"why didn't the first person to come up with the idea of using computers to predict the next element in a sequence patent that idea, in full generality"

Patents are valid for about 20 years. But Bengio et al used NNs to predict the next word back in 2000:

https://papers.nips.cc/paper_files/paper/2000/file/728f206c2a01bf572b5940d7d9a8fa4c-Paper.pdf

So this idea is old. Only some specific architectural aspects are new.

I suspect this labeling and using the labels is still harder that you think though, since individual tokens don't have truth values.

Why should they?

You could label each paragraph, for example. Then, when the LM is trained, the correct label could come before each paragraph, as a special token: <true>, <false>, <unknown> and perhaps <mixed>.

Then, during generation, you'd feed it <true> as part of the prompt, and when it generates paragraph breaks.

Similarly, you could do this on a per-sentence basis.

The idea that we're going to produce a similar amount of perfectly labeled data doesn't seem plausible.

That's not at all the idea. Allow me to quote myself:

Here’s what I think we could do. Internet text is vast – on the order of a trillion words. But we could label some of it as “true” and “false”. The rest will be “unknown”.

You must have missed the words "some of" in it. I'm not suggesting labeling all of the text, or even a large fraction of it. Just enough to teach the model the concept of right and wrong.

It shouldn't take long, especially since I'm assuming a human-level ML algorithm here, that is, one with data efficiency comparable to that of humans.

What do VELM and VETLM offer which those other implementable proposals don't? And what problems do VELM and VETLM not solve?

VETLM solves superalignment, I believe. It's implementable (unlike CEV), and it should not be susceptible to wireheading (unlike RLHF, instruction following, etc) Most importantly, it's intended to work with an arbitrarily good ML algorithm -- the stronger the better. 

So, will it self-improve, self-replace, escape, let you turn it off, etc.? Yes, if it thinks that this is what its creators would have wanted.

Will it be transparent? To the point where it can self-introspect and, again if it thinks that being transparent is what its creators would have wanted. If it thinks that this is a worthy goal to pursue, it will self-replace with increasingly transparent and introspective systems.

New proposals are useful mainly insofar as they overcome some subset of barriers which stopped other solutions.

CEV was stopped by being unimplementable, and possibly divergent:

The main problems with CEV include, firstly, the great difficulty of implementing such a program - “If one attempted to write an ordinary computer program using ordinary computer programming skills, the task would be a thousand lightyears beyond hopeless.” Secondly, the possibility that human values may not converge. Yudkowsky considered CEV obsolete almost immediately after its publication in 2004.

VELM and VETLM are easily implementable (on top of a superior ML algorithm). So does this fit the bill?

That post was completely ignored here: 0 comments and 0 upvotes during the first 24 hours.

I don't know if it's the timing or the content.

On HN, which is where I saw it, it was ranked #1 briefly, as I recall. But then it got "flagged", apparently. 

Machine Learning Street Talk interview of one of the authors: 

There was an article in New Scientist recently about "sending particles back in time". I was a physics major, but I might have skipped the time travel class, so I don't have an opinion on this. But Sabine Hossenfelder posted a video, arguing that New Scientist misrepresented the actual research.

Side note: the link didn't make it to the front page of HN, despite early upvotes. Other links with worse stats (votes at a certain age) rose to the very top. Anyways, it's currently ranked 78. I guess I don't really understand how HN ranks things. I hope someone will explain this to me. Does the source "youtube" vs "nytimes" matter? Do flag-votes count as silent mega-downvotes? Does the algorithm punish posts with numbers in them?