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Once one has the idea of “equilibrium”, one can then start to think of its properties as purely being functions of certain parameters—and this opens up all sorts of calculus-based mathematical opportunities. In living systems one sometimes also cares about the transport of electrons—though more often it’s atoms and ions and molecules.
And there are also foundational questions in computerscience. But my recent work on the foundations of machine learning suggests a broader approach, that can also potentially tell us things about the fundamental character of language, and about how it serves as a medium that can “ transport thoughts ” from one mind to another.
It’s not obvious that it would be feasible to find the path of the steepest descent on the “weight landscape” But calculus comes to the rescue. As we mentioned above, one can always think of a neural net as computing a mathematical function—that depends on its inputs, and its weights.
But with the multicomputational paradigm there’s now the remarkable possibility that this feature of physics could be transported to many other fields—and could deliver there what’s in many cases been seen as a “holy grail” of finding “physics-like” laws. I know of a few perhaps-closer approaches to our conception of multiway systems.
To make a closer analogy with quantum mechanics one can start thinking about combining different chunks of “multiway game play”, and trying to work out a calculus for how those chunks fit together. The games we’ve discussed here are all in a sense pure “games of skill”.
But with the multicomputational paradigm there’s now the remarkable possibility that this feature of physics could be transported to many other fields—and could deliver there what’s in many cases been seen as a “holy grail” of finding “physics-like” laws. I know of a few perhaps-closer approaches to our conception of multiway systems.
It didn’t help that his knowledge of physics was at best spotty (and, for example, I don’t think he ever really learned calculus). “Lick” Licklider —who persuaded Ed to join BBN to “teach them about computers”. Nowadays we’d call it the trie (or prefix tree) data structure. But his name shows up from time to time.
Because it implies that whatever “computational parametrization” or “computational description language” one uses for the ruliad, one will almost always get something that can be viewed as “computationally equivalent”. But what about other models of computation—like cellular automata or register machines or lambda calculus?
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