![\"antenna.png\"](\"https:\/\/i0.wp.com\/scientopia.org\/img-archive\/goodmath\/img_338.png?resize=216%2C181\")
<\/p>\nIt’s always amusing to wander over to the Discovery Institute’s blogs, and see what kind of nonsense they’re spouting today. So, today, as I’m feeling like steamed crap, I took a wander over. And what did I find? High grade, low-content rubbish from my old buddy, Casey Luskin. Luskin is, supposedly, a lawyer. He’s not a scientist or a mathematician by any stretch of the imagination. There’s nothing wrong with that in the abstract; the amount of time we have to learn during our lives is finite, and no one can possible know everything. For example, I don’t know diddly-crap about law, American or otherwise; my knowledge of western history is mediocre at best; I don’t really speak any language other than english. I know some physics, but my understanding of anything beyond the basics is very limited. Even when it comes to the topic of this blog, math, I’m at best an enthusiastic amateur.<\/p>\n
<\/p>\n
The problem with Casey, and people like him, is that they’re ignorant of a topic where they believe<\/em> that they’re experts. Growing up, I was taught to call that kind of behavior not just In this instance<\/a>, Casey is annoyed because a group of people at NASA used evolutionary algorithms to create a better antenna.<\/p>\n <\/p>\n The fascinating thing about the antenna story is that That’s pretty damned impressive, and pretty difficult evidence to So what’s Casey’s problem with this?<\/p>\n The presumption of evolutionary biologists, of course, is that these “brilliant designs” evolved by natural selection preserving random, but beneficial mutations. Engineers operating under such presumptions have thus tried to mimic not only the “brilliant designs,” but also the evolutionary processes that allegedly produced the designs. Biologic’s article notes that one success story of such methods was the case of NASA engineers who used evolutionary computing to produce a better antenna.<\/p>\n Did they use truly Darwinian “evolutionary computing?” The article goes on to discuss how design parameters were smuggled into the simulation, such that it really wasn’t a truly unguided Darwinian evolutionary scenario.<\/p>\n So what exactly can unguided Darwinian evolutionary computing actually produce? Probably not very much, but this is a research question that Biologic is attempting to tackle. As their research page says, they are exploring “fundamental laws governing the origin of information” by “building and testing computational models that mimic the role of genetic information in specifying functions by means of structure-forming sequences.”<\/p>\n<\/blockquote>\n In essence, he claims that the antenna is really<\/em> designed, because the engineers “smuggled” information into the system, meaning that it’s not truly an “unguided Darwinian evolution scenario”.<\/p>\n First and most important, no one ever claimed that Second, nothing was smuggled<\/em> in to the system. As Casey himself points out, they’re very<\/em> open about the fact that they provided a lot of data. From their article:<\/a><\/p>\n How impressively it works, though, depends on what you were Equally sobering is the likelihood that this striking Two major limitations to evolutionary processes seem to assure this. As they said – to design an antenna using evolutionary techniques, you need to start with an understanding of exactly what you’re looking for, what range of space to try to cover with your mutations, That doesn’t distract from the amazing outcome. A system based Casey, as usual, is trying to play the intelligent design game of saying that evolution can’t produce information. From the standpoint A DNA molecule is an amazing thing. It’s a stable system for But back to Casey. The nub of his argument is that there’s nothing So no matter what kind of stupid arguments Casey wants to make about information, the final fact remains: that the shape of the optimal antenna was not<\/em> included in the input the the evolutionary simulation, but the simulation produced something superior to the best efforts of the intelligent, skilled engineers.