Category Archives: Computers

Information Systems

Last time I explored five physical systems. This time I want to implement those five systems as information systems, by which I mean numeric versions of those five systems. The requirement is that everything has to be done with numbers and simple manipulations of numbers.

Of course, to be useful, some parts of the system need to interact with the physical world, so, in terms of their primary information, these systems convert physical inputs into numbers and convert numbers into physical outputs.

Our goal is for the numeric systems to fully replace the physical systems.

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Virtual Reality

I’ve seen objections that simulating a virtual reality is a difficult proposition. Many computer games, and a number of animated movies, illustrate that we’re very far along — at least regarding the visual aspects. Modern audio technology demonstrates another bag of tricks we’ve gotten really good at.

The context here is not a reality rendered on screen and in headphones, but one either for plugged-in biological humans (à la The Matrix) or for uploaded human minds (à la many Greg Egan stories). Both cases do present some challenges.

But generating the virtual reality for them to exist in really isn’t all that hard.

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Physical vs Abstract

Maybe it’s a life-long diet of science fiction, but I seem to have written some trilogy posts lately. This post completes yet another, being the third of a triplet exploring the differences between physical objects and numeric models of those objects. [See Magnitudes vs Numbers and Real vs Simulated for the first two in the series.]

The motivation for the series is to argue against a common assertion of computationalism that numeric models are quintessentially the same as what they model. Note that these posts do not argue against computationalism, but against the argument conflating physical and numeric systems.

In fact, this distinction doesn’t argue against computationalism at all!

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Real vs 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…

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Bummer in the Dell

If I went with longer titles, I might have called this post Why I’ll Never Buy Another Dell Computer! Or I could have gone for the much shorter Dell Sucks! But I can’t resist a good pun or play on wyrds, so Bummer it is.

About a year ago I replaced my aging Sony Vaio laptop with a Dell XPS 15. The Sony taught me some hard lessons about buying a laptop online, one of them being “you’ll be sorry if you buy a Sony” — it had many annoyances, not the least of which was the wireless never worked. And it had a literal bug in it! The Dell is better in many ways, but,… well,…

Dell you disappoint me. Let me count the ways…

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Failed States (part 3)

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.

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Failed States (part 2)

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.

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Failed States (part 1)

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…”

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Intentional States

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.

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Algorithmic Causality

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.

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