Conclusion: Every system, open or closed, is computational in a certain rigorous sense of implementing dynamics along the space dimensions (versus inputs, via transformations, into outputs) and demonstrating dynamics along the time dimension (flux of the energy+information carried through its constituent parts).
If we can create a "computer" using rocks in a binary-ish encoding, surely we can implement "computers" in more efficient encodings that utilize certain properties of physical reality.
We as humans began to approach this with mechanical computers using complex linkages, but only got so far before we decided on digital (binary) encoding and then enshrined that in silicon. To be sure, binary is a very effective way of organizing computation given our physical implementation(s), but most likely there are way more efficient ways of doing things.
One fascinating direction we are beginning to explore to achieve more efficient computation is marrying machine learning with materials engineering![1]
In fact, by this conclusion, we can only believe that the universe is merely the most efficient way to simulate itself, but this by no means precludes the possibility of simulating it in any other medium (encoding).
Premise 2: https://xkcd.com/505/
Conclusion: Every system, open or closed, is computational in a certain rigorous sense of implementing dynamics along the space dimensions (versus inputs, via transformations, into outputs) and demonstrating dynamics along the time dimension (flux of the energy+information carried through its constituent parts).
If we can create a "computer" using rocks in a binary-ish encoding, surely we can implement "computers" in more efficient encodings that utilize certain properties of physical reality.
We as humans began to approach this with mechanical computers using complex linkages, but only got so far before we decided on digital (binary) encoding and then enshrined that in silicon. To be sure, binary is a very effective way of organizing computation given our physical implementation(s), but most likely there are way more efficient ways of doing things.
One fascinating direction we are beginning to explore to achieve more efficient computation is marrying machine learning with materials engineering![1]
In fact, by this conclusion, we can only believe that the universe is merely the most efficient way to simulate itself, but this by no means precludes the possibility of simulating it in any other medium (encoding).
[1] https://www.nature.com/articles/s41586-021-04223-6