I wonder if anybody has graphed how long and how many genetic mutations have taken place between the miasma of life and 500,000 years ago.
It seems that tracking the genetic mutations would provide an approximation of the computing complexity needed. One could also look at the death rate as error rate for the success of computations.
> It seems that tracking the genetic mutations would provide an approximation of the computing complexity needed.
How are those two even approximately related?
1. If you want to know about the amount of information inside of our genome then you can just look at the genome directly. You don't need to count the number of mutations.
2. A genetic mutation isn't a computation. It's a random event.
3. Why did you choose a 500,000BC goalpost for anything? Which 500,000 genome do you want to look at? Almost all of them are not-conscious
4. There's no reason to assume biological evolution is an efficient method of manifesting consciousness.
5. Is a genome enough for consciousness? I would argue we would be less conscious without language, which exists outside of our genome.
Computation is roughly equivalent to iterating over a space of possibilities and selecting the subset that satisfy some evaluative function. To determine the inverse of a matrix, I can take the rough shape of the outcome and iterate over all possibilities, picking out the ones that multiply to the identity matrix. Evolution is the process of randomly testing variations in organisms to select the subset that satisfy the objective of superior fitness. So in a sense, evolution "computes" the blueprint for organisms that maximize fitness. The computational complexity of a given genome is then some function of the size of the species-wide population of each ancestor generation summed, with massive time and space constants.
1. Each generation was a branch based on the reaction not necessarily a genetic mutation as we understand them.
2. I'm not sure if that is what I said but I believe that.
* I did state it but I plead sloppy articulation rathe than believe each branch is a genetic mutation(an increment smaller than a mutation).
3. I figured it was far enough away to be a valid timeframe for sophisticated consciousness but not so close that the thread would be distracted by historical interpretations.
4.Something manifested consciousness and my thinking it based on some sort of survival reward system.
I have been thinking about it more and it could be the existing language models are actually large enough and it is lack of differentiations that leads to immature responses.
The developing these ideas further faces at least the challenge that an AI that is exposed to the public will develop an in accurate understanding of our world.
One driver of these misunderstandings is the lack of understanding expressed in the average internet post. The second big driver is that commercial needs requires a thought police mentality. This mentality distorts the expression of the answer the AI is articulating which my look like psychosis to the observers.
I believe that an AI will have to develop in isolation. The maturity of the current system is not able to distinguish a fact vs. fantasy. This is a problem we all posses at different levels. It's also possible we only need our personal AI assistant to be only 80% and the remaining 20% it gains from a dialog of it's host (the user).
You don't know how much biological consciousness relies on quantum effects we don't understand. We don't have large scale quantum computers so our computational models are too weak to approach it from that angle.
The first day of my first course in Biology was about Quantum Chemistry. (The last course of that year was on global ecology. Biology is a rather wide field!)
Quantum effects really do have something to do with it, (and from there on organic chemistry, organelles, and cell biology) but it seems to me that describing human behavior in terms of quantum interactions might be somewhat tedious, to say the least.
Probably looking at the level of neural networks would be more pragmatic, especially seeing the advances we're now making with artificial neural networks.
It seems that tracking the genetic mutations would provide an approximation of the computing complexity needed. One could also look at the death rate as error rate for the success of computations.