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How Even Random Numbers Show Evidence of Design

Random number generators are actually pseudo-random number generators because they depend on designed algorithms

In Define information before you talk about it, neurosurgeon Michael Egnor interviewed engineering prof Robert J. Marks on the way information, not matter, shapes our world (October 28, 2021). In the first portion, Egnor and Marks discussed questions like: Why do two identical snowflakes seem more meaningful than one snowflake. Then they turned to the relationship between information and creativity. Is creativity a function of more information? Or is there more to it? And human intervention make any difference?

Many questions arose during the discussion. Does Mount Rushmore have no more information than Mount Fuji? Does human intervention make a measurable difference? That’s specified complexity. Putting the idea of specified complexity to work, how do we measure meaningful information? How do we know Lincoln contained more information than his bust? On a hot button topic, they addressed the hope that advanced AI could somehow recognize and filter out bias and hate — the problem is that bias is innate in programming, according to the No Free Lunch Theorem. Dr. Marks then talks about his work in evolutionary computing, raising the question, can random processes produce meaningful information? Or is intelligence required?

Lastly, a question looms: Does even randomness require design?

This portion begins at 1:02:46 min. A partial transcript and notes, Show Notes, and Additional Resources follow.

Robert J. Marks: There are deterministic aspects of randomness. And this is a difficult concept to explain. But examples are obvious. If you flip a coin a million times, about 50% of the time, it will come up heads if it’s a fair coin. And that is a deterministic output of randomness.

So imagine setting up an evolutionary computing program where you have a specific outcome in mind and you performed this operation a million times. Well, it’s going to converge to that output, just like the coin flip converges to a 50% success rate. And putting together the stochastic framework in order for this to happen is what the people in evolutionary computing do.

Michael Egnor: This is an example of what Aristotle meant by saying that randomness or chance depends critically on purpose.

If I set out to design a random number generator, I would need to go to school for a decade, to learn computer engineering, to learn electronics, to learn all of that, to design a random number generator. So at the tail end of this thing random numbers would come out. But there’s nothing in the least bit random about the effort that it takes to reach that point. A random number generator is not itself a random thing. It’s a highly designed thing.

Robert J. Marks: Well, it’s a highly designed thing. And I would also argue that all random numbers generated by computers are themselves deterministic, believe it or not. In fact, they refer to them as pseudorandom number generators. There’s a little algorithm that spits out numbers that look random but underneath, they’re not random.

In fact, I have a student right now who is looking at training a neural network to forecast random numbers. If these random numbers are being generated by a deterministic algorithm, then we should be able to discover what the deterministic algorithm is.

Is there a way we can game that system and literally figure out the next random number? In fact, the only place in the world that randomness exists is in quantum collapse. That’s the only true randomness.

We talked about the critics living in silos. I read their works all the time. However, I don’t think the critics read our works. We talked about Dr. Shallit, for example:

See: Does Mt Rushmore contain no more information than Mt Fuji? That was University of Waterloo mathematician Jeffrey Shallit’s assertion.

Robert J. Marks: He had his head stuck in the silo of Shannon information theory, not understanding the context.

If he had gotten a copy of Introduction to Evolutionary Informatics or read some of the articles that we have generated, then he would understand what exactly was meant by this.

Robert J. Marks

I have also gotten into a challenge with a great gentleman, Randy Isaac, from the American Scientific Affiliation. I think he’s a former IBM guy with a physics background. And I couldn’t shake him off the idea that all information was not physical, he kept going back to the physical definition of information, and I couldn’t shake him out of that silo.

He said, well, Rolf Landauer, one of the great physicists, said that all information is physical. That’s one of the many definitions that we can have of information. So I wish before they made critical comments concerning our work, that they literally read it and became familiar with it, so that their comments would be useful.

By the way, I should mention that many times I have engaged with opponents of our work. And they have been right on at least two occasions I can think of, where they found a mistake in our reasoning. Now, it didn’t detract at all from the main thrust of our conclusions. But they were right. And if you look at our papers, at the end, acknowledge the people that did read our work and had critical, well-civilized interchanges with us. That back and forth is always important, as long as it’s done in a civilized way as opposed to a pugilistic way — which we see a lot of.

Michael Egnor: Well, one of the great tragedies — as I found when I began reading intelligent design literature — is that the questions that intelligent design scientists raise and the points they make are very profound, important points. And a great deal more progress could be made in biology and in intelligent design and evolution if the people are that Darwinian side, would simply engage with honesty, with integrity, with a genuine desire to learn. Because they have things they could teach us. These people are very smart but they come at intelligent design as an enemy, instead of as a tool for a better understanding evolution. And it’s a real shame.

Michael Egnor

Robert J. Marks: Exactly. I remember a film clip in Science Uprising of a guy that made a profound statement: Science and academia exist in these silos and have already decided on a materialistic, naturalistic framework. Our job is to fill in the details.

Walter Bradley made a great observation. He was being deposed by an ACLU attorney who asked if he was a Christian — trying to appeal to the genetic fallacy of discrediting him.

He says yes.

And they said, “How can you be objective and looking at these things and be a Christian at the same time?”

