Yes. Probably the reason why this is off in the EU: MS does not want another high fine for criminal behavior.

Or rather the "Backup & Restore" options are not even there. Makes sense, no one-drive. This is yet another reason to configure a local account. It prevents the MS corporate assholes from pushing their choices on you.

Microsoft achieved their goal. Sony won't be able to leverage games from Activision or Blizzard or Bethesda or any of the other studios they bought in order to expand their dominance in the console market. This was about anti-competitive behavior on the part of Microsoft and now that they've got what they want there's no reason for them to keep spending money.

Meanwhile the very real threat of the steam machine is completely irrelevant for at least the next five years because of the AI apocalypse and our unwillingness to do anything about it politically. And yeah it's a political problem. Deal with it.

So Microsoft can basically kick back and soak up all our money and there really isn't fuck all we can do about it. They don't care about the game division they care about their mainline software division and making sure there isn't any credible threat to it. And no Linux is not a credible threat.

They are just to be on the safe side spinning up some of the old lawsuits to cause fud. And it will work. Although I think that's mostly directed at Europe.

That's one of the things big corporations and billionaires and now trillionaires don't leave anything up to chance. They grind everyone in the pulp and take everything and don't leave us with anything. It's not just about greed or lust for power it's about having complete absolute power. And we are always happy to give it to them for reasons I wish I could say I didn't understand but with all the research into the various neurological problems right wing voters have combined with 60% of the populist reading at a 6th grade level well it all kind of makes sense...

Exactly. Building GPUs is difficult. Building AI chips is much simpler without having to deal with all the graphics parts.

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.

an AI model somewhere. If there is ever another Democrat president here in the states then Microsoft will get a very very tiny hand slap for illegally using the data without notice and go about their business with a massive trove of your information used to train the AI design to replace you at work...

It's the credit card Network. And this is a slashvertisment.

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.

They will pay about $2k a month on that for the rest of their lives... Their starting pay will be about $6k/mo more than that.

The math is not hard here.

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.

Per TFS, this is specifically a study regarding "50 top research universities"

That could be literally any field, though.

Some firms will still employ people who can think, because their leadership can think well enough to know computers can't. They will no doubt reap the rewards of keeping humans in more loops. But in the meantime, a lot of people are going to suffer a whole lot.