Compare like-to-like: separated identical twins to sepaparted fraternal twins.

I think the best introduction to the topic would be this lecture, which is mostly about all of the problems with separated twin studies. Identical twins starts at 37 minutes.

I think a better argument than #2 would be that evolution tends to remove genetic variantions.

Thank you

These two facts seem incompatible:

1. Personalities are inherited. Identical twins separated at birth are statistically more similar than fraternal twins.
2. The human population has almost zero genetic variation, and there is significant mixing so variations do not systematically cluster. Therefore, it seems unlikely that subtle personality differences are due to genetic variation.

My hypothesis is that animal personalities are encoded in epigenetic changes.

This allows personalities to be inherited, crossover, and evolve. Life experiences can induce epigenetic changes, which allows animals to reliably adapt in a single generation. All of this without requiring any genetic variation. A population of clones could have diverse personalities stored in their epigenome.

While playing with evolutionary algorithms, I had the startling realization that all genetic mutations are bad. It’s common knowledge that biology abhors genetic mutation, and I assumed that was only because mutations cause cancer. But my computer programs are immune to cancer, and they also abhor mutations. This is counterintuitive, given that evolution requires mutations to procede.

For proof of the fact that mutations are bad, consider that evolution is an optimization algorithm, and after it reaches a local optimum further mutations will be strictly detrimental. The concept of evolutionary pressure is the ability of an evolutionary algorithm to remove deleterious mutations from a population. If mutations accumulate faster than they can be removed then the population will suffer a genetic collapse. This is a common failure mode of evolutionary algorithms.

The ideal evolutionary algorithm would have at most one mutation in each individual, and each of their lives would be an experiment to evaluate that single mutation. And then through many generations of chromosomal crossover the best mutations would combine into a single genome.

I believe you are looking for the distinction between closed loop and open loop controllers. IIUC this theorem only applies to open loop controllers. OP's flowchart does not contain a feedback loop. For comparison, Richard Kennaway's flowchart has a feedback loop between Z and R.

An example of an open loop controller is a dishwasher or laundry machine.

All animals are examples of closed loop controllers.

Biological evolution produces "messy" models. They are needlessly complex and difficult to understand. And yet they are alive!

Here are my notes on the topic of evolution and artificial life. The section "Sparsity & Modularity" discusses what I mean by "messy" models. https://coldcoffee.neocities.org/evolution_review

Forget about the model's weights, the revolution will be published in an academic journal. The underlying principles of AGI are going to be talked about, even if the exact methods are secret. As a scientist, to discover something important and then carry it to the grave is an absurd proposition. That is not asking people for discretion or secrecy, that is asking them for their resignation and early permanent retirement.

The nuclear bomb industry is in a similar state: we all know the basic science but we dont publish schematics! This minor omission from the scientific literature only slowed down nuclear armament, but it did not prevent the cold war nuclear arms race. However, unlike bombs, AGI might actually be useful and so most people are much more motivated to persue this tech.

Sorry to get your hopes up but I was being facetious and provocative. Instead of a glass jar, our horse's brain is going to live inside of a computer simulation. Nonetheless, I think my argument still holds true.

Neuroscientists scoff at the thought of whole brain simulation. They're incredulous and as a result they're unambitious. They want it but they know they can't have it; they've got sour grapes. Despite these bad vibes, they have been working diligently and I think we're not too far off from making simulations which are genuinely useful.

On a wacky side note, IMO, if we did have a horses brain in a jar, then interacting with it would be the easy part. There have been some really neat advances in how we interact with brains. 

• We can make neurons light up when they activate, see GCaMP

  • And here is a video of GCaMP in action:

• We can activate synapses with light, see Optogenetics

The hard part would be keeping it alive for its 25-30 year lifespan even though it's missing important internal organs like the heart, lungs, liver, and adaptive immune system.