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.
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.
I’ve always liked (philosopher and cognitive scientist) David Chalmers. Of those working on a Theory of Mind, I often find myself aligned with how he sees things. Even when I don’t, I still find his views rational and well-constructed. I also like how he conditions his views and acknowledges controversy without disdain. A guy I’d love to have a beer with!
Back during the May Mind Marathon, I followed someone’s link to a paper Chalmers wrote. I looked at it briefly, found it interesting, and shelved it for later. Recently it popped up again on my friend Mike’s blog, plus my name was mentioned in connection with it, so I took a closer look and thought about it…
Then I thought about it some more…
Last Friday I ended the week with some ruminations about what (higher) consciousness looks like from the outside. I end this week — and this posting mini-marathon — with some rambling ruminations about how I think consciousness seems to work on the inside.
When I say “seems to work” I don’t have any functional explanation to offer. I mean that in a far more general sense (and, of course, it’s a complete wild-ass guess on my part). Mostly I want to expand on why a precise simulation of a physical system may not produce everything the physical system does.
For me, the obvious example is laser light.
I’ve been on a post-a-day marathon for two weeks now, and I’m seeing this as the penultimate post (for now). Over the course of these, I’ve written a lot about various low-level aspects of computing, truth tables and system state, for instance. And I’ve weighed in on what I think consciousness amounts to.
How we view, interpret, or define, consciousness aside, a major point of debate involves whether machines can have the same “consciousness” properties we do. In particular, what is the role of subjective experience when it comes to us and to machines?
For me it boils down to a couple of key points.
Moving on from system states (and states of the system), today I’d like to fly over the landscape of different systems. In particular, systems that are — or are not — viewed as conscious.
Two views make this especially interesting. The first holds that everything is computing everything and — under computationalism — this includes conscious computations. The second (if I understand it) holds that anything that processes input data into some kind of output is conscious. (I’m not clear if the view also sees an input-output system as a computer.)
So I want to explore what I see as major landmarks in the landscape of systems that… well, about the only thing we can probably all agree on is that they do something.
Last week we took a look at a simple computer software model of a human brain. (We discovered that it was big, requiring dozens of petabytes!) One goal of such models is replicating consciousness — a human mind. That can involve creating a (potentially superior) new mind or uploading an existing human mind (a very different goal).
Now that we’ve explored the basics of calculation, code (software), computers, and (computer software) models, we’re ready to explore what’s involved in attempting to model a (human) mind.
I’m dividing the possibilities into four basic levels.