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