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wolfram

rules for building these patterns that you make in cellular automata. So the obvious question was, could you evolve those rules like natural selection does? Could you make mutations and selection and so on on those underlying rules? And could you see that produce some sort of interesting patterns of growth. So I tried that in 1985. I didn't find anything interesting. Do you mind explaining how you tried that? Because look, the way that it works with
Concept
wolfram
Score
4 · causes · because
Status
candidate — not yet promoted to canon

Corpus evidence — top 10 passages

Most-relevant passages from the entire indexed corpus (67,286 paragraph chunks across YouTube transcripts, PubMed, arXiv, archive.org, Stanford Encyclopedia of Philosophy, OpenAlex, and more) ranked by semantic similarity (bge-small-en-v1.5).

  1. 01 · yt0.805

    Um, so anyway, this is this is what one finds. This is just looking at each each picture here is a different possible underlying rule. So they're in a sense different artificial physicses and the question is what do they all do? So many of them do very simple things but my all-time favorite science discovery is the one numbered 30 there rule 30 and if you look at what it does has a very simple uh kind of rule for how it's constructed but yet you started off in just one black cell it makes this pattern that seems kind of complicated. When I first saw this I kind of thought well if I run it long

    yt/OWyugUdBups-stephen-wolfram-computation-at-the-foundations-of-everything/transcript.txt

  2. 02 · yt0.799

    So I started thinking well okay what can can we write programs that will reproduce what happens in the natural world and uh then the next question was well you know the programs we're used to writing are big complicated things that are set up for some particular purpose we have in mind but what about programs in the wild what about programs which are just sort of picked at random enumerated very simple programs what do such programs typically do and so that got me into asking that question and so I started looking at very simple programs. Here's an example of of one. This is a creature called

    yt/OWyugUdBups-stephen-wolfram-computation-at-the-foundations-of-everything/transcript.txt

  3. 03 · pubmed0.793

    Despite Darwin, we remain children of Newton and dream of a grand theory that is epistemologically complete and would allow prediction of the evolution of the biosphere. The main purpose of this article is to show that this dream is false, and bears on studying patterns of evolution. To do so, I must justify the use of the word "function" in biology, when physics has only happenings. The concept of "function" lifts biology irreducibly above physics, for as we shall see, we cannot prestate the ever new biological functions that arise and constitute the very phase space of evolution. Hence, we c

    pubmed/PMID-24704211-prolegomenon-to-patterns-in-evolution/info.md

  4. 04 · blog0.792

    With \(r = 1\), there are 8 possible neighbors (see Fig. 4 above) to be mapped to \({1, 0}\), giving a total of \(2^8 = 256\) rules. Starting with random initial conditions, Wolfram went on to observe the behavior of each rule in many simulations. As a result, he was able to classify the qualitative behavior of each rule in one of four distinct classes. Repeating the original experiment, we simulated the evolution of two rules for each class of Wolfram’s scheme. 2.3 The Classes of the 256 Rules Class 1 rules leading to homogeneous states, all cells stably ending up with the same value: Rule 25

    blog/plato-stanford-edu/cellular-automata.md

  5. 05 · blog0.790

    The evolutionary pattern displayed contrasts with the simplicity of the underlying law (the “Hat rule”) and ontology (for in terms of object and properties, we only need to take into account simple cells and two states). The global, emergent behavior of the system supervenes upon its local, simple features, at least in the following sense: the scale at which the decision to wear the hat is made (immediate neighbors) is not the scale at which the interesting patterns become manifest. This example is a paradigmatic illustration of what makes CA appealing to a vast range of researchers: even perf

    blog/plato-stanford-edu/cellular-automata.md

  6. 06 · openalex-fanout0.789

    — cited 437x (1998) Introduction To Artificial Life — cited 408x (1993) Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations — cited 399x (1999) Through the Looking Glass of Complexity: The Dynamics of Organizations as Adaptive and Evolving Systems — cited 389x (2000) A brief history of cellular automata — cited 381x (1990) Invertible cellular automata: A review — cited 381x (2007) Cryptography with Cellular Automata — cited 379x (1994) Evolving cellular automata to perform computations: mechanisms and impediments — cited 356x

    openalex-fanout/W2001109406-universality-and-complexity-in-cellular-automata/info.md

  7. 07 · _intake0.786

    The “new guiding theory” evolution will not come from them. It will come from an outlier in another science or it will come from people like you, who realize the early mistake and begin to innovate a new grand theory. I decided ten years ago to use quantum theory to be the basis of my guiding theory of how a cell really works. I did so because the scientists in the area of physics have used experiments to validate their speculation. All science begins with observation that causes us to guess. From that guess, we do experiments to validate the guess. If the data does not support the guess, your

    _intake/kruse-blog-corpus/articles/energy-epigenetics-10-quantum-puzzle.md

  8. 08 · blog0.786

    Toad \(t_{0}\) \(t_{1}\) \(t_{3}\) Eaters have the feature of devouring other configurations, e.g., gliders, maintaining intact their own form (because of this, they play an important role for Life ’s computational abilities). An Eater devouring a Glider \(t_{0}\) \(t_{2}\) \(t_{4}\) A typical evolution of Life starting from random initial conditions may contain all of the above notable figures and much more. Some initial configuration may end up, even after few time steps, into static or simple periodic structures. Other configurations, though, can produce non-periodic, increasingly complex e

    blog/plato-stanford-edu/cellular-automata.md

  9. 09 · pubmed0.782

    A theory for the evolution of cellular organization is presented. The model is based on the (data supported) conjecture that the dynamic of horizontal gene transfer (HGT) is primarily determined by the organization of the recipient cell. Aboriginal cell designs are taken to be simple and loosely organized enough that all cellular componentry can be altered and/or displaced through HGT, making HGT the principal driving force in early cellular evolution. Primitive cells did not carry a stable organismal genealogical trace. Primitive cellular evolution is basically communal. The high level of nov

    pubmed/PMID-12077305-on-the-evolution-of-cells/info.md

  10. 10 · blog0.781

    In the early Sixties, E.F. Moore (1962) and Myhill (1963) proved the Garden-of-Eden theorems stating conditions for the existence of so-called Gardens of Eden, i.e., patterns that cannot appear on the lattice of a CA except as initial conditions. Gustav Hedlund (1969) investigated cellular automata within the framework of symbolic dynamics. In 1970 the mathematician John Conway introduced his aforementioned Life game (Berkelamp, Conway, & Guy 1982), arguably the most popular automaton ever, and one of the simplest computational models ever proved to be a universal computer. In 1977, Tommaso To

    blog/plato-stanford-edu/cellular-automata.md

Curation checklist

  • ☐ Verify excerpt against source recording
  • ☐ Tag tier (axiom · law · principle · primary derivation · observation)
  • ☐ Cross-cite to ≥1 primary source (PubMed / arXiv / archive.org)
  • ☐ Promote to bucket-canon/04-information/