Psalm 139:14 NLT
"Dembski opens with a simple illustration: someone searches for a hidden Easter egg, guided by clues like “warmer” or “colder.”
"Dembski opens with a simple illustration: someone searches for a hidden Easter egg, guided by clues like “warmer” or “colder.”
These clues help, but Dembski asks: Where did the guide get the information? Finding the right instructions is just as hard — sometimes harder — than the original search.
This idea is called the conservation of information: you can’tmagically reduce the difficulty of a problem by adding guidance, because the guidance itself must come from somewhere. In mathematical terms, you never get something from nothing. Any system that solves a complex problem must contain, or have access.
This idea is called the conservation of information: you can’tmagically reduce the difficulty of a problem by adding guidance, because the guidance itself must come from somewhere. In mathematical terms, you never get something from nothing. Any system that solves a complex problem must contain, or have access.
Dembski uses this principle to challenge Darwinian evolution. Conventional claims about evolution, he argues, assume that natural processes can produce complex life from simple beginnings without an intelligent source. But this assumption violates conservation of information, because the complexity requires prior information.
He critiques a common description of evolution, the familiar claim that monkeys at typewriters could eventually accidentally produce the works of Shakespeare by chance — with the help of a correcting agent (a lab technician who uses White-Out).ess to, the information needed to solve it.
That lab tech, Dembski says, already knows the target outcome and introduces outside knowledge. Without that external input, random mutation and natural selection alone are insufficient to explain biological complexity.
Dembski is skeptical of AGI — the idea that machines will someday think and reason like humans. He argues that it is a myth. While machines can perform specific tasks using massive amounts of data, humans achieve much more with much less.
For example, Tesla’s AI uses billions of video frames to learn driving, but people learn to drive with far less input. This shows human intelligence is fundamentally different, and probably unmatchable by machines."
MindMatters