<\/p>\n","protected":false},"excerpt":{"rendered":" It’s always amusing to wander over to the Discovery Institute’s blogs, and see what kind of nonsense they’re spouting today. So, today, as I’m feeling like steamed crap, I took a wander over. And what did I find? High grade, low-content rubbish from my old buddy, Casey Luskin. Luskin is, supposedly, a lawyer. He’s not […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[31],"tags":[],"class_list":["post-702","post","type-post","status-publish","format-standard","hentry","category-intelligent-design"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4lzZS-bk","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/702","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/comments?post=702"}],"version-history":[{"count":0,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/702\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/media?parent=702"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/categories?post=702"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/tags?post=702"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nignorant, but pig-ignorant. It’s a foolish kind of arrogance, where you believe that you know as much as people who’ve spent years studying something, even though you’ve never even read an elementary textbook. It’s like the dozens of people who’ve emailed my “disproofs” of Cantor’s theorem, when they don’t actually know what “cardinality” actually means<\/em>.<\/p>\n
\nno one had any idea of just what a “better antenna” would look like. In fact, they wound up with something that looks like a paper clip bent into triangles. Let me repeat the key<\/em> thing here: a bunch of
\nengineers wanted a better antenna. They had no idea<\/em> what that
\nbetter antenna would look like. But by throwing it into an evolutionary
\nalgorithm, they produced an antenna better than anything designed by a human being<\/em>. <\/p>\n
\nconfront for anyone who wants to claim that random mutation plus selection can’t produce anything new. Of course, that wouldn’t stop someone like Casey: he, in his masterful brilliance, knows more about this than even the people who did the experiment!<\/p>\n\n
\nevolutionary techniques like this are perfect simulations of biological Darwinian evolution<\/em>. Casey is, as usual, battling against a
\nstraw man. If you were to point out to the engineers involved that this simulation wasn’t a true simulation of biological evolution, their
\nresponse would be something along the lines of “Yeah, so?”<\/p>\n\n
\nexpecting. You can’t fault the NASA engineers for choosing the automated
\nevolutionary approach when you consider the alternative–a pair of
\nneedle-nose pliers, half a ton of paper clips, and a whole lot of wrist
\nstrain. But if you really saw evolutionary computing as a high-speed
\nversion of the process that produced all the jaw-dropping designs of
\nbiology, well… you ought to be more than a little disappointed.<\/p>\n
\ndisparity–between the stunning things attributed to evolution and the
\nmodest things we get by harnessing it–will persist.<\/p>\n
\nFirst, it turns out that if you want these processes to go anywhere, you
\nreally do need to master the design principles specific to your
\nobjective. You’d better believe the NASA team did their homework for the
\ntask they were tackling–they knew what materials to use, they knew the
\nrange of dimensions to explore, they knew what kind of geometric space
\nto explore, and they knew how to model the performance of any prototype
\nwithin those specifications. So the software they used was intelligently
\npre-configured for this particular design task and no other.<\/p>\n<\/blockquote>\n
\nhow to perform the mutations, how to evaluate different results
\nduring the selection phase, and so on.<\/p>\n
\non replication, mutation, and selection produced a better design<\/em> for the antenna than the best designed by intelligent human engineers. And none of the engineers could have predicted that outcome.<\/p>\n
\nof information theory, as I’ve pointed out time<\/a> and again<\/a>, that’s just pure rubbish. Random processes, by definition<\/em> produce huge quantities of information. In fact, the interesting thing about the
\nkinds of systems that make up living things isn’t how much<\/em> information they encode, but how little<\/em>.<\/p>\n
\nencoding a quantity of information that is essential for the function
\nof life as we understand it. But from an information-theory standpoint,
\nit’s really amazingly sparse. It’s a double-helix, where each half of the helix contains exactly the same information as the other half. It’s got a substantial backbone, which is copied over and over and over down the chain. It’s really highly<\/em> compressible.<\/p>\n
\ninteresting about the production of this new antenna, because the “information” needed to produce it was “smuggled” in to the simulation. But if that’s really<\/em> the case, then the question is, why did the engineers bother with an evolutionary process? Why didn’t they just
\nuse their information to figure out what the optimal antenna geometry was? Engineers are known for being very down to earth, practical, results-oriented people. If they could produce the optimal solution
\nby themselves, they would. But the fact of the matter is, they couldn’t. They didn’t<\/em> have the information that they needed to figure out
\nwhat the optimal antenna could look like.<\/p>\n