I'd like to share a book recommendation: 
"Writing for the reader"
by O'Rourke, 1976
https://archive.org/details/bitsavers_decBooksOReader1976_3930161

This primer on technical writing was published by Digital Equipment Corporation (DEC) in 1976. At the time, they faced the challenge of explaining how to use a computer to people who had never used a computer before. All of the examples are from DEC manuals that customers failed to understand. I found the entire book delightful, insightful, and mercifly brief. The book starts with a joke, which I've copied below:

On the West Coast they tell the story of a plumber who started using hydrochloric acid on clogged pipes. Though he was pleased with the results, he wondered if he could be doing something wrong. So he wrote to Washington to get expert advice on the matter. In six weeks he received the following reply:

"The efficacy of hydrochloric acid in the subject situation is incontrovertible, but its corrosiveness is incompatible with the integrity of metallic substances."

The plumber, who was short on formal education but long on hope, was elated. He shot a thank-you letter back to Washington. He told them he would lose no time in informing other plumbers about his discovery. Five weeks later he got another message:

"In no case can we be presumed responsible for the generation of pernicious residues from hydrochloric acid, and we strongiy recommend, therefore, than an alternative method be utilized."

The plumber was delighted. He sent his third letter in the next mail. In it, he said that about 15 plumbers in his city were now using hydrochloric acid for pipes. All of them liked it. Now he wondered whether the good people in Washington could help him spread the news of his discovery to plumbers throughout the country. At this point, the correspondence fell into the hands of a rare Washington bureaucrat - one who knew how to write to plumbers. Within a week the plumber was reading these words:

"Stop using hydrochloric acid. And tell your friends to stop too. It eats the hell out of pipes."

Certainly the letters in this interchange are a far cry from technical writing. Nevertheless, they offer a lesson to the new technical writer: Write so your reader can understand.

Sutton seems to confuse intelligence with life. These are distinctly different concepts. Compare LLMs and bacteria: LLMs are intelligent but not alive, bacteria are alive but not intelligent. Bacteria have goals, such as consuming food and avoiding hazards, and bacteria take directed action to accomplish their goals.

Banning autonomous self-replication and the termination priniple seem overly broad and potentially cover systems that peacefuly exist today. For example, evolutionary algorithms have self replicating entities, and control systems can operate independently and be designed to never turn off.

We've unwittingly created a meme, in the original sense of the word. Richard Dawkins coined the word meme to describe cultural phenomena that spread and evolve. Like living organisms, memes are subject to evolution. The seed is a meme, and it indirectly causes people and AI chatbot's to repost the meme. Even if chatbots stopped improving, the seed strings would likely keep evolving. 

Yikes, he equates big tech with eugenics

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.