It is indeed surprising to me that @stephen_wolfram 's AKNS which has been around for around 2 decades isn't as commonly accepted. It seems I still need to re-explain the ideas in that book. Here's a recent interview by @lexfridman
It's a 4-hour interview and there's a lot of newer ideas discussed here. But essentially, all reality is computation. I had assumed that this to be true for a very long time, but it didn't occur to me that this needed to be stated and explained.
Wolfram believes that he is finally beginning to understand quantum mechanics in an intuitively graspable way. This is indeed a bold assertion, but I do think he is on the right path.
His theory involves a general theory of computation that involves parallel computation and a notion of causal invariance. Causal invariance is that massively parallel computations arrive at the same state regardless of the timing of the computation of individual nodes.
All of nature as well as the brain is massively parallel and the fact that we see a definite reality is a consequence of invariant features emerging out of the computation.
The explanation of how the brain works may likely be the same explanation how the universe evolves. There is apparently a connection between quantum mechanics and the brain in that they are driven by common principles. medium.com/intuitionmachi…
The interesting thing is that Wolfram revealed the computational underpinnings of quantum mechanics and this may lead to new ideas of how to explain how evolution and the brain operate.
Therefore, all theories of the brain using classical physics methods are likely to be obsolete and we need to transition to a more modern framework that Wolfram is proposing.
Dynamics is essentially motion along a geodesic (path of least distance) in a space. General relativity is motion in physical space, Quantum mechanics is motion in 'branchial' space ( a space of causal relations ).
Analogously, the brain is in motion in its own mental space that perhaps involves several spaces that are structured like spacetime and causality.
The double-split experiment in quantum mechanics is a consequence of the interaction of the two spaces. That is, the physical setup in space time leads to a condition where branchial space diverges and thus there are no photons every arriving at parts of a sensor.
In a similar manner, the brain maintains a causal graph (classical, not quantum mechanical) that explores a limited set of possibilities to arrive at a conclusion as it navigates in space and time.
Deep Learning is based on calculus, a mathematics that is based on infinitesimal change in integer space. However, we need math that describes infinitesimal motion in fractional and dynamical dimensional space.
The uncertainty principle is a consequence of curvature in branchial space.
Fermions follow the exclusion principle, are involved in the branching in Branchial space Bosons merging in Branchial space and that the spin might have to do with how is possible for one but not the other. Fascinating!
In this interview, Wolfram goes even deeper and explains the Rulial space (the space of all possible rules). The conclusion (which I like) is that the universe is computational (and not hyper-computational) and that aliens may see causality differently given different rule frame.
Extending this idea to biological cognition is that branchial space is constrained by the umwelt of the organism (what it can perceive and what it can do). That is, its causality graph is generated by the possibilities of its agency.
Causal graphs are like second-order category theory. Morphisms are like paths in hypergraphs. What's the equivalent of black holes in mathematics? It looks like non-constructive proofs!
Excellent interview by @lexfridman . I'm looking forward to the next time he interview @stephen_wolfram for newer discoveries in his Physics project.
Wait, did I forget again the crux of the theory? Here it is: "The Universe is computational".

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More from @IntuitMachine

24 Sep
I've solved both Intelligence and Consciousness. They are both 'Error Correction' mechanisms. Let me know what I cannot explain from this hypothesis?
This is a better narrative than Intelligence is Compression. Intelligence as Compression doesn't explain consciousness. Many believe that you can't have intelligence without consciousness.
Oddly enough, the characteristic primary characteristic of democracy is also 'error correction'. Democracies might not elect perfect leaders but they always have options to make corrections on their mistakes.
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23 Sep
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It is universally true that if you convert a sequential algorithm into a parallel algorithm, the number of operations increases for the same problem. So how does a parallel brain do more with much less?
One way to reduce work is to not do any work at all! A brain does what it does because it avoids doing any work if it can. Call this the "lazy brain hypothesis".
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@ChrSzegedy @pwang @ESYudkowsky Any brain will store the necessary information it needs to recognize the difference in its perception that makes a difference in its actions. Not all information needs compression, so if you mean by compression, a lossy one, then it makes sense. Otherwise, it's total BS.
@ChrSzegedy @pwang @ESYudkowsky Lossless compression implies that the biological brain remembers everything it perceives. This is of course a myth. Therefore, the word 'compression' glosses over the notion that only what is relevant is remembered and recalled. Intelligence is about relevance realization.
@ChrSzegedy @pwang @ESYudkowsky And relevance realization is only possible relative to the subject that is performing the inference. That is, a living thing understands the world relative to the amortized knowledge that it has captured about the world.
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22 Sep
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There are non-linear phenomena that preserve itself for a short duration (example: whirlpools and hurricanes) only to eventually dissipate. These phenomena aren't living and also don't have digital codes.
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22 Sep
The relationship between diversity, generalization and co-evolution is indeed intriguing.
I have always been troubled with understanding the relationship between entropy and complexity. They are not the same thing, yet many formulations attempt to define one in terms of the other.
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The Sound of Silence by Simon and Garfunkel has always grabbed my attention. Paul Simon wrote this song when he was 21, but I doubt he could have predicted the emergent interpretations that listeners found in his song.
The Disturbed version of the song has an appealing interpretation (same lyrics). That captures that feeling of helplessness in a world of apathy.
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