For you not to waste your precious time - "orb" is just a virtual machine. And the thing requires a phone number, which it tells you at the END of the registration.
I have new marketing jingle for that company: "market your AI slop with non-AI slop".
I am on the Amp team. I just turned off SMS verification. Not needed anymore. Was required for ~5% of signups for anti-abuse, but I think we're good without it now.
The people doing the work aren't paying for AI, they're getting paid by AI.
The people who are having the costs of users not paying "their fair share" (this phrase has officially jumped the shark) are the investors who are subsidizing these companies.
In case of lmgtfy the receiver was usually the lazy person. The question was usually so simple that simple that asking it for nth time on public forum was the waste of everyone's time. In case of pasting LLM response verbatim the responder is usually the one wasting everyone's time.
LLMs are ascendant partially because they do not "RTFM", "read the FAQ", or "question closed as duplicate" at you regardless of how many times they've heard a question before.
No, you can't copyright an idea, only an expression of an idea, and LLMs operate at the level of ideas. They don't literally stitch together code from training.
Well, not exactly. A LLM is still a computer, doesn't have an intelligence (beside being called AI). That means that their output is a mere computation of their input data, and their input data it's the stuff that was used for the training.
If you imagine it as a "box" you feed into it material and a prompt and it spits out the same material rearranged to do what you did ask for. It does nothing more than a permutation of their input data, as does any computer program, of course in extremely complex and obscure way, but if you reason it abstractly it's the same things Turing theorized almost a century years ago, input -> BOX -> output.
So *of course* the output *is* a derived work of the input, and thus a GPL code should not really used as a training set.
I'm pretty familiar with the technical details of how an LLM works. They absolutely operate more on a level of abstract concepts than acting like some kind of text-Frankenstein as you're suggesting (as if the model is Frankenstein and its output is his monster).
Here's a very basic example: if you have access to a typical language model's weights, you can subtract the embedding for "man" from the embedding for "king", add the embedding for "woman", and land somewhere very close to the embedding for "queen".
Why is "intelligence", whatever that means, a prerequisite for a machine to process ideas in the abstract?
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