This continues my discussion of A Computational Foundation for the Study of Cognition, a 1993 paper by philosopher and cognitive scientist David Chalmers (republished in 2012). The reader is assumed to have read the paper and the previous post.
I left off talking about the differences between the causality of the (human) brain versus having that “causal topology” abstractly encoded in an algorithm implementing a Mind CSA (Combinatorial-State Automata). The contention is that executing this abstract causal topology has the same result as the physical system’s causal topology.
As always, it boils down to whether process matters.
No, sorry, I don’t mean the Bletchey Bombe machine that cracked the Enigma cipher. I mean his theoretical machine; the one I’ve been referring to repeatedly the past few weeks. (It wasn’t mentioned at the time, but it’s the secret star of the Halt! (or not) post.)
The Turing Machine (TM) is one of our fundamental definitions of calculation. The Church-Turing thesis says that all algorithms have a TM that implements them. On this view, any two actual programs implementing the same algorithm do the same thing.
Essentially, a Turing Machine is an algorithm!
Over the past few weeks we’ve explored background topics regarding calculation, code, and computers. That led to an exploration of software models — in particular a software model of the human brain.
The underlying question all along is whether a software model of a brain — in contrast to a physical model — can be conscious. A related, but separate, question is whether some algorithm (aka Turing Machine) functionally reproduces human consciousness without regard to the brain’s physical structure.
Now we focus on why a software model isn’t what it models!
Is that you, HAL?
Last time, in Calculated Math, I described how information — data — can have special characteristics that allow it to be interpreted as code, as instructions in some special language known to some “engine” that executes — runs — the code.
In some cases the code language has characteristics that make it Turing Complete (TC). One cornerstone of computer science is the Church-Turing thesis, which says that all TC languages are equivalent. What one can do, so can all the others.
That is where we pick up this time…
The previous post, Halt! (or not), described the Turing Halting Problem, a fundamental limit on what computers can do, on what can be calculated by a program. Kurt Gödel showed that a very similar limit exists for any (sufficiently powerful) mathematical system.
This raises some obvious questions: What is calculation, exactly? What do we mean when we talk about a program or algorithm? (And how does all of this connect with the world of mathematics?)
Today we’re going to start exploring that.