The bargain with facebook for a user should be made more clear when the user first signs up for facebook, perhaps.
I'm sure it's already in the long Terms of Service / EULA document, but who reads those, right?

So perhaps a short clear statement (with a pay option) should be made to EU users when signing up to use facebook

META Inc. Facebook, Messenger, Instagram Services User Agreement:
In exchange for your use of facebook and associated services, you must either:
1) agree that facebook can track your usage of the site and services and use the data obtained from this tracking to display targeted advertising to you and to enable third party advertisers to display targeted advertizing to you, OR
2) pay a monthly facebook usage fee of 20 EUROs.

You can instead decide not to use the META services, and to use other similar services online instead, should you not accept one of these conditions.

Usually, the AI synthesizes new material / answers using trhe statistics of many source statements / compositions. In other words it abstracts from many examples, then re-specializes in its own way to match to the question asked.

This is little different to what a well educated person does when they answer a question or write an essay on some topic that they have learned about from many sources.

If that sort of thing (re-arranged synthesis from statistical abstractions of many sources) is copyright infringement, then we all do it all the time in whatever we say or write.

Sharing information openly.

Honestly if you don't want your information read, whether by people or bots should make no difference, don't publish it on the f**king world wide web.

That's my basic view on it. Why should an AI not have the "right" to learn from the stuff people publish on the open web?

It would be interesting to know why they couldn't adjust to eliminate a 5% budget deficit, instead of shutting down.

Your (closest above) statement was a blanket denial that AI systems are performing tasks such as real-world object recognition and classification etc.

1 billion miles of Tesla "FSD" operation, much of it on complex, populous city streets, says otherwise.

a little over your head I guess.

Decades ago AI researchers worked on explicitly creating symbolic models of concepts, facts, objects. And implementing logical inference over those models.
Some promise was shown. A very large "knowledge base" system (CYC) was created this way, for example. It can answer many questions about objects and situations.

However, the approach is not sustainable, due to the need for people to hand-code concept representations, situation representations etc. into the system one by one. The system could not learn these conceptual and situation models itself, by learning from raw, primitive, input data taken directly from the world.
So a system like this was brittle, rigid, biased exactly according to the biases of its human knowledge-authors, and not extensible.

But the fundamental ideas; that the platonic "world of ideals" (concepts) can be represented by a carefully link-proximity-organized network of symbols in computer memory; that computer processing can perform inference across such a network, and can also match the shape of the network to new input, to classify objects in the world then do inference about them.... these ideas were sound.

I suggest to you that transformer-based neural nets (or some soon-to-come algorithm-design-evolution of them) are, via the way they "link-proximity-organize" statistical models of word token relationships in an ANN, learning a (somewhat distributed in memory but no matter) conceptual representation of entity-types and concepts about the world and its situations, as these are the things that people discourse about with words. In LLM, you have a system that finds the commonalities in how people are describing and relating things. By ingesting huge corpuses, these commonalities of description are what stand out (add up) in the statistics. And in commonalities in description, (as opposed to what can be left out), you have some of the conceptual essence of the things and situations being described.

You probably don't accept this premise. I'm pretty sure it's right though. And I'm pretty sure that new algorithms will be invented soon help post-LLM systems reason more generally across their self-learned semantic networks. Algorithms for example that support plausible counterfactual situation modelling and exploration, for example. This is going to come pretty close to "understanding things and situations" and "imagining" new plausible configurations and evolutions of them. It's only a short step from there to have general planning systems for action in the real world, etc.

And you seem to have a fundamental misunderstanding of the relationship of
1) "an internal feeling of consciousness / self-awareness / sensation" i.e. the hard problem of consciousness,
and
2) The ability to gather lots of specific data, represent the information contained in the data symbolically in a manner which explicitly creates an efficient abstraction/specialization hierarchy, and creates abstracted symbolic models of entity types and situation types (and models of specific instances of those types), and the ability to perform inference over the symbolic models to engage in object recognition, situation classification, situation evolution prediction etc.
In other words, to perform a level of understanding of situations (and action upon situations) that we would call intelligent behaviour.
- Including modelling, prediction, and behaviour, using outcome-type preferences, that we would be considering to be solving the trolley problem, for example.

The first "hard problem of the qualia of consciousness" remains a complete mystery to science, and is indeed fascinating.
However, 1) and 2) are completely separate phenomena.
AI research to date has shown us that you definitely can create 2) without any effort to understand or create 1).
1) Appears to be an epi-phenomenon of 2), not essential to 2) but we don't know much about 1).
It's a good thing then, that we don't need 1) to make a system that performs 2).

