This ends an arc of exploration of a Combinatorial-State Automata (CSA), an idea by philosopher and cognitive scientist David Chalmers — who despite all these posts is someone whose thinking I regard very highly on multiple counts. (The only place my view diverges much from his is on computationalism, and even there I see some compatibility.)
In the first post I looked closely at the CSA state vector. In the second post I looked closely at the function that generates new states in that vector. Now I’ll consider the system as a whole, for it’s only at this level that we actually seek the causal topology Chalmers requires.
It all turns on how much matching abstractions means matching systems.
This is a continuation of an exploration of an idea by philosopher and cognitive scientist David Chalmers — the idea of a Combinatorial-State Automata (CSA). I’m trying to better express ideas I first wrote about in these three posts.
The previous post explored the state vector part of a CSA intended to emulate human cognition. There I described how illegal transitory states seem to violate any isomorphism between mental states in the brain and the binary numbers in RAM locations that represent them. I’ll return to that in the next post.
In this post I want to explore the function that generates the states.
Last month I wrote three posts about a proposition by philosopher and cognitive scientist David Chalmers — the idea of a Combinatorial-State Automata (CSA). I had a long debate with a reader about it, and I’ve pondering it ever since. I’m not going to return to the Chalmers paper so much as focus on the CSA idea itself.
I think I’ve found a way to express why I see a problem with the idea. I’m going to have another go at explaining it. The short version turns on how mental states transition from state to state versus how a computational system must handle it (even in the idealized Turing Machine sense — this is not about what is practical but about what is possible).
“Once more unto the breach, dear friends, once more…”
This is what I imagined as my final post discussing 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 two posts.
This post’s title is a bit gratuitous because the post isn’t actually about intentional states. It’s about system states (and states of the system). Intention exists in all design, certainly in software design, but it doesn’t otherwise factor in. I just really like the title and have been wanting to use it. (I can’t believe no one has made a book or movie with the name).
What I want to do here is look closely at the CSA states from Chalmers’ paper.
Over the last three posts I’ve been exploring the idea of system states and how they might connect with computational theories of mind. I’ve used a full-adder logic circuit as a simple stand-in for the brain — the analog flow and logical gating characteristics of the two are very similar.
In particular I’ve explored the idea that the output state of the system doesn’t reflect its inner working, especially with regard to intermediate states of the system as it generates the desired output (and that output can fluctuate until it “settles” to a valid correct value).
Here I plan to wrap up and summarize the system states exploration.
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!
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…