# Estimating the Complexity of Innate Knowledge

The following is a very crude estimate of the informational complexity of the innate knowledge of human beings. To be more exact, it is a crude estimate of an upper limit to the information content of this knowledge. It might be off by an order of magnitude or so. So this is a “back of an envelope” or “back of a napkin” kind of calculation. It just gives a direction into which to try to get a more accurate calculation.

According to the human proteome  project (http://www.proteinatlas.org/humanproteome/brain), about 67 % of the human genes are expressed in the brain. Most of these genes are also expressed in other parts of the body, so they probably form part of the general biochemical machinery of all cells. However, 1223 genes have an elevated level of expression in the brain. In one way or the other, the brain-specific structures must be encoded in these genes, or mainly in these genes.

There are about 20.000 genes in the human genome. So the 1223 genes. So about 6.115 % of our genes are brain specific. Probably, we share many of these with primates and other animals, like rodents, so the really human-specific part of the brain-specific genes might be much smaller. However, I am only interested here in an order-of-magnitude-result for an upper limit.

I have no information about the total length of these brain-specific genes, so I can only assume that they have average length.

According to https://en.wikipedia.org/wiki/Human_genome, the human genome has  3,095,693,981 base paris (of course, there is variation here).  Only about 2 % of this is coding DNA. There is also some non-coding DNA that has a function (in regulation, or in production of some types of RNA) but let us assume that the functional part of the genome is maybe 3%. That makes something in the order of 92 – 93 million base pares with a function (probably less). That makes 30 million to 31 million triplets. If the brain genes have average length, 6.115 % of this would be brain specific. That makes that is something like 1.89 million triplets.

The triplets code for 20 amino acids. There are also start- and stop-signals. The exact information content of a triplet would depend on how often it appears, and they are definitely not equally distributed, but let us assume that each of them codes for one out of 20 possibilities (calculating the exact information content of a triplet will require much more sophisticated reasoning and specific information, but for our purposes, this is enough). The information content of a triplet can then be estimated as the dual logarithm of 20 (you need 4 bits to encode 16 possibilities and 5 bits to encode 32 possibilites, so this should be between 4 and 5 bits). The dual logarithm of 20 is 4.322. So we multiply this with the number of triplets and get  8.200.549 bits. This is  1.025.069 bytes, or roughly a megabyte (something like 200 – 300 times the length of this blog article).

So the information content of the brain coding genes that determine the structure of the brain is in the order of a megabyte (smaller than many pieces of commercial software). The structure of the brain is generated by the information contained in these genes. This is probably an overestimate because many of these genes might not be involved in the encoding of the connective pattern of the neurons, but, for example, in the glial immune system of the brain or other brain specific, “non-neuronal” stuff.

If the brain’s structure is encoded in these genes, the information content of these structures cannot be larger than the information content of these genes. Since there are many more neurons, a lot of their connectivity must be repetitive. Indeed, the cortex consists of neuronal columns that show a lot of repetitive structure. If one would describe the innate brain circuitry, i.e. that found in a newborn (or developing in the small child in processes of ripening), and you compress that information to the smallest possible size, determining its information content, that information content cannot be larger than the information content of the genes involved in its generation. The process of transcribing those genes and building the brain structures as a result can be viewed as a process of informtion transformation, but it cannot create new information not contained in those genes. The brain structure might contain random elements (i.e. new information created by random processes) and information taken up from the environment through processes of perception, experimentation and learning, but this additional information is, by definition, not part of the innate structures. So the complexity of the innate structures or the innate knowledge, i.e. the complexity of the innate developmental core of cognition, must be limited by the information content of the genes involved in generating the brain.

The above calculation shows that this should be in the order of magnitude of a megabyte or so.

This means also that the minimum complexity of an artificial intelligent system capable of reaching human-type general intelligence cannot be larger than that.

We should note, however, that human beings who learn and develop their intelligence are embedded in a world they can interact with through their bodies and senses and that they are embedded into societies. These societies are the carriers of cultures whose information content is larger by many orders of magnitude. The question would be if it is possible to embed an artificial intelligent system into a world and a culture or society in a way to enable it to reach human-like intelligence. (This also raises ethical questions.)

In other words, the total complexity of innate knowledge of humans can hardly extend the amount of information contained in an average book, and is probably much smaller. It cannot be very sophisticated or complex.

(The picture, displaying the genetic code, is from https://commons.wikimedia.org/wiki/File:GeneticCode21-version-2.svg)