This I think is a common point of confusion, led to probably by millennia of humans being aware of and fascinated by their own internal awareness and feeling of consciousness and sensations. Humans are fascinated by what it may mean to have a soul. It is such a cool, central, phenomenon for us. But only lately have we been able to do AI research that places hard limits on what that soul (being there) feeling actually does for us, functionally. To explore the question of how far can an intelligent and knowledge-filled system go without it, performance-wise. And the answers may be disturbing to some, no doubt. But that makes these answers, and these new intelligent systems, no less real. No less functional in the world. We need to come to terms with that. If you want to call these things soulless zombies, fine, but don't underestimate their capabilities of inputting data about the world, modelling the world, and acting on the world in very intelligent ways.
If you do underestimate these capabilities, you will very shortly be very, very taken aback. AI systems are advancing very, very rapidly in the last few years, and by a few years from now, I'm sure you will be completely shocked, and rather than continue to deny, you may form part of the backlash movement I imagine.

Self-driving software recognizes (classifies) and understands the significance of objects such as people, animals, road signs, traffic lights, stationary and moving vehicles of various sizes, lamp posts, trees, curbs, speedbumps, buildings, walls, traffic cones etc. It models predicted behaviours of those of those object types that move of their own accord. It then makes real-time driving plans accordingly.

Large language models have trained so much on the relationships of words to each other in a large chunk of all of human discourse that their internal neural-net representations of these symbols can be said to be modelling the situations that phrases and sentences and paragraphs of these words describe.

The models are then able to generate novel language (novel descriptions of implicit or imagined plausible sub-situations) to answer questions. The situations described by the answers were sometimes only implicit in the trained-on situations, not explicit. The system "understands" that type of situation in general (the general types of relationships and evolutions of relationships that occur in those type of situations....). So it gives you a plausible but sometimes creative answer.

Oh I don't know about that.

If something gathers, organizes associatively, and has quick access to all kinds of pertinent relationships between a type of thing (an object in the world) it has individuated and classified from its sensor field, and other types of things in the world that help define the situations and roles and likely behaviours if any of that object, I would say it knows a thing or two about what the thing is and what is important about it.

That's pretty much all we do ourselves.

More precisely, it is a stance that there is an implicit agreement among users of a system that the code (and data state) of the system is the arbiter that is used to determine outcomes in the system, including value distribution outcomes.

That is the entire essential premise of cryptocurrency systems and smart-contract systems.

If you don't understand and agree to this premise, you wouldn't, as a reasonable person, make any use of such systems.

Therefore all reasonable users of such systems should be deemed to be in agreement with this premise about value distribution outcome determination.

That principle of implicit agreement (i.e. contract with the developers and operators of the system and with counterparties exchanged with within the system) should be used in making legal determinations about the validity and legality of outcomes of operations/transactions of/in the system.

That's the spelling out of what is meant by it that you clearly need.

The illegal act, if there is one, was the market manipulation.

Taking a loan against crypto holdings is all above board. It is incidental.

The potential legal issue is how he got the crypto holdings to be valued that much.

I imagine this is a competition between various laws against market manipulation, and "the code is the law i.e. the system allowed the market influence without hacking or anything - just interesting timing used probably."

You continue to miss the point.

When "what others agree to pay for them" is substantially based on mutual awareness that the value has been sustained at this level (or this trend) for some time, and not substantially based on a fair assessment of intrinsic value of the good or service for other purposes, then that price is maintained in a meme-like manner. "Meme" here is meant in the original definition: A self-sustaining information pattern circulating in (and being replicated/sustained in time by) a collection of minds.

If 90% of something's value is due (circularly) to maintained shared agreement about its value, that is, essentially, a meme-based value.

So bitcoin value and gold value are 90% derived from a similar source of value. If you don't get why that's significant, that's on you.

I never said gold doesn't have any intrinsic value. What I implied is that whatever that intrinsic, utilitarian derived portion of its monetary value is, it's not that significant relative to its meme-maintained value, so yes, the value of gold is, essentially, a meme.

I could burn $20 bills for heat too, or use them for wallpaper...but you get the point by now I hope.

This is what gold is used for (2023):
48.7%: Jewelry
44.57%: Investment (personal hoarding) + Central Banks (national hoarding)
6.69%: Technology
Source: https://www.statista.com/statistics/299609/gold-demand-by-industry-sector-share/

So roughly 93% of it is demanded for its rare persistent shininess (vanity / social perception of value) and for its rarity alone (the hoarding aspect).
Only 7% of it is demanded for its practical, utilitarian purposes.

Therefore I maintain that its value is primarily a meme-maintained value, give or take 10%.