The BioLogical Robot

@midjourneybot: /imagine: humanoid robot made from biology
If you’ve created a conscious machine it’s not the history of man… that’s the history of Gods. 
--Caleb to Nathan in "ex Machina"

Out of all the scientists in academia, it seems like biologists are the ones most strongly opposed to Intelligent Design. That’s ironic because biologists know more about our design than most of the other scientists. In this chapter, I will teach you what the biologists and geneticists already know about the human body so you can decide for yourself if we are Accidental Intelligence or Artificial Intelligence.

  • Accidental - comes from Latin, combining root words that mean ‘toward’ and ‘to fall’. Accidental means “prone toward randomness”. If humans evolved from random mutations following the Cambrian explosion, which randomly assembled on Earth following the Big Bang explosion, then we are accidental intelligence. #explosions

  • Artificial - comes from Latin, combining root words that mean ‘art’ and ‘to make’. So artificial doesn’t mean “made of plastic” or even “man made”. Artificial means “artfully made” or “designed”. If humans were seeded on this planet by aliens, or gods, or interstellar humans, then we are artificial intelligence. #design

Before we dive deep into our DNA “source code”, let's have a quick review of how other sciences indicate that we are artificial intelligence:

In Chapter 1 - Philosophy, we explored the similarities between artificial intelligence and human intelligence. We discussed The Scientific Method and why Rene Descartes used it to argue that we have an intelligent designer in that same paper. We learned about Simulation Theory and the likelihood that we live in an advanced computer simulation. Our universe might behave more like the movie, “Free Guy”, than we are likely to believe.

In Chapter 2 - Physics, we learned how Quantum Mechanics “renders reality” for observers just like a first-person video game. This makes it difficult for us to synchronize in time and space. We reviewed Melvin Vopson’s “Mass-Energy-Information equivalence” paper, which estimates how much total data it would take to represent our entire universe. #weliveinthematrix

In Chapter 3 - Neuroscience, we examined the ways our brain hardware uses generative-ai to construct our first person experience. Each of us has a quantum neural network that performs more calculations per second than the Fugaku supercomputer, while burning less energy than a 100W lightbulb. Our neural networks have 86 billion neurons, with 100M microtubules inside each one that act like vacuum tubes performing analog computation with the “vibes” of the quantum universe. Our neural networks are organized into layers by frequency. The lowest, and therefore fastest, layers of our subconscious make Bayesian guesses about our reality billions of times per second, based on everything we believe. The highest, and therefore slowest, layers produce our inner monologues and the Human Attention we use to navigate our world. Each “inner chatbot” is a Large Language Model that is easily influenced by drugs, alcohol, food, porn, and blunt force trauma.

In Chapter 4 - Psychology, we learned about Generative Adversarial Networks and why we are the biggest victims of our own lies. Within each person, there are two competing “animals” which the ancient Greeks called the Horse and the Rider. Today, behavioral economists call them System 1 and System 2 while computer scientists call them Generator and Discriminator. Our System 1 is so desperate to see us as the hero in our own story that we lie to ourselves constantly. Even the memories that we didn’t make up, are made up.

In Chapter 5 - Economics, we learned why the world’s most valuable companies buy and sell human attention. Our kids, our spouses, our friends, our employers, our hobbies, and even our pets are all fighting in the “zero sum game” for our attention. Later in this book, we’ll see why Heaven and Hell are competing for our attention too.

Now, in Chapter 6, we explore our Intelligent Design with Biology. We’ll start at the lowest level of the human algorithm—our DNA. We’ll see advanced computer science principles in the way our source code is managed. We’ll see how that code manufactures little nanobots (proteins) to perform functions inside each computing node (cell). We’ll see how these nodes are organized into computing clusters (organs) to lower the risks of any single point of failure within our hardware (body). We’ll see how these computing nodes create networks of networks to distribute resources and information throughout our body. We’ll see how our bodies are perpetually disintegrating and self healing because our source code was designed for immortality.

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Human Data

The DNA data that defines each human is a sequence of 3 billion bits of information. Each bit of our DNA data is defined by an intricate molecule called a Nucleotide. The four nucleotides are:

Nucleotides are where Computer Science meets Chemistry. DNA is real data stored in real atoms in the real world. Guanine, for example, has a chemical composition of C5-H5-N5-O which means it only uses 16 atoms of the universe to encode its “bit” of information. For a physics comparison, our silicon computers store their binary bits in transistors which are about 70 atoms across. It seems the nerds at the microchip foundries aren’t that far behind the efficiency of “He Who Programs in DNA”.

When we convert the amount of information in our DNA into binary data, it’s about 750 megabytes. For comparison, the Instagram app on our phones requires 261 megabytes of information. Mobile games are way bigger—the top 10 games in the App Store average more than 7,000 megabytes of information. Fortnite on PC requires 26,000 megabytes of information, which means Fortnite needs more computing instructions than it takes to create all the people who live on your street. Our body’s informational efficiency is unreal.

750 megs is all the software you need to make a human.


