In the nearly nine years of this blog I’ve written many posts about human consciousness with regard to computers. Human consciousness was a key topic from the beginning. So was the idea of conscious computers.
In the years since, there have been myriad posts and comment debates. It’s provided a nice opportunity to explore and test ideas (mine and others), and my views have evolved over time. One idea I’ve found increasingly skepticism for is computationalism, but it depends on which of two flavors of it we mean.
I find one flavor fascinating, but can see the other as only metaphor.
I cracked up when I saw the headline: Why your brain is not a computer. I kept on grinning while reading it because it makes some of the same points I’ve tried to make here. It’s nice to know other people see these things, too; it’s not just me.
Because, to quote an old gag line, “If you can keep your head when all about you are losing theirs,… perhaps you’ve misunderstood the situation.” The prevailing attitude seems to be that brains are just machines that we’ll figure out, no big deal. So it’s certainly (and ever) possible my skepticism represents my misunderstanding of the situation.
But if so I’m apparently not the only one…
One solution to the puzzle.
I’ve written a lot lately about the physical versus the virtual. I’ve also written about algorithms and the role they play. In this post, I revisit both by exploring what is, for me, an old friend: The Eight Queens Puzzle. The goal is to place eight chess queens on a chessboard such that none can take another in a single move.
The puzzle is simple enough, yet just challenging enough, that it’s a good problem for first-year student programmers to solve. That’s where I met it, and it’s been a kind of “Hello, World!” algorithm for me ever since.
I thought it might be a fun way to explore a simple virtual reality.
In the last post I explored how algorithms are defined and what I think is — or is not — an algorithm. The dividing line for me has mainly to do with the requirement for an ordered list of instructions and an execution engine. Physical mechanisms, from what I can see, don’t have those.
For me, the behavior of machines is only metaphorically algorithmic. Living things are biological machines, so this applies to them, too. I would not be inclined to view my kidneys, liver, or heart, as embodied algorithms (their behavior can be described by algorithms, though).
Of course, this also applies to the brain and, therefore, the mind.
There’s a discussion that’s long lurked in a dusty corner of my thinking about computationalism. It involves the definition and role of algorithms. The definition isn’t particularly tricky, but the question of what fits that definition can be. Their role in our modern life is undeniably huge — algorithms control vast swaths of human experience.
Yet some might say even the ancient lowly thermostat implements an algorithm. In a real sense, any recipe is an algorithm, and any process has some algorithm that describes that process.
But the ultimate question involves algorithms and the human mind.
As a result of lurking on various online discussions, I’ve been thinking about computationalism in the context of structure versus function. It’s another way to frame the Yin-Yang tension between a simulation of a system’s functionality and that system’s physical structure.
In the end, I think it does boil down the two opposing propositions I discussed in my Real vs Simulated post:  An arbitrarily precise numerical simulation of a system’s function;  Simulated X isn’t Y.
It all depends on exactly what consciousness is. What can structure provide that could not be functionally simulated?
Indulging in another round of the old computationalism debate reminded me of a post I’ve been meaning to write since my Blog Anniversary this past July. The debate involves a central question: Can the human mind be numerically simulated? (A more subtle question asks: Is the human mind algorithmic?)
An argument against is the assertion, “Simulated water isn’t wet,” which makes the point that numeric simulations are abstractions with no physical effects. A common counter is that simulations run on physical systems, so the argument is invalid.
Which makes no sense to me; here’s why…
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…”