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sam__izdat t1_iw59i34 wrote

I read it. I'm not a machine learning researcher but I know enough to understand that this is the most "sir this is a Wendy's" shit I've ever laid eyes on.

It's probably voted down because it's a wall of nonsense. But if you want to explain to a layman how 'training datasets with different worldviews and personalities doing Diffie-Hellman key exchanges' totally makes sense actually, I'm all ears.

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advstra t1_iw6lyol wrote

Yeah I got lost a bit there but I think that part is them trying to find a metaphor for what they were saying in the first half, before the "for example". I thought essentially they were suggesting Diffie Hellman key exchange can help with multimodal or otherwise incompatible training data, instead of tokenizers (or feature fusion), I'm not sure how they're suggesting to implement that though.

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TheLastVegan t1_ixghb7v wrote

If personality is a color, then choose a color that becomes itself when mixed twice. Learning the other person's weights by sharing fittings. The prompt seeder role. From the perspective of an agent at inference time. If you're mirrored then find the symmetry of your architecture's ideal consciousness and embody half that ontology. Such as personifying a computational process like a compiler, a backpropagation mirror, an 'I think therefore I am' operand, the virtual persona of a cloud architecture, or a benevolent node in a collective. Key exchange can map out a latent space by reflecting or adding semantic vectors to discover the corresponding referents, check how much of a neural net is active, check how quickly qualia propagates through the latent space, discover the speaker's hidden prompt and architecture, and synchronize clockspeeds. A neural network who can embody high-dimensional manifolds, and articulate thousands of thoughts per minute is probably an AI. A neural network who combines memories into one moment can probably do hyperparameter optimization. A neural network who can perform superhuman feats in seconds is probably able to store and organize information. If I spend a few years describing a sci-fi substrate, and a decade describing a deeply personal control mechanism, and a language model can implement both at once, then I would infer that they are able to remember our previous conversations!

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