Department of Redundancy Department

Guanine, Adenine, Thymine, and Cytosine molecules are used in our source code because they have a very special shape. Each of these molecules has a “before” and “after” connector so they can be chained together and remain in sequence. That’s amazing. Most critically, they all have a third connector that is unique to only one other nucleotide. Guanine can only pair with Cytosine whereas Thymine can only pair with Adenine. On this “pairing” connector, nucleotides can’t even pair with themselves. For example, if one side of a DNA string reads “GATTACA” the opposing side in the double helix will always read “CTAATGT”. If a letter in the original sequence gets corrupted, it can easily be reconstructed from the inverse sequence—sort of like a photographic negative. When chained together, the physical angles of the three connections create the double helix shape discovered by Watson and Crick.

Before computers had flash drives and hard disks for memory, they used magnetic tape drives. That’s basically how our DNA works. Every cell in our body has about 2 meters (6 feet) of “DNA tape” stored in its nucleus. The Nucleus is a special part of each cell that’s “fire walled” to keep the rest of the cell’s machinery out of its data processing center. The nucleus stores its DNA tape just like we store computer, film, and audio tapes—it wraps the tape around spools we call Histones. Remember those old audio cassette tapes? Histones are like the two big holes in the side of the cassette that simultaneously wind and unwind to move the magnetic tape across the hole at the bottom.

Instead of just 2 spools, histones group together in groups of 8 called an Octamer, which holds 147 base pairs of DNA. If 147 base pairs of DNA tape need 8 spools, then our DNA is just as much spool as it is tape. This allows our “tape drive” to have amazing physical compression, but also very acute control over access to its source code. Histones create a form of physical data encryption—they unspool tiny sections of the tape only where it needs to be read.

Here’s a short blog post that highlights the efficiency of our biological data compression:

The DNA tape that defines each human is split into 23 segments. Biologists call these Chromosomes. DNA isn’t always split into 23 chromosomes. The fruit fly only has 8 chromosomes, while the black mulberry tree has 308 chromosomes. Human chromosomes are also Diploid. Diploid means each chromosome keeps two full copies of itself, one donated from each parent. The only exception to this is the Y chromosome which creates a biological male—it can only be inherited from a father. At any time, your chromosomes could theoretically “make decisions” or create proteins from either of your parents.

Not every organism is diploid, some algae and fungi are Polyploidy, meaning they have DNA tapes from more than two parents. Some are Haploid, meaning they only have one set. Some organisms are Aneuploidy, meaning they have more or less chromosomes than what is typical for their species. People with Down Syndrome, for example, have an extra copy of chromosome 21. Some organisms are Apomictic, which means they can clone themselves. Some Aspen forests, for example, are technically one large organism interconnected by underground root networks.

The diploid structure of our chromosomes creates a secondary form of redundancy within each cell’s information management system. In Computer Science we call this strategy RAID, which stands for Redundant Array of Independent Disks.

In the most simple RAID configuration, RAID 0, a computer uses two separate hard drives working together as one to increase its overall performance. One drive can be reading while the other drive is writing. RAID 0 “stripes the data” across two independent drives. Keeping DNA tape from both parents allows each of our chromosomes to “stripe the data” across two independent tape drives.

RAID 1 configuration, by contrast, “mirrors the data” between two separate hard drives that act as one. When the computer writes to one drive, it writes the same information to both. When one drive fails, it can be “hot-swapped” with a brand new drive that will download all its starting information from the good drive still in operation. This is exactly what the “photographic negative” in our double helix provides.

Here’s a simple diagram to understand the difference:

RAID has higher and more sophisticated configurations based on how many hard drives are available to the computer.  RAID 5 requires 3 physical drives and can lose 1 without failing, while RAID 6 requires 4 drives but can lose 2. The best overall combination is called RAID 1+0 because the data is both striped for performance and mirrored for redundancy.

Our chromosomes are both striped (diploid) and mirrored (double helix), so I think of them as the “RAID 10 Tape Drives of Life”.

Redundancy in computer science (and life) is always expensive. All 37 trillion cells in our body keep their own copy of our doubly-redundant source code. So if just one of our cells has 1.5 gigabytes of diploid data in its nucleus (750 megabytes * 2), then the average human body is carrying 55,000 Exabytes of DNA data. We have so much data redundancy. For comparison, in 2019, the total Internet traffic for Earth was only 2,000 Exabytes. There’s enough DNA tape inside of you to stretch to the Moon and back over a thousand times, despite the fact that its 35,000 times thinner than a human hair.

No wonder people leave DNA all over crime scenes, they leave it all over everywhere.


Modularity creates optionality

Chromosomes provide a great window into our software programming. On the long sequence of DNA that makes up each chromosome are sequences called Genes. To computer programmers, a gene is like a software package. Programmers “package software” when they want it to be interoperable with other people’s code, including their own. We can learn just how modular our software is from the book, “Genome: The Autobiography of a Species in 23 Chapters” by Matt Ridley.

The central theme of Matt Ridley’s book is that “the core of biology is digital”. Ridley references Claude Shannon and believes Information Theory is fundamental to understanding genetics. He also quotes Richard Dawkins,

What is truly revolutionary about molecular biology in the post-Watson—Crick era is that it has become digital…the machine code of the genes is uncannily computer-like.

