An article in Science magazine discussed how the behavior of ant colonies can be viewed as “computer algorithms,” with each ant running a simple program that enables the colony to perform complex tasks such as deciding when and where to move the nest. Unlike a central leader, ants operate autonomously, basing decisions on interactions with a few nearby ants, leading to a different kind of computation than traditional computers with a central processing unit and random-access memory.
Similarly, a 1994 article by brain researchers questioned whether the brain is a computer, concluding that if we adopt a broader concept of computation, the answer is yes. The brain, like ant colonies, computes through billions of neurons working in parallel without central control.
In the previous chapters, we explored life and evolution in computers. Here, we examine how computation occurs in nature. Generally, computation in nature involves a complex system processing information to succeed or adapt in its environment. To study this, scientists use simplified models like cellular automata.
Cellular Automata
Turing machines formalize computation as transforming input on a tape via set rules to produce output. Modern computers, designed with von Neumann architecture, consist of random access memory (RAM) and a central processing unit (CPU) that executes instructions stored as strings of 1s and 0s.
A cellular automaton (CA) is a grid of cells, each following a rule based on the states of neighboring cells. For instance, imagine a grid of lightbulbs that turn on or off depending on the state of their eight neighbors. The grid updates synchronously, with each bulb following a rule like “turn on if the majority of neighbors are on.”
The variety of possible rules for cellular automata is vast. Each cell update rule determines the next state based on the current states in its neighborhood. These simple components, lacking central control, can exhibit complex, unpredictable behavior.
The Game of Life
John Conway’s “Game of Life,” a cellular automaton invented in 1970, uses a simple two-state system where cells are either alive (on) or dead (off). Conway’s rules involve birth, survival, loneliness, and overcrowding to determine cell states. Despite its simplicity, Life can simulate a universal computer, capable of performing any computation a standard computer can, though inefficiently.
The Four Classes
Physicist Stephen Wolfram studied cellular automata’s behavior, categorizing them into four classes based on their patterns:
- Class 1: Uniform final patterns.
- Class 2: Cycling between a few patterns.
- Class 3: Random-looking behavior.
- Class 4: Complex interactions and localized structures.
Wolfram proposed that class 4 rules are capable of universal computation. His assistant Matthew Cook proved that Rule 110, a simple elementary cellular automaton, is universal.
Wolfram’s “New Kind of Science”
In his 2002 book, A New Kind of Science, Wolfram presented the Principle of Computational Equivalence, suggesting:
- Natural processes are computations.
- Many natural systems can support universal computation.
- Universal computation limits the complexity of natural computations.
- Natural computations are equivalent in sophistication.
Wolfram speculated that the universe operates on simple cellular automaton-like rules, possibly describable in a few lines of code. His book, despite mixed reviews, brought significant attention to cellular automata research.
Wolfram’s theories remain controversial. The idea that all computations in nature are equivalent in sophistication is debatable, as is the notion of a single rule governing the universe. Nonetheless, his work highlights the potential of simple models to explain complex natural phenomena.
In 1989, I read an article by physicist Norman Packard about using genetic algorithms to design cellular automaton rules, which fascinated me. Although other commitments delayed my work on this idea, I finally had the opportunity to explore it with the help of Peter Hraber and Rajarshi Das at the Santa Fe Institute.
We aimed to evolve cellular automaton rules for the “majority classification” task, where the automaton must determine whether the initial configuration has a majority of on or off states. Unlike von-Neumann-style computers, cellular automata lack central processing and random access memory, making this task challenging.
Using a genetic algorithm, we evolved rules for a one-dimensional cellular automaton with each cell connected to three neighbors on either side. Initially, we considered a simple “local majority vote” rule, which proved ineffective. We then encoded cellular automaton rules as bit strings and used the genetic algorithm to evolve solutions over multiple generations.
The evolved rules, although not immediately understandable at the genomic level, demonstrated correct behavior through complex patterns. Physicist Jim Crutchfield’s conceptual tools helped us interpret these patterns as information-processing structures. We identified “particles” representing boundaries between regions, which carried information and facilitated the computation.
This particle-based analysis explained how the cellular automaton processed information and performed the majority classification task. The genetic algorithm had evolved a rule that could be understood in terms of these information-processing particles.
This approach to understanding computation in decentralized systems could extend to other contexts, including brain computation and the behavior of plant stomata networks, where distributed, particle-like interactions may play a role in information processing.
