The usual companies may not have ethics as you write, but politically motivated company can easily embed "ethics" in a recommendation engine like that.
Vipassana meditation to reveal fears and other mental processes that are hidden. Also to repair focus disseminated by the modern culture of constant distractions. Even 15 minute session is enough to feel the potential and become more aware.
Sometimes this awareness is enough to change bad mental habits. But there are other things that you continue to do even if you understand that you don't want it. These are "imprints" NLP terminology.
To change these imprints my friend does really long Vipassana sessions for more than 10 hours. She claims to change at least one strong imprint that was "panic when staying at home alone". Which is related to an event happened when she was less than a year old.
I have not tried so long Vipassana sessions and to change my imprints I do Connirae Andreas "Core Transformation" technique. It's easier to have other person do it to you, however with enough determination you can train to do it by yourselves. Unfortunately it's still a lot of efforts to learn it. Anyway may be that helps.
They process Lucene index and create embedded representation of it. Then you can search over that representation for "semantic" matches.
Last time I checked it about a year ago the embedded collection of documents was kept in the memory and the search was implemented by a linear scan. So I suspect it can be slow on very large collection of documents.
"Adversarial examples generalize across models trained to perform the same task, even if those models have different architectures and were trained on a different training set."
To really feel the difference you have to work on complex logic.
It's easy to be under illusion that you can create everything using a verbose language. At some point the complex system you work on just gets extremely hard to reason about.
For verbose languages you reach this point considerably faster.
You can try to find a dataset that contains the equal number of positive and negative documents (sentences, etc.) and use it as the validation set. I.e. to tune your hyperparameters on it.
In the simple case your hyperparameter can be α in
Part of the answer is here - https://www.wheresyoured.at/the-men-who-killed-google/