And Bradley’s comment was, I think, ingenious. He said, “Look,” he says, “I’m not the one of the silo; it’s you that are in the silo. I can accept naturalistic things happening. I see materialistic consequences all the time. I don’t prescribe to it as a philosophy. But I also have a broader perspective, because there’s many things that I can bring into conclusions that are outside of your narrow silo. It’s not me that’s in the narrow silo, it’s you.”

And the ACLU attorney immediately switched directions in his questions. It’s just a brilliant comeback. And I think it’s very apropos.

Phillip E. Johnson (1940–2019)

Michael Egnor: One of the first intelligent design theorists that I read was Phillip Johnson, who passed away recently. And I owe him a tremendous debt because he opened that insight to me… He said that Darwinism isn’t really much of a scientific theory. It’s a philosophy. It’s a metaphysical system. And it kind of dresses up as a scientific theory. But it really is a philosophical system for attempting to explain biology on purely materialistic terms. And we should address it that way. It’s a philosophical problem, it’s not a scientific problem.

Note: Phillip E. Johnson (1940–2019), a Berkeley law professor, wrote penetrating critiques of the effects of the assumption of naturalism (nature is all there is) in science. Scientists started out hoping naturalism would be an inference to the best explanation. But then, culturally, many drifted toward accepting any naturalist explanation as better than any other, despite deep flaws. This has been especially true in discussions of evolution:

“Philosophical naturalism is so deeply ingrained in the thinking of many educated people today, including theologians, that they find it difficult even to imagine any other way of looking at things. To such people, Darwinism seems so logically appealing that only a modest amount of confirming evidence is needed to prove the whole system, and so they point to the peppered-moth example as virtually conclusive. Even if they do develop doubts whether such modest forces can account for large-scale change, their naturalism is undisturbed. Since there is nothing outside of nature, and since something must have produced all the kinds of organisms that exist, a satisfactory naturalistic mechanism must be waiting to be discovered.” – “The Establishment of Naturalism

Robert J. Marks: Well, I don’t think anybody that has looked into the mathematics of Darwinism, as is covered in Introduction to Evolutionary Informatics can come to the conclusion that Darwinian evolution works without some sort of guidance. There needs to be lots and lots of active information in the process to make it work. It can’t be done just through a random process.


Here are all the episodes in the series. Browse and enjoy:

  1. How information becomes everything, including life. Without the information that holds us together, we would just be dust floating around the room. As computer engineer Robert J. Marks explains, our DNA is fundamentally digital, not analog, in how it keeps us being what we are.
  2. Does creativity just mean Bigger Data? Or something else? Michael Egnor and Robert J. Marks look at claims that artificial intelligence can somehow be taught to be creative. The problem with getting AI to understand causation, as opposed to correlation, has led to many spurious correlations in data driven papers.
  3. Does Mt Rushmore contain no more information than Mt Fuji? That is, does intelligent intervention increase information? Is that intervention detectable by science methods? With 2 DVDs of the same storage capacity — one random noise and the other a film (BraveHeart, for example), how do we detect a difference?
  4. How do we know Lincoln contained more information than his bust? Life forms strive to be more of what they are. Grains of sand don’t. You need more information to strive than to just exist. Even bacteria, not intelligent in the sense we usually think of, strive. Grains of sand, the same size as bacteria, don’t. Life entails much more information.
  5. Why AI can’t really filter out “hate news.” As Robert J. Marks explains, the No Free Lunch theorem establishes that computer programs without bias are like ice cubes without cold. Marks and Egnor review worrying developments from large data harvesting algorithms — unexplainable, unknowable, and unaccountable — with underestimated risks.
  6. Can wholly random processes produce information? Can information result, without intention, from a series of accidents? Some have tried it with computers… Dr. Marks: We could measure in bits the amount of information that the programmer put into a computer program to get a (random) search process to succeed.
  7. How even random numbers show evidence of design Random number generators are actually pseudo-random number generators because they depend on designed algorithms. The only true randomness, Robert J. Marks explains, is quantum collapse. Claims for randomness in, say, evolution don’t withstand information theory scrutiny.

Show Notes

  • 00:00:09 | Introducing Dr. Robert J. Marks
  • 00:01:02 | What is information?
  • 00:06:42 | Exact representations of data
  • 00:08:22 | A system with minimal information
  • 00:09:31 | Information in nature
  • 00:10:46 | Comparing biological information and information in non-living things
  • 00:11:32 | Creation of information
  • 00:12:53 | Will artificial intelligence ever be creative?
  • 00:17:40 | Correlation vs. causation
  • 00:24:22 | Mount Rushmore vs. Mount Fuji
  • 00:26:32 | Specified complexity
  • 00:29:49 | How does a statue of Abraham Lincoln differ from Abraham Lincoln himself?
  • 00:37:21 Achieving goals
  • 00:38:26 | Robots improving themselves
  • 00:43:13 | Bias and concealment in artificial intelligence
  • 00:44:42 | Mimetic contagion
  • 00:50:14 | Dangers of artificial intelligence
  • 00:54:01| The role of information in AI evolutionary computing
  • 01:00:15| The Dead Man Syndrome
  • 01:02:46 | Randomness requires information and intelligence
  • 01:08:58 | Scientific critics of Intelligent Design
  • 01:09:40 | The controversy between Darwinian theory and ID theory
  • 01:15:07 | The Anthropic Principle

Additional Resources


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How Even Random Numbers Show Evidence of Design