Computation in Nature
Szilard’s insight into the connection between information and the second law of thermodynamics has significantly impacted science, elevating information to a fundamental component of reality alongside mass and energy. In biology, living systems are often described as information-processing networks. However, the concept of information processing remains somewhat ambiguous outside the formal context of Turing machines and von Neumann-style computers.
This chapter explores the concept of information processing in natural systems, focusing on the immune system, ant colonies, and cellular metabolism. The goal is to identify common principles of information processing in these decentralized systems.
What is Information Processing?
In natural systems, computation is the processing of information to adapt to or succeed in the environment. To understand this, we need to answer:
- What constitutes information in the system?
- How is it communicated and processed?
- How does this information acquire meaning?
Information Processing in Traditional Computers
Turing formalized computation in the 1930s, which led to the design of von Neumann-style computers. In these computers:
- Information is represented by tape symbols and states of the tape head.
- It is communicated and processed through reading, writing, and state changes by the tape head, following the program’s rules.
- The meaning of information comes from human interpretation.
Information Processing in Cellular Automata
For cellular automata, the answers are less clear. For instance, in a cellular automaton evolved to perform majority classification:
- Information is in the lattice’s state configurations at each time step.
- It is communicated and processed via neighborhood cell interactions following the automaton’s rule.
- Meaning is derived from the human interpretation of the task being performed.
High-level descriptions like particles and their interactions can help understand information processing in cellular automata.
Information Processing in Living Systems
The chapter explores information processing in the immune system, ant colonies, and cellular metabolism to identify common principles.
The Immune System
The immune system processes information to protect the body from pathogens. Key components include lymphocytes, which have receptors that bind to specific antigens. The system employs randomness to create diverse receptors and uses feedback mechanisms to determine whether an immune response is warranted.
Ant Colonies
Ant colonies perform complex tasks like foraging and task allocation without central control. Foraging ants leave pheromone trails, which other ants follow based on pheromone concentration. Task allocation involves ants switching tasks based on encounters with other ants performing different tasks.
Cellular Metabolism
Metabolism involves chemical processes within cells, controlled by metabolic pathways and feedback mechanisms. For instance, glycolysis adjusts based on ATP levels, demonstrating how pathways self-regulate to meet the cell’s needs.
Principles of Information Processing in Decentralized Systems
Key principles include:
Communication via Sampling
Information is communicated through spatial and temporal sampling by individual components, like lymphocytes sampling antigens or ants sampling pheromones.
Random Components of Behavior
Randomness allows components to explore a vast space of possibilities. For example, lymphocytes have randomly generated receptors, and ant foragers move randomly to find food.
Fine-Grained Exploration
Complex systems benefit from fine-grained architecture, allowing simultaneous exploration of many possibilities. This parallel terraced scan enables systems to adapt dynamically based on ongoing feedback.
Interplay of Unfocused and Focused Processes
Adaptive systems balance random explorations with focused actions based on perceived needs. For example, the immune system continuously generates new lymphocytes while focusing on those that effectively bind antigens.
How Does Information Acquire Meaning?
Meaning in living systems is tied to survival and natural selection. Information gains meaning through its impact on an organism’s fitness, guiding responses that enhance well-being or reproductive success. This understanding can inspire artificial systems, such as artificial immune systems and ant colony optimization algorithms, to solve real-world problems.
Easy Things Are Hard
Recently, I asked my eight-year-old son, Jake, to put on his socks. He humorously placed them on his head, illustrating a fundamental difference between humans and computers. Despite inherent ambiguities in human language, we usually understand each other due to our sensitivity to context. For instance, when I ask my husband where my keys are, and he simply replies, “yes,” I get annoyed because I expected him to tell me their location.
Modern computers lack this contextual sensitivity. For example, spam filters may fail to identify obvious spam, and search engines often require tailored headlines to improve web accessibility. However, computers excel in certain narrow domains, such as driving vehicles across rugged terrain, diagnosing diseases, solving complex equations, and playing chess.
Despite these advancements, computers still struggle with tasks requiring human-level understanding, such as natural language processing and common sense reasoning. Marvin Minsky aptly described this paradox as “Easy things are hard.” Computers can perform tasks considered highly intelligent by humans but falter at simple tasks easily handled by a child.
Making Analogies
One critical capability missing in computers is the ability to make analogies. Analogy-making involves perceiving abstract similarities between different things despite superficial differences. This skill is fundamental to human intelligence.
Examples of Human Analogies:
- A child recognizes that dogs in picture books, photos, and real life are instances of the same concept.
- A person identifies the letter “A” in various typefaces and handwriting styles.
- Someone understands that saying “I call m