The difference in death rate between the USA and Japan from Alzheimer’s disease is most likely just due to coding differences (coding in the sense of imputation, not programming). Japan seem to be using the proximal cause more often. Switch to ‘Influenza and Pneumonia’ and you’ll see that the order is swapped. Pneumonia is a common immediate cause of death in Alzheimer’s.
There does seem to be a difference between the brain circuits in small animals versus large ones. The circuits in small animals are very highly integrated systems, where individual neurons often preform multiple roles. A human designer trying to do the same thing would have break everything up into independent functional modules and end up using 100 times as many neurons. There are limits however to what biological evolution can do. For larger brains repeating sub-circuit motifs are the norm, rather than hyper-integration.
I’ll copy here what I said elsewhere about biological versus artificial evolution:
“It is fortunate that biological systems are heavily quantized, especially in bacteria and viruses. An atom is there or not, discrete point mutations are there or not, a plasmid is there or not . If the cost landscape where not so heavily quantized we simply wouldn’t exist. The crossover mechanism higher animals use is a weak optimizer but it does make the cost landscape less rough than what asexual microbes have to contend with.
Hence we can adapt to pathogens despite having a far longer time between generations and a far lower population count. It is also true (I think) that having a larger genome reduces the roughness of the cost landscape by giving more degrees of freedom.
In a non-quantized artificial system a perturbation in any of the basis directions gives a smoothly changing alteration in cost. A mutation in all dimensions gives a new cost that is a summary measure of multiple clues. Following mutations downhill in cost means following multiple clues about which way to go. If there were quantization in many basis directions a small movement in those directions would give you not information about whether such a movement was good or bad. You would get not clues in those directions, less clues overall, which is obviously detrimental.
A point here being that artificial evolution on digital computers can be far more efficient than biological evolutions. If you accept that back propagation is in some sense a form of evolution (at a slight stretch) then you can see that a GPU cluster can build in a few weeks the capacity to do vision that took biological evolution many millions of years to create.
I have some kind of code here: https://github.com/S6Regen/Thunderbird ”
“Here’s a link for crossover being a weak optimizer: https://youtu.be/WoamKUfisVM
It is actually to allow non-lethal mixability of traits. And that ends up implementing the multiplicative weights update algorithm, or so they say.
You might ask then, why are fungi not more lethal pathogens given what I said and that they reproduce by crossover. I don’t really know but I presume it has to do with crossover being a weak optimizer and maybe they have a smaller number of genes than a large animal.
A neural network can have squashing activation functions or non-squashing ones.
What I noticed from my experiments with associative memory is that squashing type activation functions result in attractor states/error correction/(soft) quantization. That seems to be difficult for evolution to deal with, especially if you use hard binary threshold activation functions (the ultimate squashing function.)
One the other hand nets with non-squashing activation are very easy to evolve. And result in reasoning in sparse patterns. Of course, just because evolution favors non-squashing activation functions does not mean they are the best possible ones to use. It could be squashing ones are if you had a suitable algorithm.”
I should go and check fungi actually reproduce by crossover. It’s been a long time since I did basic biology!
It starts with two bogus entries for Luxemburg and Andorra.
Both are small countries with much lower tobacco excise taxes than their neighbors, so their numbers are grossly inflated by cross-border tobacco ‘tourism’, which is perfectly legal.
Luxemburg has an excise tax of 126.19€, France 216.61€. That’s about a 1.80€ saving per package of 20.
I think it’s justified to conclude that the Wikipedia table should be labeled “sales” and not “consumption”. I wonder what other factors skew this table.
The difference in death rate between the USA and Japan from Alzheimer’s disease is most likely just due to coding differences (coding in the sense of imputation, not programming). Japan seem to be using the proximal cause more often. Switch to ‘Influenza and Pneumonia’ and you’ll see that the order is swapped. Pneumonia is a common immediate cause of death in Alzheimer’s.
That was surprising, so I went to check:
Also, almost half never smoked or drank alcohol. So, it’s not like “smoking promotes long life”, as some might interpret your words 🙂
Prior to that, you have to control for genetic differences. How long do Japanese Americans live?
There does seem to be a difference between the brain circuits in small animals versus large ones. The circuits in small animals are very highly integrated systems, where individual neurons often preform multiple roles. A human designer trying to do the same thing would have break everything up into independent functional modules and end up using 100 times as many neurons. There are limits however to what biological evolution can do. For larger brains repeating sub-circuit motifs are the norm, rather than hyper-integration.
I’ll copy here what I said elsewhere about biological versus artificial evolution:
“It is fortunate that biological systems are heavily quantized, especially in bacteria and viruses. An atom is there or not, discrete point mutations are there or not, a plasmid is there or not . If the cost landscape where not so heavily quantized we simply wouldn’t exist. The crossover mechanism higher animals use is a weak optimizer but it does make the cost landscape less rough than what asexual microbes have to contend with.
Hence we can adapt to pathogens despite having a far longer time between generations and a far lower population count. It is also true (I think) that having a larger genome reduces the roughness of the cost landscape by giving more degrees of freedom.
In a non-quantized artificial system a perturbation in any of the basis directions gives a smoothly changing alteration in cost. A mutation in all dimensions gives a new cost that is a summary measure of multiple clues. Following mutations downhill in cost means following multiple clues about which way to go. If there were quantization in many basis directions a small movement in those directions would give you not information about whether such a movement was good or bad. You would get not clues in those directions, less clues overall, which is obviously detrimental.
A point here being that artificial evolution on digital computers can be far more efficient than biological evolutions. If you accept that back propagation is in some sense a form of evolution (at a slight stretch) then you can see that a GPU cluster can build in a few weeks the capacity to do vision that took biological evolution many millions of years to create.
I have some kind of code here:
https://github.com/S6Regen/Thunderbird ”
“Here’s a link for crossover being a weak optimizer:
https://youtu.be/WoamKUfisVM
It is actually to allow non-lethal mixability of traits. And that ends up implementing the multiplicative weights update algorithm, or so they say.
You might ask then, why are fungi not more lethal pathogens given what I said and that they reproduce by crossover. I don’t really know but I presume it has to do with crossover being a weak optimizer and maybe they have a smaller number of genes than a large animal.
A neural network can have squashing activation functions or non-squashing ones.
What I noticed from my experiments with associative memory is that squashing type activation functions result in attractor states/error correction/(soft) quantization. That seems to be difficult for evolution to deal with, especially if you use hard binary threshold activation functions (the ultimate squashing function.)
One the other hand nets with non-squashing activation are very easy to evolve. And result in reasoning in sparse patterns. Of course, just because evolution favors non-squashing activation functions does not mean they are the best possible ones to use. It could be squashing ones are if you had a suitable algorithm.”
I should go and check fungi actually reproduce by crossover. It’s been a long time since I did basic biology!
You reference this list:
https://en.m.wikipedia.org/wiki/List_of_countries_by_cigarette_consumption_per_capita
It starts with two bogus entries for Luxemburg and Andorra.
Both are small countries with much lower tobacco excise taxes than their neighbors, so their numbers are grossly inflated by cross-border tobacco ‘tourism’, which is perfectly legal.
Looking at the 2018 EU tobacco excise table:
https://ec.europa.eu/taxation_customs/sites/taxation/files/resources/documents/taxation/excise_duties/tobacco_products/rates/excise_duties-part_iii_tobacco_en.pdf
Luxemburg has an excise tax of 126.19€, France 216.61€. That’s about a 1.80€ saving per package of 20.
I think it’s justified to conclude that the Wikipedia table should be labeled “sales” and not “consumption”. I wonder what other factors skew this table.