Analog computer: AKAT-1 (1959)
Last September I posted the Pancomputation trilogy (parts: I, II & III) which was a follow-up to last spring’s Digital Dualism trilogy (parts: 1, 2 & 3). The first trilogy was a continuation of an exploration of computer modeling I started in 2019. Suffice to say, over the course of writing these posts, my views on what “computing” means evolved and crystalized.
As discussed in the Pancomputation posts the notion of computation is difficult to pin down (many general concepts are because we don’t have even more general concepts to define them with). A pancomputation view sees everything as computing. A computer science view restrictively equates it with a Turing Machine.
I’ve realized my view depends heavily on computational dualism.
Oh, look! Dancing Pixies!
In the last two posts I’ve explored some ideas about what a computer is. More properly, what a computation is, since a computer is just something that does a computation. I’ve differentiated computation from calculation and, more importantly, evaluation. (This post assumes you’ve read Part I and Part II.)
I’ve also looked at pancomputationalism (the idea everything computes). The post hoc approach of mapping of random physical states to a computation seems especially empty. The idea of treating the physical dynamics of a system as a computation has more interesting and viable features.
That’s where I’ll pick things up.
Last time I began exploring what we mean by the terms “computer” or “computation.” Upon examination, these turn out to be not entirely obvious, so some resort to the edge cases: Computers are Turing Machines; or Everything is a computer.
Even then the situation remains stubbornly not obvious. Turing Machines are abstractions quite different from what we typically call computers. Saying everything computes creates such a broad umbrella that it renders the notion of computation nearly useless.
This series explores the territory between those edge cases.
Earlier this year I wrote a trilogy of posts exploring digital dualism — the notion that a (conventional) computer has a physical layer that implements a causally distinct abstract layer. In writing those posts I found my definition of computation shifting slightly to embrace the notion of that dualism.
The phrase “a (conventional) computer” needs unpacking. What is a computer, and what makes one conventional? Computer science offers a mathematical view. Philosophy, as it often does, spirals in on the topic and offers a variety of pancomputation views.
In this series I’ll explore some of those views.
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.
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’ve written here before about chaos theory and how it prevents us from calculating certain physical models effectively. It’s not that these models don’t accurately reflect the physics involved; it’s that any attempt to use actual numbers introduces tiny errors into the process. These cause the result to drift more and more as the calculation extends into the future.
This is why tomorrow’s weather prediction is fairly accurate but a prediction for a year from now is entirely guesswork. (We could make a rough guess based on past seasons.) Yet the Earth itself is a computer — an analog computer — that tells us exactly what the weather is a year from now.
The thing is: it runs in real-time and takes a year to give us an answer!