Archive for the 'Computer Science' Category
In Bradford's thread Superstitious Nonsense I found myself sidetracked from the actual topic by a couple of our critics who seemed to be playing dumb. In the attempt to outline the evidence for immaterial process in what we call the "Mind" – as opposed to the physical machine we call the brain – I introduced the concept of noncomputability which factors large in the Hameroff-Penrose Orch-OR theory of consciousness.
Raevmo asked repeatedly for an example of this noncomputability, which I realize would take us even farther afield (deep subject). With that thread now over 100 comments, not something I would long be able to follow with my clunky dial-up. So this thread will take that topic out of Bradford's thread so that it can be examined further.
Recently I decided to check out the Biologic Institute website again, due to Guts linking it in a comment on this site. Generally I follow www.intelligentdesign.org for daily posts of interest in the ID community, so BI's updates haven't shown up on my radar.
Either way, the BI's most recent entry, where they reference current use of evolutionary principles in design, was of particular interest to me. From the link:
It’s not that evolutionary design has failed on computers—far from it. One of the most celebrated successes, for example, is a NASA antenna that looks like a bent paper clip.  It may not be much to look at, but this odd little design works better than any known alternative, which is why NASA has deployed it in space.
Designs like this are achieved by first generating a number of virtual prototypes on computers, where design parameters are assigned more-or-less arbitrary values, and then using a mathematical model to calculate how good each of these initial prototypes is. Mirroring survival of the fittest, the bad prototypes are discarded and the better ones are replicated to fill out a virtual population. To simulate mutation, the prototypes are then altered by changing their parameter values slightly.
This spawns a large collection of second-generation prototypes. The process of culling, replicating, and mutating is then repeated until an acceptably good design solution results. Numerous variations to this procedure can be applied, but this describes the general approach used in evolutionary computing. And in many interesting cases, like the NASA antenna, it works.
In the blog entry Indicators of Design TT commenter Rock stated:
Computer scientists have known for sometime, at least for 60 years, that simple systems are capable of universal computation, given only as much “programming” as is required by a statement of initial conditions, an adumbration (“netlist”) of the material properties of the computing elements, and a dynamical law over all.
I found an Uncommon Descent blog entry I had come across some time ago and subsequently forgotten. Gil Dodgen wrote Writing Computer Programs by Random Mutation and Natural Selection. There were a number of interesting comments and numbered among the commenters was at least one regular TT commenter.
One thing that has always interested me about computer simulated evolution of networks is that virtually (pun intended) all of them turn out to be non-modular. This has been pointed out in the literature (Thompson et. al 1998).
Modularity itself is something that the front-loading hypothesis predicts:
If life was designed and our analogy to human design is substantial, then we would predict life would be modular…Modularity enhances evolution and thus the perpetuation of design.
The Design Matrix p.167-168
The researchers running these sort of simulations make use of duplication, recombination, mutation, selection until they see the relevant result. However, there is a lot more to the evolution of life-like networks.
After watching as a number of threads descended into chaos from interesting starts, an underlying oddity seems to beg attention from the fisticuffs over word usage that has become so prevalent of late. In the Post-Wedge World the perennial dueling metaphysics hasn't waned one bit, but something new has come to the fore.
We've been mixing it up with a commenter who calls himself "aiguy" to identify with the field of computer science called "Artificial Intelligence." It would appear that he has a problem with ID's use of the word "Intelligent" to describe its focus. Aiguy tells us that we have no definition of intelligence for either AI or ID, but he wants ID to drop the term anyway, perhaps so he can feel better about the use of it in his own discipline of science. Who knows?
If it were just this one critic who was bent by the terminology it would just be a single critic with a single issue about terminology. Instead, aiguy is just the latest in a string of critics who have lodged complaints in recent months about ID's use of the word "Intelligent" and insisted that it be dropped from the lexicon.
It strikes me that with such universal focus on the word – whether the complaint is that it's a metaphysical concept or an ill-defined term – the 'other' word has slipped under the radar into mainstream usage. Is it now okay to speak of biological systems in terms of "Design" so long as "Intelligent" isn't attached?
One of our recent, frequent participants here at Telicthoughts — aiguy — had what I consider to be a fascinating, intriguing comment here:
http://telicthoughts.com/are-dem-bunny-prints/#comment-193453, which I quote below:
Indeed – I can learn!
And so can my computer. Using genetic and other machine learning algorithms, it has learned to design complex machines that I didn't even understand until I saw them work. Not only that, but it decides all by itself what it is that it wants to design!
It really is so counter-intuitive to see how simple laws and a deterministic machine operating according to blind, natural processes gives rise to amazingly intelligent results without any intervention from me. I suppose it could be said to demonstrate "foresight" (it produces designs all at once without visible intermediate trial-and-error), but of course everything it does really is completely determined by its innate structure and its interactions with its environment. I have no reason to think it is aware of anything, so if it has beliefs and desires it doesn't know it. I really don't think it knows what it is doing – or why.
Yup, a completely unconscious, blind, unaware, deterministic physical mechanism operating according to nothing but fixed law and chance, incapable of doing anything but what it does, and there it is grinding out artifacts of complex form and function. Intelligence is so cool!
When materials scientists and developers discovered the neat, multi-functional, elegantly designed biological structure of protein "nanotubes" in eukaryote cells they eagerly 'borrowed' the designs – thought to arise spontaneously through the binding affinities of the state-switching proteins involved – to construct similar artificial ['artifactual'] structures of carbon atoms (occasionally doped with gold or other atoms) for use in development of future generations of electronic devices and medical treatment delivery systems.
Then, much to the surprise of scientists generally, it was discovered that life can fabricate nanotubes of particular elemental form too – as 'artifacts' in their environment, not as pieces-parts of themselves. The work appears in the December 7 issue of PNAS and was partially funded by the Center for Nanoscale Innovation for Defense…
Mathematician Stephen Wolfram's A New Kind of Science is devoted to self-organization and complexity theory, or how simple rules can have complex results. Physicist Cosma Shalazi didn't like the book. Judging from the title of his review, "A Rare Blend of Monster Raving Egomania and Utter Batshit Insanity", he really didn't like the book. Here's what he has to say about Wolfram's grasp of biology:
Wolfram displays absolutely no understanding of evolution, or what would be necessary to explain the adaptation of organisms to their environments. This is related to his peculiar views on methodology. If you want to get a rough grasp of how the leopard might get its spots, then building a CA model (or something similar) can be very illuminating. It will not tell you whether that's actually how it works. This is an important example, because there is a classic theory of biological pattern formation, or morphogenesis, first formulated by Turing in the 1950s, which lends itself very easily to modeling in CAs, and with a little fine-tuning produces things which look like animal coats, butterfly wings, etc., etc. The problem is that there is absolutely no reason to think that's how those patterns actually form; no one has identified even a single pair of Turing morphogens, despite decades of searching. Indeed, the more the biologists unraveling the actual mechanisms of morphogenesis, the more complicated and inelegant (but reliable) it looks. If, however, you think you have explained why leopards are spotted after coming up with a toy model that produces spots, it will not occur to you to ask why leopards have spots but polar bears do not, which is to say that you will simply be blind to the whole problem of biological adaptation.
Come think of it, that's a criticism that applies to many evolutionary computer simulations as well (Avida, anyone?).
(HT: Stranger Fruit)