It's "Learn to prmpt" these days, dude.

Note that they've split this into about a dozen different settings which all have to be turned off. So that's a dozen things I have to change on four or five different PCs to disable this new "feature" which I'm sure wasn't added to steal my data, honest guv.

Congratulations, they've now gated the security updates behind 26H2, because Dumbfuck Klanbitch Trump and the Treasonfuck GOP allowed them to.

Once a month it tries to force my browser default back to Edge. I tell it to fuck off.

A few times they've done it BEHIND MY BACK and I had to fix it manually after a fake "patch" to windows.

Before the days of Dumbass Fuckwit Trump and the Constitution Hating Treasonshit GOP, we had government entities that SUED Microsoft under Antitrust law for this shit.

Fuck the GOP. Fuck them for enabling this shit. Every crossburning Constitution-hating member of the GOP is a fucking shitrag.

The KlanFuckSix do not CARE about the Constitution, you Putin-Dicklicking GOP TreasonTrashBITCHCUNT. All of you KlanBitchGOPFucks are cockroaches, traitors to the country and we need some fumigation of your infestation.

but IIRC, Apple has iOS's iCloud backup on by default (and quickly runs out of space and then nags you to subscribe to a paid tier).

DUH. That's the play. They force you into a "service" as an "opt out." Then - oh, the free tier is insufficient. Or the free tier JUST GOES AWAY and then they're holding your computer hostage.

Retardican GOP failed utterly at governance, the government entities that were supposed to be watching out for us and enforcing antitrust law died under Trump. Every Retardican traitor to the Constitution helped cause this shit.

If not for Treasonfuck Trump and the Shiteating GOP who hate the Constitution, this would never have been allowed, and the Federal agencies would actually be hauling MS's ass in for antitrust about this.

This is pathetic. The USA is over and dead.

Learning multiple languages in most cases has nothing to do with parenting or nutrition or leaning habits. Most people who learn multiple languages learn them because they live in communities where multiple languages are spoken.

Multilingualism is weakly correlated with intelligence. It's strongly correlated with growing up in an area where people speak many languages.

Well, this data says that your best bet is to have learned a lot of languages as a child. The study found that earlier acquisition of multiple languages was more effective than learning additional languages later in life.

Yes, it is "all that different". You're comparing conceptual similarity, not data similarity. Data (and usually) appear similar without being conceptually similar, or appear different while actually being conceptually similar. A dolphin, a lungfish, a coelacanth, a tuna, a shark, and a hagfish may all appear visually to be the same sort of "thing" (fish), but they're all actually radically different things. What you actually want to be comparing is the core conceptual aspects of things, not their outward (raw data) appearances.

it always annoys me to see announcements of a correlation being described as a cause-and-effect.

Whenever I see cause-and-effect stated without them being clearly distinguished, I always ask myself "could they have the cause and effect flipped around?"

Maybe people that have "younger brains" are much more likely to be in the group that has learned another language recently?"

That seems to be much more likely than the stated order, since people with more youthful minds would be more likely to be learning new languages than others with older, less-flexible minds?

Or stated another way, "younger minds encourage multi-lingual" rather than "multi-lingual encourages younger minds".

Oh, and I forgot to include the "why". TL/DR: it just works better.

To form a latent, the network fundamentally has to learn correlations, in order to be able to accurately compress and then decompress the information in its input space. Due to the limited size of the pinch, you simply can't store every possible data-space representation of a given input. The encoding down to these vectors must persist despite the sort of "real-world" variations that occur in the input space.

Within the latent space (latents are vectors), you can think of individual subconcepts as being directions with in the latent space. In the "king" / "queen" case, for example, adding "man" times some scalar shifts you forward and backwards along degrees of masculinity. In the case of a spectrum, these vectors represent various fundamental molecular properties of the material being scanned. By computing the cosine distance vs. known entries in the vector db and picking the most similar entries, you are in effect minimizing the distance between the molecular properties of the samples.

By contrast, you certainly CAN minimize distances in data space! The problem is that data space lacks correlations. Two things being physically similar in data space doesn't actually mean that much if the things that are different are extremely definitionally-critical aspects. By contrast, definitionally-critical aspects inherently become a key part of vector encoding. Trying to work in text space, for example, a distance algorithm might declare that "king" and "kingdom" are textually similar, but "queen" does not appear at all similar to it. But it's fundamentally similar in vector space.

For YHVH's sake, does nobody in this thread know what a vector DB is?

Translating that to signatures for chemicals is basic spectroscopy which has worked for decades.

The emerging standard for spectroscopy is to translate the spectrum into a latent vector and match against vector keys. For example, Spec2Vec.

This is not what you may at first be thinking of when you see vector, e.g. "the spectrum itself is represented as a vector". These vectors are latents, not spectra.

The most common way to create a latent is via an autoencoder. You task a neural network with recreating its input and put a "pinch" in the middle of the network. This forces the network, to maximize its predictive accuracy, to create a dense conceptual representation of the concepts at hand at the pinch: a latent. The left hand side of the network becomes a latent encoder (detecting concepts in the input space and encoding them into the latent space), while the right hand side becomes a latent decoder (translating the latent space back to data space).

(Note that Spec2Vec is a bit different - it's based on Word2Vec and treats the peaks and neutral losses as "words" and the entire spectrum as a "document", seeking to learn the correlations between peaks - but this is getting out into the weeds)

One of the properties of latents (in most latent spaces) is that they let you interpolate concepts (for the classic example, the latent for "king", minus the latent for "man", plus the latent for "woman", resembles the latent for "queen"), and they let you measure the conceptual distance between concepts (cosine similarity)

Once you have a trained encoder and decoder, a vector database can handle vector queries. The ideal situation would be that all rows would be keyed with vectors and the cosine similiarity to your query key is calculated vs. the table keys, but since that's not practical to query every row, Approximate Nearest Neighbor algorithms are used to approximate that. Hits matching a desired minimum level of similarity are returned. Note that the decoder is no longer needed, you only care about encoding the latents.

Search engines widely use vector databases, BTW, though they usually also combine them with text matching ("hybrid search") - the latter is fast and cheap, so it doesn't hurt to add it.

It's not "fuzzy matching", it's a vector database. You're looking up with conceptual keys into conceptual-indexed tables. The keys are latents, not fuzzy string-matching rules.

Um, no. AI was founded as an academic discipline in 1956, at the Dartsmouth Workshop. The term has been in widespread use for seven decades.

Also, AI != ML. ML is an AI technique. Most AI today is ML, but it wasn't always that way. Symbolic AI, Rules-Based AI, etc used to dominate before NNs advanced enough to push them aside. Expert Systems are a classic example of non-ML AI.