No. Traditionally, donors have no standing to sue charity. From https://www.thetaxadviser.com/issues/2021/sep/donor-no-standing-sue-donor-advised-fund.html

California limits by statute the persons who can sue for mismanagement of a charitable corporation's assets. The court found that the claims raised by Pinkert for breach of a fiduciary duty for mismanagement of assets were claims for breach of a charitable trust. The court determined that under California law, a suit for breach of a charitable trust can be brought by the attorney general of California...

The patent is not yet granted.

Someone from South Korea is extremely skeptical and wrote a long thread going into paper's details why it must be 100% false: https://twitter.com/AK2MARU/status/1684435312557314048. Sorry it's in Korean, but we live in the age of miracle and serviceable machine translation.

But it wasn't until the 1940s and the advent of the electronic computer that they actually built a machine that was used to construct mathematical tables. I'm confused...

You are confused because that is not the reality. As you can read on Wikipedia's entry on difference engine, Scheutz built a difference engine derivative, sold it, and it was used to create logarithmic tables.

You must have read this while writing this article. It is prominent in the Wikipedia article in question and hard to miss. Why did you make this mistake? If it was a deliberate act of misleading for narrative convenience I am very disappointed. Yes, the reality is rarely narratively convenient, but you shouldn't lie about it.

My median estimate has been 2028 (so 5 years). I first wrote down 2028 in 2016 (so 12 years after then), and during 7 years since, I barely moved the estimate. Things roughly happened when I expected them to.

I am curious how this fine-tuning for function calling was done, because it is user controllable. In the OpenAI API, if you pass none to function_call parameter, the model never calls a function. There seem to be one input bit and one output bit, for "you may want to call a function" and "I want to call a function".

While I agree being led by someone who is aware of AI safety is a positive sign, I note that OpenAI is led by Sam Altman who similarly showed awareness of AI safety issues.

Parallelization part (data parallelism, tensor parallelism, pipeline parallelism, ZeRO) is completely standard. See Efficient Training on Multiple GPUs by Hugging Face for a standard description. Failure recovery part is relatively unusual.

That is trivial to program? For example, you can have AutoGPT UI which lists pending tasks with icons next to them, where clicking a trashcan will completely erase it from the context. That doesn't need any LLM-level help like LEACE.