PyTorch themselves used nanoGPT training as demo for this: https://pytorch.org/blog/accelerating-large-language-models/
Of course, you wouldn't encode real-time navigation data, but a small block of identifying text. Either way, though, someone without a copy of the spreading code isn't going to notice it or decode it. Given enough redundancy in both the time and frequency domains, removing it wouldn't be easy either.
For reference, look at how the societal damage of social networks has been handled: too little, too late. Same goes for RentTech.
But, I don't know the solution. The common computer has become so powerful that we cannot simply rely on inaccessible materials to prevent the danger of overly-powerful tech spreading too fast, as we do with bioweapons or traditional WMDs.
Fight tech with more tech, I suppose.
We want to go back to New York in the 90s? Petty theft everywhere?
With great power comes .... ? Profit?
Text to speech was a natural playground for us to share with the community and get some feedback. Given that this model is a full GPT model, the text input is merely a guidance and the model can technically create any audio from scratch even without input text, aka hallucinations or audio continuation.
When used as a TTS model, it’s very different from the awesome high quality TTS models already available. It produces a wider range of audio – that could be a high quality studio recording of an actor or the same text leading to two people shouting in an argument at a noisy bar. Excited to see what the community can build and what we can learn for future products.
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