Information makes a huge difference to what happens among human beings. But it is not like matter or energy. It doesn’t weigh anything or generate heat. How can we understand it scientifically?
A partial transcript follows. This portion begins at 01:10. Show notes and links follow.
Robert J. Marks: Well, my background is not in biology, but it is in computer science and computer engineering. And one of the things we do is do artificial intelligence. And I think maybe your question translated to artificial intelligence is, can anything happen in artificial intelligence from totally random unguided mutations and processes to allow for something to happen? And the answer is absolutely not. Winston Ewert, design theorist William Dembski, and I did a lot of work on analyzing programs that were purported to generate information.
Note: The background is that the advent of the computer suggested to some people that it would be possible to show that all the information in life forms is easily generated, via Darwinian evolution.
Robert J. Marks: And so people tried that, and there were people jumping up and down and saying, “Ah, yes, we have proven Darwinian evolution.” There was a problem though with their simulations. Number one is that all of the simulations were guided to be successful. And they were random. They were stochastic. You had the three steps of evolution. You had the random mutation. You had the killing off of the weak and the survival of the fittest, and then you had the re-population. The key in those three steps is survival of the fittest. How do you determine what the survival of the fittest is? In order to do that, you have to have something called a fitness function or an objective function. That needs to be imposed by the programmer. The programmer is telling you how the organism can better itself, and that is necessary in order to perform evolution on the computer.
Note: In short, the computer wasn’t doing it; the programmer was. The book that resulted is Introduction to Evolutionary Informatics
Robert J. Marks: The mathematics is based on something which is called the No Free Lunch theorem, which was popularized in the IEEE Transactions on Evolutionary Computing in 1997, where Wolpert and Macready showed that if you have no idea about the direction that you’re going, you’re never going to get there.
… We showed that not only was this true, but we could measure the degree to which people infused information into the search process.
Note: We are being asked to believe that blind chance can produce everything from elephants to engineers.
Michael Egnor: How could experts like these Darwinists not see that? I mean, that’s really blind. That’s quite amazing.
Robert J. Marks: I have this old theory of the difference between scientists and engineers. I’m an engineer. A scientist often come up with good theories and they like these theories, and they’re vetted and they’re placed up on a throne, and they’re kind of worshiped like a queen and protected like a queen. Whereas engineers make the queen come down from the throne and scrub the floor. And if she doesn’t scrub the floor, we fire her. And I think that that’s probably the case here. Now, why do these people do this? I heard an old story… And this is well circulated so people might’ve heard about this before, but I call it the dead man syndrome. And it illustrates the challenges of being in a silo of belief, a silo of ideology that you can’t see out of. The story of the dead man syndrome is that a man enters a psychiatrist office and says, “Doc,”… He was really sad by the way. He says, “Doc, I’m dead.”
And he started sobbing. He went over and sat down and put his head down and started to cry. And the psychiatrist was just astonished. He said, “Well, come on. You’re not dead. You’re walking, you’re talking, and dead people don’t do that.” The guy says, “Yeah, I know, it’s astonishing, isn’t it? That I can walk and talk, but doc I’m dead.” So the psychiatrist thought of a way that he could make an explanation to the man and convince the man that he wasn’t dead.
So he asked the guy, “Do dead men bleed?” And the patient said, “Why, no, dead men don’t bleed.” He said, “Here, give me your finger.” And so he picked his finger and the guy started to bleed. A little puddle of red blood came up and the guy’s eyes got big. And he looked at the doctor and looked at the puddle, and looked at the doctor and he said, “Doc, this is incredible. You’re right and I’m wrong. Dead men do bleed.”
So the point of that story is if you’re so ensconced in an ideology, that you are going to be pounding square pegs into round holes in order to defend that silo of ideology. And I think we point a finger at a Darwinist for doing that. But I think everybody has to be concerned about placing themselves in a silo of belief, and allow themselves open to other explanations, and go where the evidence leads us. I mean, this is what the scientists say, right? Go where the evidence leads us. And the evidence in terms of Darwinian evolution, especially as implemented and simulated by a computer, is that no, it simply doesn’t work. Not unless it’s guided.
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- 00:41 | Introducing Dr. Robert J. Marks
- 01:10 | The role of information in AI evolutionary computing
- 06:41 | The Dead Man Syndrome
- 11:02 | Randomness requires information and intelligence
- 14:50 | Scientific critics of Intelligent Design
- 19:28 | The controversy between Darwinian theory and ID theory
- 22:29 | The Anthropic Principle
- Robert J. Marks at Discovery.org
- Chapter 7 of: R.J. Marks II, W.A. Dembski, W. Ewert, Introduction to Evolutionary Informatics, (World Scientific, Singapore, 2017).
- Winston Ewert, William A. Dembski and Robert J. Marks II “Algorithmic Specified Complexity in the Game of Life,” IEEE Transactions on Systems, Man and Cybernetics: Systems, Volume 45, Issue 4, April 2015, pp. 584-594.
- Winston Ewert, William A. Dembski and Robert J. Marks II “On the Improbability of Algorithmically Specified Complexity,” Proceedings of the 2013 IEEE 45th Southeastern Symposium on Systems Theory (SSST), March 11, 2013, pp. 68-70
- Winston Ewert, William A. Dembski, Robert J. Marks II “Measuring meaningful information in images: algorithmic specified complexity,” IET Computer Vision, 2015, Vol. 9, #6, pp. 884-894