We can do the same with living organisms. The human genome contains about 6.2 billion nucleotides. Since there are 4 nucleotides (A, T, G, C), we need two bits for each of them, and since there are 8 bits in a byte, that gives us around 1.55 GB of data.
In other words, all the information that controls the shape of your face, your bones, your organs and every single enzyme inside them – all of that takes less storage space than Microsoft Word™.
There are two ways to see this is incorrect.
DNA’s ability to structure an organism is mediated through its chemical mileau. It is densely, dynamically regulated through a complex and dense mesh of proteins and regulatory RNA and small signaling molecules at every timepoint in every organism throughout the life cycle. Disruption of that chemical mileau renders the organism nonviable. This is a separate issue from the fact that evolution overwhelmingly operates on DNA sequence.
The DNA in a particular organism/cell is one point in a very long series of complex inheretance chains going back 4.5 billion years. I’m comfortable rounding off the maximum complexity of the soma to the maximum possible complexity of the complete set of ancestral DNA sequences. But we can go further by noticing that an individual’s DNA sequence is not just the combination of their direct ancestors—the entire ancestral lineage at every step is sampled from a distribution of possible genomes that is produced from mechanisms impacting reproduction.
In a more mathematical sense, while it’s true that, conditional on a specific non-stochastic function, the number of values in the output set is less than or equal to the number of values in the input set, if the function can vary freely then there is no such constraint.
The soma might be viewed as a stochastic function mapping DNA inputs to phenotypic outputs. The stochastic aspect gives a much larger number of theoretically possible outputs from the same input set. And the fact that the ‘function’ (soma) itself varies from organism to organism increases the number of phenotypes that can be generated from a given amount of DNA still further.
All these arguments also apply to technology. MS Word ‘co-evolved’ with Windows, with programming languages, with hardware, and this context must be taken into account when thinking about how complex a machine is.
The DNA in a particular organism/cell is one point in a very long series of complex inheretance chains going back 4.5 billion years. I’m comfortable rounding off the maximum complexity of the soma to the maximum possible complexity of the complete set of ancestral DNA sequences. But we can go further by noticing that an individual’s DNA sequence is not just the combination of their direct ancestors—the entire ancestral lineage at every step is sampled from a distribution of possible genomes that is produced from mechanisms impacting reproduction.
To be fair here, the learning process, if it exists is really slow, such that we can mostly ignore this factor, and the ability of learning ancestral knowledge that was distilled by people before you is probably a big reason why humanity catapulted into the stratosphere.
(The other is human bodies are well optimized for making good use of tools, at least relative to other animal genomes that exist).
There are two ways to see this is incorrect.
DNA’s ability to structure an organism is mediated through its chemical mileau. It is densely, dynamically regulated through a complex and dense mesh of proteins and regulatory RNA and small signaling molecules at every timepoint in every organism throughout the life cycle. Disruption of that chemical mileau renders the organism nonviable. This is a separate issue from the fact that evolution overwhelmingly operates on DNA sequence.
The DNA in a particular organism/cell is one point in a very long series of complex inheretance chains going back 4.5 billion years. I’m comfortable rounding off the maximum complexity of the soma to the maximum possible complexity of the complete set of ancestral DNA sequences. But we can go further by noticing that an individual’s DNA sequence is not just the combination of their direct ancestors—the entire ancestral lineage at every step is sampled from a distribution of possible genomes that is produced from mechanisms impacting reproduction.
In a more mathematical sense, while it’s true that, conditional on a specific non-stochastic function, the number of values in the output set is less than or equal to the number of values in the input set, if the function can vary freely then there is no such constraint.
The soma might be viewed as a stochastic function mapping DNA inputs to phenotypic outputs. The stochastic aspect gives a much larger number of theoretically possible outputs from the same input set. And the fact that the ‘function’ (soma) itself varies from organism to organism increases the number of phenotypes that can be generated from a given amount of DNA still further.
All these arguments also apply to technology. MS Word ‘co-evolved’ with Windows, with programming languages, with hardware, and this context must be taken into account when thinking about how complex a machine is.
To be fair here, the learning process, if it exists is really slow, such that we can mostly ignore this factor, and the ability of learning ancestral knowledge that was distilled by people before you is probably a big reason why humanity catapulted into the stratosphere.
(The other is human bodies are well optimized for making good use of tools, at least relative to other animal genomes that exist).