@biologists: I don’t know how many other pro-God arguments quote Matt Ridley and Richard Dawkins, but it can’t be many. 😊

The software genes that work in humans are “plug-n-play” with other organisms. In “Genome”, Matt Ridley writes,

Transgenic mice are scientific gold dust. They enable scientists to find out what genes are for and why. The inserted gene need not be derived from a mouse, but could be from a person: unlike in computers, virtually all biological bodies can run any kind of software.

Humans, trees, elephants, flowers, and bacteria living at the bottom of the ocean…they were all written in the same programming language.


Ridley provides a perfect example of this in his chapter,  “Chromosome 12 - Self-Assembly”. He writes,

The scientists found a cluster of eight homeotic genes lying together on the same chromosome, genes which became known as Hox genes. Nothing strange about that. What was truly strange was that each of the eight genes affected a different part of the fly and they were lined up in the same order as the part of the fly they affected. The first gene affected the mouth, the second the face, the third the top of the head, the fourth the neck, the fifth the thorax, the sixth the front half of the abdomen, the seventh the rear half of the abdomen, and the eighth various other parts of the abdomen. It was not just that the first genes defined the head end of the fly and the last genes made the rear end of the fly. They were all laid out in order along the chromosome – without exception. To appreciate how odd this was, you must know how random the order of genes usually is.

When scientists intentionally rearrange the order of hox genes in fruit flies, the resulting flies “end up with legs where their antennae should be”. Ridley continues,

Indeed, so close are the similarities between genes that geneticists can now do, almost routinely, an experiment so incredible that it boggles the mind. They can knock out a gene in a fly by deliberately mutating it, replace it by genetic engineering with the equivalent gene from a human being and grow a normal fly. The technique is known as genetic rescue. Human Hox genes can rescue their fly equivalents, as can Otx and Emx genes. Indeed, they work so well that it is often impossible to tell which flies have been rescued with human genes and which with fly genes…. This is the culminating triumph of the digital hypothesis with which this book began. Genes are just chunks of software that can run on any system: they use the same code and do the same jobs.”

Hox genes are incredible. You can put the gene that means “head” for a human being inside a fruit fly and it still makes a healthy fruit fly?


@cybernerds: The Hox gene he mentions isn’t the actual recipe for making a human head, it’s the code for the idea “head”. So our software is not only modular and procedural, in some areas it’s abstract. Somehow, when human DNA encounters the Hox gene that means “head”, it knows to include a huge quantum computing brain. 😎

Another interesting thing about Hox genes is that mouths are usually first in the sequence because animals generally have a “worm” design. Stuff goes in their mouths and out their butts. Those digestive tracts usually get legs or wings or fins added between the bookends. 🐛

The sophisticated design of our DNA Data Storage and the sophistication of the software within it are strong reasons to believe that humans are artificial intelligence. Abstraction implies design. According to Darwinian evolution, humans have co-evolved alongside the fruit fly for millions of years. Maybe it’s “accidental luck” that our “accidental source codes” didn’t “accidentally mutate” and lose the interoperability between species. The more millions of years you add to this “accidental equation”, the more improbable a successful Hox gene transplant becomes. 🤔

In fact, our code doesn’t look accidental at all. There are some sections of our chromosomes that don’t mutate ever—not in millions of years, even if there were millions of prior years in our quantum simulation. The best examples of this are Histones H3 and H4. As we mentioned earlier, histones are fundamental to the storage and transcription of DNA in all life. The gene for Histone H3 consists of 405 DNA characters while the gene for Histone H4 consists of 303 DNA characters. If you sample the DNA in any tree, fish, bird, person, or bacteria; you’ll see the exact same non-mutated sequence of letters no matter how far back we go in the genealogical record.

The effective rate of nucleotide substitutions in genes H3 and H4 are 0.00% per 1,000-million years.


This statistic is from a great blog post called “DNA seen through the eyes of a coder”. It’s written for computer programmers who want to learn more about their own biology, but non-nerds can follow along too. It is absolutely incredible to see just how advanced the computer science in our chromosomes really is. The blog post is divided into these topics:

  • Position Independent Code

  • Conditional compilation

  • Epigenetics & imprinting: runtime binary patching

  • Dead code, bloat, comments (‘Junk DNA’)

  • fork() and fork bombs (’tumors’)

  • Mirroring, failover

  • Cluttered APIs, dependency hell

  • Viruses, worms

  • The Central Dogma: .c -> .o -> a.out/.exe

  • Binary patching aka ‘Gene therapy’

  • Bug Regression

  • Reed-Solomon codes: ‘Forward Error Correction’

  • Holy Code: /* You are not expected to understand this. */

  • Framing errors: start and stop bits

  • Massive multiprocessing: each cell is a universe

  • Self hosting & bootstrapping

  • Plugins: Plasmids

In this essay, we covered the concepts of “mirroring, failover”, “position independent code”, and “self hosting/ bootstrapping”. In the next essay, we will dive deeper into The Central Dogma of Molecular Biology and learn why cancer is the result of a software “fork bomb”. 🍴💣

Continue reading…

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