July 28th, 2024

Complex systems emerge from simple rules

The article explores emergent systems, illustrating how complexity arises from simple rules, using examples like the Game of Life and large language models, emphasizing interconnectedness in biology, chemistry, and physics.

Read original articleLink Icon
Complex systems emerge from simple rules

The article discusses the concept of emergent systems, emphasizing that complex systems can arise from simple rules. It illustrates this idea using the Game of Life, a cellular automaton with four basic rules governing the survival and reproduction of cells. These simple rules can lead to intricate patterns and behaviors, demonstrating how complexity can emerge from simplicity. The author argues that biology emerges from chemistry, which in turn emerges from physics, highlighting that all these layers of complexity are interconnected. Emergent properties are defined as characteristics that arise from the interactions of components within a system, which do not belong to the individual components themselves. The article also touches on large language models like ChatGPT, which exhibit emergent properties, producing coherent text and solving problems in ways that were not explicitly programmed. The unpredictability of these emergent properties is noted, as developers could not foresee the capabilities of models like GPT-3. The author reflects on human behavior as another example of a complex system, pondering the origins of thoughts and actions and the rules that govern them. Overall, the piece encourages contemplation of the nature of complexity in both artificial and natural systems, suggesting that understanding these emergent properties could shape the future of artificial intelligence.

Related

We must seek a widely-applicable Science of Systems

We must seek a widely-applicable Science of Systems

The text discusses the importance of a Science of Systems, focusing on Complex Systems. Emphasizing computer science's role, it explores potential applications in various fields and advocates for scientific progress through unified theories.

Computational Life: How Well-Formed, Self-Replicating Programs Emerge

Computational Life: How Well-Formed, Self-Replicating Programs Emerge

The study explores self-replicating programs on computational substrates, emphasizing emergence from random interactions and self-modification. It investigates programming languages, machine instruction sets, and theoretical possibilities, contributing to Origin of Life and Artificial Life fields.

Darwin Machines

Darwin Machines

The Darwin Machine theory proposes the brain uses evolution to efficiently solve problems. It involves minicolumns competing through firing patterns, leading to enhanced artificial intelligence and creativity through recombination in cortical columns.

Artificial consciousness: a perspective from the free energy principle

Artificial consciousness: a perspective from the free energy principle

The article explores artificial consciousness through the free energy principle, suggesting the need for additional factors beyond neural simulations to replicate consciousness in AI. Wanja Wiese emphasizes self-organizing systems and causal flow's role in genuine consciousness.

The Puzzle of How Large-Scale Order Emerges in Complex Systems

The Puzzle of How Large-Scale Order Emerges in Complex Systems

Researchers explore emergence in complex systems, defining it as patterns from interactions. A new framework likens emergent phenomena to "software in nature," governed by macroscale rules. Computational mechanics simplifies predicting emergent behaviors.

Link Icon 8 comments
By @talkingtab - 7 months
OMG one must wonder about a system that produces this article and especially these comments. If you are interested in this area you might consider:

* Hidden Order by John Holland. Especially if you are technical, then read it from the perspective of "how would I build one of these"? (See RIP).

* Signs Of Life: How Complexity Pervades Biology. An overwhelming book. At the very least you should come out aware of how little we understand.

* What is money? Really. We all know the mythical person month perspective, but according to that New York City can not exist.

* The RIP routing protocol.

* A New Kind of Science.

Gestalts, created from complex adaptive systems, surround us. (Inside joke). I think this is an example of the old adage "I won't see it until I believe it" which is just a simplified version of Sapir Whorf.

[edit: add Wolfram's "A New Kind of Science" ]

By @nathan_compton - 7 months
Utterly banal. For one thing, to say that life supervenes upon chemistry which supervenes upon physics is actually taking the _opposite_ position than "life is emergent," at least to the extent that philosophers agree about what emergent means. I understand emergence to mean what Deacon means: the properties of the emergent system are independent from the properties of the system underneath it at least to the extent that you couldn't predict the behavior of the emergent system with just the physics, at least not without a lot of work and the specific initial conditions required. The idea being that many possible systems could serve as the basis for the emergent system and that the emergent system is some kind of set of self-reinforcing constraints imposed on the underlying system.

As it happens, I don't believe this argument if taken all the way. Sure, emergence has the property that, in some sense, one knows more about the emergent system if it is described at its own level rather than at the level of the underlying system (eg, if you tell me you are hungry I'm much more likely to correctly predict you will order pizza than if you give me a catalog of the state of all your neurons), but your behavior still supervenes upon the neurons inexorably. Even the constraints that maintain the emergent system ultimately are expressed in terms of the underlying system.

By @hdivider - 7 months
Look at it this way: life on Earth is composed primarily of C, H, O, N , and a number of trace atoms.

If life evolved here on Earth, then somehow, CHON+trace all self-organized into all of Earth life today, including us, and all we humans have done and will do in the future.

Now: say we could go back just before life evolved. Even with very very good data, and with whatever talent (AI, science, anything) and technology from today, would we be able to truly describe the emergence paths for those CHON+trace atoms?

Impossible. This would be far beyond our level of science and technology. We can't even do this for much simpler systems and shorter time horizons. You'd have to predict the emergence of life, the full properties and behavior of all forms of life in the last 4 billion years, and last but not least, humans and all that humans have done and thought since then.

Yet clearly nature emerged all this from perhaps small amounts of 11 elements or so. Complexity is one of the greatest unknowns in our present civilization.

By @therediterator - 7 months
One way of thinking is that the rules are designed by humans. The game is not a game until we establish the rules. At any point in time in this realm of life, if we try to establish some control, or try to create a frame of "simple" rules around it, it becomes complex.

So it doesn't matter if the ChatGPT is designed by simple rules, the minute we try to control the randomness, it becomes complex.

By @janalsncm - 7 months
Most systems don’t have interesting stable states like Conway’s Game of Life, they either collapse or explode. You can verify this yourself by changing the rules of the Game of Life.

LLMs aren’t simple systems either, they are density estimators pretrained on terabytes of text, then fine tuned with human feedback. The simple fact that the author believes this suggests he doesn’t know much about LLMs.

By @WithinReason - 7 months
Not a great analogy, since Game of Life in Game of Life didn't emerge, it was programmed by a human.
By @shoggouth - 7 months
Other people might be interested in Stephen Wolfram's approach to physics[0].

[0]: https://wolframphysics.org/