You seem to be possibly massively underestimating the computational level involved. The size of the human genome is a bad metric to use. In some respects it overestimates since a lot is junk DNA or DNA where changing the sequence slightly will not make a difference (since some amino acids are coded for by multiple codons). But overall, it is a massive underestimate of the computational power needed. The human brain is what needs to be modeled, and that’s a much more complicated object that arises from the interaction of the genome, the environment, and the initial conditions in the egg. Using an argument based on the genome size is at best naive.
Embryo’s environment is rather isolated from external influences, and AFAIK reproduction system development doesn’t depend on information collected from postnatal environment. Thus it must be egg initial conditions, which contain all additional information you speak of. However It is strange that evolution don’t use such massive amount of information for adaptations. Are you sure that Kolmogorov complexity of newborn child is much larger than that of genome?
Kolmogorov complexity might be low, but that’s only the amount needed to specify the entity. Predicting the behavior of the entity is a different question entirely. For example, the string given by f(n)= p(A(n,n)) mod 3 where A(m,k) is the Ackermann function and p(m) is the nth prime number has very low Kolmogorov complexity. (The Turing machine to do this is simple enough that one could if one wanted to write out all the states without much effort). But calculating this function beyond n=3 or so is not feasible. The issue is not just the total information but the degree of interaction. Your estimate ignores how much interaction there is between neurons. As with the example with the Ackermann function, the level of interaction may create something which is computationally intractable even if the specification length is very short.
There are other methods of estimating the computational level necessary to model the human brain that do try to calculate estimates based on neuronal interactions, and they are many orders of magnitude larger than your estimate. If computational power continues to increase exponentially this shouldn’t make that much of a difference (order of 15 to 30 years or so in terms of computational power), but it doesn’t stop your estimate from being far below the correct value.
You are right, I’ve underestimated processing power required (technically I estimated how many processing power can small group raise now, but implication was obvious). Brain’s PP have theoretical upper limit of 10^21- 10^22bit/s, estimated using Landauer’s principle. However there’s indication that brain cells operate orders of magnitude below theoretical limit, it doesn’t factor out other sources of computation power, but it somewhat mitigates my error.
You seem to be possibly massively underestimating the computational level involved. The size of the human genome is a bad metric to use. In some respects it overestimates since a lot is junk DNA or DNA where changing the sequence slightly will not make a difference (since some amino acids are coded for by multiple codons). But overall, it is a massive underestimate of the computational power needed. The human brain is what needs to be modeled, and that’s a much more complicated object that arises from the interaction of the genome, the environment, and the initial conditions in the egg. Using an argument based on the genome size is at best naive.
Embryo’s environment is rather isolated from external influences, and AFAIK reproduction system development doesn’t depend on information collected from postnatal environment. Thus it must be egg initial conditions, which contain all additional information you speak of. However It is strange that evolution don’t use such massive amount of information for adaptations. Are you sure that Kolmogorov complexity of newborn child is much larger than that of genome?
Edit: Spellcheck.
Kolmogorov complexity might be low, but that’s only the amount needed to specify the entity. Predicting the behavior of the entity is a different question entirely. For example, the string given by f(n)= p(A(n,n)) mod 3 where A(m,k) is the Ackermann function and p(m) is the nth prime number has very low Kolmogorov complexity. (The Turing machine to do this is simple enough that one could if one wanted to write out all the states without much effort). But calculating this function beyond n=3 or so is not feasible. The issue is not just the total information but the degree of interaction. Your estimate ignores how much interaction there is between neurons. As with the example with the Ackermann function, the level of interaction may create something which is computationally intractable even if the specification length is very short.
There are other methods of estimating the computational level necessary to model the human brain that do try to calculate estimates based on neuronal interactions, and they are many orders of magnitude larger than your estimate. If computational power continues to increase exponentially this shouldn’t make that much of a difference (order of 15 to 30 years or so in terms of computational power), but it doesn’t stop your estimate from being far below the correct value.
You are right, I’ve underestimated processing power required (technically I estimated how many processing power can small group raise now, but implication was obvious). Brain’s PP have theoretical upper limit of 10^21- 10^22bit/s, estimated using Landauer’s principle. However there’s indication that brain cells operate orders of magnitude below theoretical limit, it doesn’t factor out other sources of computation power, but it somewhat mitigates my error.