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.
That said, minds make the situation complex on multiple accounts. For one, minds are the only topic we study while also having direct subjective experience. We know what consciousness feels like from the inside.
There is also that brains are, by far, the most complex natural mechanisms we’ve encountered. We simply don’t understand how they work in purely biological and mechanical terms.
But that is a nut we are cracking — one we will eventually open. We may run into any number of physical limits (resolution, bandwidth, data size, computational intractability, even Gödel) that prevent technology, but we ought to be able to grasp the science.
Given the actual hard problem (how phenomenal experience arises in a biological machine when physics has no account for it), plus the uniqueness of the brain as an information processing system, it’s possible some kind of naturalistic dualism turns out to be true.
Dualist only in the sense that it might be surprising to discover that solid materials arranged the right way, and stimulated by energy, can emit coherent light. That seems almost… magical. But it happens.
Or that different materials arranged a different right way, and stimulated by energy, can emit coherent thought. That, too, seems almost magical (but it happens).
[It’s only our commitment to naturalism that forces us to insist it can’t possibly be magic. Maybe someday science throws up its hands and says, “We just can’t figure it out. It’s effectively magic to us!”]
Anyway, to the topic at hand: The Mind Algorithm.
To review, the question is due to a list, presented in my Structure vs Function post. The list divided (numerical) mind simulations into three categories:
- A physical simulation of the brain.
- A functional emulation of the brain.
- A unified mind algorithm.
The first two options have foreseeable paths that we’re already setting forth on. The first involves physics simulations at some level of granularity. The second involves algorithmic replacements for brain functions.
In the post I said the last was something of a Holy Grail and chimera (remember, the Holy Grail didn’t exist although many sought it).
The challenge involves the possible existence of a “mind algorithm” so what would that entail?
One approach is to view physical machines as Turing Machines.
Machines typically have a closed set of states they cycle through, which is exactly why algorithms are good for describing them.
So it’s very tempting to see them as reified algorithms, and — when it comes to human-made machines — I can see a justification for the view. As I wrote last time, one can view the blueprint or design as the “program” and the machine itself as the engine. (I also said I think that’s more a metaphor than a reality.)
In any event, accepting the view, and seeing biology as a machine, gets us to algorithms reified in biological things.
A problem I see is that it’s one thing to look at a clock and pick apart the causal machine and implied algorithm, the machines in biology are very, very tiny. In some cases, molecular.
I’m not certain the resulting organ (the brain) is a “machine” in the usual sense (of a fully deterministic mechanism).
More to the point, I’m therefore not certain we can view the brain, or what it does, as algorithmic. Its complexity may transcend such a description.
Which, again, is not to say algorithms can’t describe these systems to some degree of precision. They absolutely can.
The issue whether a physical process is, itself, an algorithm (in more than a metaphorical sense). At a very fine grain, how information in DNA becomes proteins, for example, things do look very mechanical and algorithmic.
Above that level, however, things become increasingly analog and less algorithmic. I’m not sure having some properties of algorithms at low levels is a strong argument that a system is algorithmic.
There is another constraint I couldn’t get to last time. It involves the instruction set, which is the list of things the engine knows how to do.
Obviously an algorithm can’t have instructions that can’t be performed — that would break the algorithm. So it is limited to the instruction set known to a given engine.
It’s possible to view a simple machine, with a closed set of states, as an algorithm because we sense the instruction set for it is manageable. An instruction set for a putative mind algorithm seems a formidable challenge. Such a thing might be almost infinite.
This is also why the simple “machines” of biology appear algorithmic — we sense that manageable instruction set.
One more consideration I’ll throw into the mix: Algorithms, at least the obvious algorithms, are — it seems to me — distinctly human inventions.
They’re very early members of the information age (which combined technology and mathematics). As such, I’m not sure nature deals in algorithms, as tempting as it may be to see them there.
There is an interesting question about where algorithms source from. Can the thing that invented the idea be, itself, an algorithm?
Certainly algorithms can be created (by humans!) to generate other algorithms, but can an algorithm come up with a totally new idea (not some mashup of previous ideas)?
One characteristic of many mathematical operations is that they result in new number values, but not new numbers. When you add two integers, you always get an integer. The formal expression is that “integers are closed under addition.”
Algorithms seem, generally speaking, to be closed under their instruction sets.
They aren’t original thinkers. I like to think I am.
One final note: Al Gore and algorithms … these things are not the same. From what I’ve seen Al Gore has no rhythm at all.
Stay mindful, my friends!