Bird Flight Automaton
Let’s just watch: Notice, just the flapping, using gears and links [far simpler than the real bird]: Amazing. END
Functionally Specified Complex Information and Organization
Let’s just watch: Notice, just the flapping, using gears and links [far simpler than the real bird]: Amazing. END
As fans of Dune will readily see, an ornithopter is a flapping wing flying machine, as a helicopter is a rotary wing flying machine. Where of course we have seen how Dragonflies are elliptical winged insects capable of up to 56 km/h speed. They also have pterostigmas that control flutter, gaining up to 25% speed [in gliding mode]. Then, there are airflow and wing flex sensors that indicate sophisticated, highly tuned control loop networks. These insects are capable of forward and reverse flight, hovering and sideways flight. They also have up to 97% success rate in predation. Such a natural model will of course inspire engineers. So, we can see here, a Dragonfly robot ornithopter: A clip: Resemblance to a Read More ›
We have been using a 3-D printer-constructor formalism, and now we can use it to see how hill climbing leads to local trapping. Again, the core formalism: Now, let us modify by allowing some sort of local random mutation to d(E) case by case within an n-run, now seen as a generation, so E1 to En are all incrementally different, and in effect are a ring around E in a fitness landscape. From this, we can see a survival filter that on average selects for superior performance. This leads, naturally to hill-climbing, perhaps even to several related peaks in a chain on an island of function. But now, we see: Here, we see that hill climbing leads to peak trapping, Read More ›
It seems that it is exceedingly hard for some to understand what FSCO/I is about. In responding to an objector, I wrote as follows just now, and think it is worth headlining for reference: Where, K-Complexity is summarised by Wikipedia, as a first level point of reference that would have been immediately accessible all along: <<In algorithmic information theory (a subfield of computer science and mathematics), the Kolmogorov complexity of an object, such as a piece of text, is the length of a shortest computer program (in a predetermined programming language) that produces the object as output. It is a measure of the computational resources needed to specify the object, and is also known as algorithmic complexity, Solomonoff–Kolmogorov–Chaitin complexity, program-size Read More ›
One of the more peculiar objections to the design inference is the strident, often repeated claim that the genetic code is not a code, and that DNA and mRNA are not storing algorithmic, coded information used in protein synthesis. These are tied to the string (yes, s-t-r-i-n-g) data structure, a key foundational array for information storage, transfer and application. So, it seems useful to address the string as a key first principles issue, with the onward point being that strings of course can and do store coded information. Let us begin with, what a string — yes, s-t-r-i-n-g — is (though that should already be obvious from even the headline): Geeks for Geeks: A string is a sequence of characters, Read More ›
Peter Robinson’s Uncommon Knowledge brings three authors together, Tom Holland, Stephen Meyer and Douglas Murray: A key consideration: vs, this notorious poetic assertion: Of course, both of these reflect the rise of the skeptical mindset among the educated elites, the modern inferior good that stands in for the cardinal virtue, prudence. So, we cannot escape the epistemic challenge, what it means to know and to what confidence, especially as regards roots of reality and our place in reality. (Where, trivially, for any reasonably definable field, X, the claim that one knows on some warrant that there is no objective, knowable truth regarding X, is instantly self-referentially incoherent and self defeating. As this hyperskeptical claim is about X and claims objective Read More ›