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In this episode we have the difficult yet beautiful topic of trying to model complex systems like nature and the universe computationally to get into; and how beyond a low level of complexity all systems, seem to become equally unpredictable. We have a whole episode in this series on Complexity Theory in biology and nature, but today we’re going to be taking a more physics and computational slant.

Another key element to this episode is Observer Theory, because we have to take into account the perceptual limitations of our species’ context and perspective, if we want to understand how the laws of physics that we’ve worked out from our environment, are not and cannot be fixed and universal but rather will always be perspective bound, within a multitude of alternative branches of possible reality with alternative possible computational rules. We’ll then connect this multi-computational approach to a reinterpretation of Entropy and the 2nd law of thermodynamics.

The fact that my guest has been building on these ideas for over 40 years, creating computer language and Ai solutions, to map his deep theories of computational physics, makes him the ideal guest to help us unpack this topic. He is physicist,  computer scientist and tech entrepreneur Stephen Wolfram. In 1987 he left academia at Caltech and Princeton behind and devoted himself to his computer science intuitions at his company Wolfram Research. He’s published many blog articles about his ideas, and written many influential books including “A New kind of Science”, and more recently “A Project to Find the Fundamental Theory of Physics”, and “Computer Modelling and Simulation of Dynamic Systems”, and just out in 2023 “The Second Law” about the mystery of Entropy. 

One of the most wonderful things about Stephen Wolfram is that, despite his visionary insight into reality, he really loves to be ‘in the moment’ with his thinking, engaging in socratic dialogue, staying open to perspectives other than his own and allowing his old ideas to be updated if something comes up that contradicts them; and given how quickly the fields of physics and computer science are evolving I think his humility and conceptual flexibility gives us a fine example of how we should update how we do science as we go. 


Why we discuss: 

00:00 Intro

07:45 The history of scientific models of reality: structural, mathematical and computational.

14:40 Late 2010’s: a shift to computational models of systems.

20:20 The Principle of Computational Equivalence (PCE)

24:45 Computational Irreducibility - the process that means you can’t predict the outcome in advance.

27:50 The importance of the passage of time to Consciousness.

28:45 Irreducibility and the limits of science.

33:30 Godel’s Incompleteness Theorem meets Computational Irreducibility.

39:00 Relations between pockets of reducibility can’t build up to a theory of everything.

41:30 Our scientific egocentric perspective gives us info on our perspective, but not on the whole universe.

42:20 Observer Theory and the Wolfram Physics Project.

45:30 Modelling the relations between discrete units of Space: Hypergraphs.

47:30 The progress of time is the computational process that is updating the network of relations.

49:20 Space and time are very different: Space is the extension of this big network and time is the progression of computation applied to this network.

50:30 We ’make’ space.

51:30 Branchial Space - different quantum histories of the world, branching and merging

54:30 We perceive space and matter to be continuous because we’re very big compared to the discrete elements.

56:30 Branchial Space VS Many Worlds interpretation.

58:50 Rulial Space: All possible rules of all possible interconnected branches.

01:04:45 Our laws of physics are bound to our observer perspective, observers aggregate different branches of history.

01:07:30 Wolfram Language bridges human thinking about their perspective with what is computationally possible.

01:10:30 Could biological or artificial intelligences in different rulial spaces be able to interpret computational language?

01:11:00 Computational Intelligence is everywhere in the universe. e.g. the weather.

01:18:15 Uses in other sciences like Biology of the ‘Observers aggregate different branches of history’ idea. 

01:19:30 The Measurement problem of QM meets computational irreducibility and observer theory. 

01:20:30 Entanglement explained - common ancestors in branchial space.

01:31:15 Maximum Entanglement speed through branchial space.

01:32:40 Inviting Stephen back for a separate episode on AI safety, safety solutions and applications for science, as we did’t have time.

01:33:30 “The thing about computational irreducibility is that there can always be surprises”.

01:36:30 Tendency towards Entropy only from our observers aggregate point of view.

01:37:30 At the molecular level the laws of physics are reversible.

01:40:00 We can’t invert the computation with our computational limitations.

01:40:30 What looks random to us in entropy is actually full of the data.

01:42:30 Time symmetry meets time being the process of computation.

01:45:30 Entropy defined in computational terms.

01:48:20 The fact that we believe in the 2nd law is because our minds are finite.

01:50:30 If we ever overcame our finite minds, there would be no coherent concept of existence.

01:51:30 Parallels between modern physics and ancient eastern mysticism and cosmology.

01:53:30 Other knowledge traditions are not linear in their progression of concepts.

01:55:30 Reductionism in an irreducible world: saying a lot from very little input.

01:58:00 Mechanistic reductionism is wrong because of observer theory.





“The Second Law: Resolving the Mystery of the Second Law of Thermodynamics”, Stephen Wolfram

“A New Kind of Science”, Stephen Wolfram

Observer Theory Article, Stephen Wolfram

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