This is the third post of a series exploring the duality I perceive in digital computation systems. In the first post I introduced the “mind stacks” — two parallel hierarchies of levels, one leading up to the human brain and mind, the other leading up to a digital computer and a putative computation of mind.
In the second post I began to explore in detail the level of the second stack, labeled Computer, in terms of the causal gap between the physical hardware and the abstract software. This gap, or dualism, is in sharp contrast to other physical systems that can, under a broad definition of “computation,” be said to compute something.
In this post I’ll continue, and hopefully finish, that exploration.
In the previous post I introduced the “mind stacks” — two essentially parallel hierarchies of organization (or maybe “zoom level” is a more apt term) — and the premise of a causal disconnect in the block labeled Computer. In this post I’ll pick up where I left off and discuss that disconnect in detail.
A key point involves what we mean by digital computation — as opposed to more informal, or even speculative, notions sometimes used to expand the meaning of computation. The question is whether digital computing is significantly different from these.
The goal of these posts is to demonstrate that it is.
The Age of Fire is a key milestone for a would-be technological civilization. Fire is a dividing line, a technology that gave us far more effectiveness. Fire provides heat, light, cooking, defense, fire-hardened wood and clay, and eventually metallurgy.
The Age of the Electron is another key technological milestone. Electricity provides heat and light without fire’s dangers and difficulties, it drives motors, and enables long-distance communication. It leads to an incredible array of technologies.
The Age of the Algorithm is just as much of a game-changer.
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.
Recently I mentioned that mathematician John Conway died last April. To his eternal disgruntlement, he is most famous for his “game” of Life — something he considered trivial and inferior to his real mathematical work. That fame is largely due to a Martin Gardener column in Scientific American — the most popular column the magazine had published up to then.
I said I wasn’t going to write about Life because it’s such a well-covered topic, but I thought I might whip up an implementation in Conway’s honor. (Went there; did that; videos below.) Getting into it made me realize Life connects back to my virtual reality posts.
So it turns out I am going to write about it (a little).
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.
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…”