no single point of failure. Artificial intelligence is often top-down and highly optimized for specific datasets, which can make it rigid or vulnerable when faced with entirely unprecedented, "out-of-distribution" scenarios. [1, 2]
The Human-Machine Synthesis: Collective intelligence increasingly overlaps through Hybrid Swarm Intelligence. Platforms utilizing real-time human-AI collaboration aim to merge the computational speed of artificial networks with the intuition, empathy, and context of human collectives.

Feature [1, 2, 3, 4, 5]	Biological Collective Intelligence	Artificial Collective Intelligence
Origin	Emerges naturally through evolution, social learning, and decentralized communication (e.g., pheromones, speech).	Programmed or engineered using multi-agent systems, neural networks, and massive training datasets.
Adaptability	Highly adaptable to unpredictable environments because control is distributed downward to the lowest level (e.g., individual cells or insects).	Often relies on top-down, human-engineered structures. Adaptation usually requires retraining models or modifying algorithms.
Scalability	Naturally scalable with low overhead, as each agent is largely autonomous and self-sufficient.	Highly scalable in computational power, but requires immense energy resources and specialized hardware infrastructure.
Efficiency	Excellent at physical tasks, pattern recognition, and real-time environmental navigation despite limited individual cognitive power.	Unmatched in processing speed, data aggregation, and structured logic, but often lacks real-world physical embodiment and context.

Leonard Susskind on Relativity and Quantum Physics

Andrei Tarkovsky

“We can express our feelings regarding the world around us either by poetic or by descriptive means... I prefer to express myself metaphorically. Let me stress: metaphorically, not symbolically. A symbol contains within itself a definite meaning, certain intellectual formula, while metaphor is an image. An image possessing the same distinguishing features as the world it represents. An image — as opposed to a symbol — is indefinite in meaning. One cannot speak of the infinite world by applying tools that are definite and finite. We can analyse the formula that constitutes a symbol, while metaphor is a being-within-itself, it’s a monomial. It falls apart at any attempt of touching it."