Relative point to point

• which Blockchain are we talking here? How does it compare to the current banking infrastructure?
• again, which one? How does it compare to the current pricing?
• escrow is a thing, someone can build up a PayPal equivalent on top of a Blockchain, the list goes on
• the current system doesn’t do great here, some Blockchains makes it way more traceable, in fact
• skill issue, but also solvable with a PayPal equivalent
• not a fact, what does this even mean?
• does it?

You could say the Linux kernel is an astronomically terrible idea because it doesn’t do anything…but it is just the platform, the good comes from what people build on top of it that add all these quality of life features you miss

Buy ydy

I don’t really follow your logic, how else would you propose to shape the audio that is not “just an effect”.

Your analogy to real life does not take into account that the audio source itself is moving, so their is an extra variable outside of just stereo signal -which is what spatial audio is modelling

And your muffling example sounds a bit over simplified maybe? My understanding is that the spatial stuff is produced by phase shifting the LR signals slightly

Finally why not go further? “I don’t listen to speaker audio because it’s all just effects and mirages to sound like a real sound, what only 2^16 discrete positions the diaphragm can be in” :p

Do you read a song on demand when you are singing the lyrics verbatim?

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It’s definitely not indexed, we use RAG architectures to add indexing to data stores that we want the model to have direct access to, the relevant information is injected directly in the context (prompt). This can somewhat be equated to short term memory

The rest of the information is approximated in the weights of the neural network which gives the model general knowledge and intuition…akin to long term memory

There’s is a huge difference though.

That being one is making hardware and the other is copying books into your training pipeline though

The copy occurs in the dataset preparation.

Privacy preserving federated learning is a thing - essentially you train a local model and send the weight updates back to Google rather than the data itself…but also it’s early days so who knows what vulnerabilities may exist