>> Of course, this is a high-level description and the actual process involves a lot of complex mathematics and computation. But I hope it gives you a better sense of the mechanisms behind these models.

For the record, I just polished off a PhD in AI (symbolic machine learning) after a Master's where I studied neural nets and NLP, including plenty of language generation. You're teaching your grandma to suck eggs.

And I'm really very tired with this kind of conversation that never teaches me anything new. Your comment is still "what"'s all the way down. You never explain why or how word embeddings capture aspects of meaning, you 're just repeating the claims by Mikolov or whoever. Look, here:

>> Through this training process, the model learns to represent words and phrases as high-dimensional vectors, also known as embeddings. These embeddings capture many aspects of the words' meanings, including their syntactic roles and their semantic similarities to other words.

That's just a claim, made long ago, and challenged at the time, and the challenge ignored, and it keeps being bandied about as some kind of scientific truth just because critics got tired or bored having their criticims consistently ignored and gave up trying.

This is what I point out above: connectionists never stop to consider criticism of the limitations of their systems' until someone rubs their face in it - like Minsky and Pappert did in 1969, which then caused them to be forever reviled and accused of causing an AI winter, when what they really caused was some connectionists to get off their butts and try to improve their work, a process without which we wouldn't, today, have backpropagation applied to NNs, and the potent image classifiers, good models of text, etc, that it enabled.

As to the "evidence" you profer, mainly preprints on arxiv, and mainly consisting of budding researchers uploading papers consisting of little more than leaderboards (those little tables with the systems on one side, the datasets on the other side, and your system's results in bold, or no paper) those are useless. 99% of research output in deep learning and neural nets is completely useless and never goes anywhere- because it lacks novelty, it is completely devoid of any theoretical results, and it is unreproducible even when the code is available.

For example, you mention studies on "question answering". Ca. 2018 Google published a paper where they reported that their BERT language model scored near-human performance on some question answering dataset without ever even having been trained on question answering. A scientific miracle! Some boffins who clearly don't believe in miracles wondered why that would even be possible and dug a bit, and found that BERT was overfitting to surface statisical regularities of its dataset. They created a new test dataset devoid of such statistical regularities and BERT's performance went down the drain, until it hit rock bottom (a.k.a. "no better than chance"). So much for "semantic similarity" measured over word embeddings modelling meaning.

But this is exactly the kind of work that I say connectionists consistently ignore: nowhere will you find that subsequent language models were tested in the same way. You will instead find plenty of tests "demonstrating" the ability of language models to represent semantics, meaning, etc. It's all bullshit, self-delusion at best, conscious fabrications otherwise.

This is the paper (I'm not affiliated with it in any way):

Probing Neural Network Comprehension of Natural Language Arguments

https://aclanthology.org/P19-1459/

But this kind of work is thankless for the undertaking academics and most of us have more important things to do. So the criticism eventually dwindles and what remains is the bullshit, and the fabrications, and the fantasies, seeping into mainstream discourse and being repeated uncritically - by yourself, for example. I can't even summon the compassion to not blame you anymore. For all I know you're exactly one of those connectionists who don't even understand their work is not science anymore, but spectacle.

P.S. I am not blind to the change of tone in your recent comments and I'm really sorry to be so cranky in response, when I should be cordial in reciprocity, but I've really had enough of all this. Unscientific bullshit has permeated everything and oozed everywhere. Perhaps it's time for me to take a break from HN, because it really doesn't look like I can have an original, curious conversation on here anymore.