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by f_devd
1161 days ago
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That's not quite the same as I meant, sense of time passing is not 'X seconds passed" as these are you are describing when time passed into the text modality (which means it's treated as text), a sense of time passing means it can choose when to generate new text and abstain from generating new text if it's not the right time, it can observe the other modalities passively, etc; this requires continuous-time features and the loop, or alternatively a continuous neural network like spiking networks. Likewise memory with respect to time is is both long-term (lifespan) and short term memory which includes this continuous-time such that it can describe events that it has witnessed and correlate the events by their timing & it's context Now the reason it's still a "maybe" then is because we would need to reasonably prove it's not a stochastic parrot. |
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I don't see why it matters if the "time signal", whatever it is - and you surely need one for an internal clock either way - is text or something else. The models that we have only have text inputs, so naturally it would be a token (but it could easily be a specialized non-text token like BOS/EOS if we trained the model that way). And the model can abstain from generating anything given any input - this is actually not uncommon for smaller models. GPT-3.5 and GPT-4 never seem to do it, but then again it's specifically fine-tuned for chat, i.e. always producing an output.
Long-term memory is a general problem with these things, but its short-term memory is its context window, so why would it have problem correlating events there? And for long-term memory, if it is implemented as an API under the hood that the model uses to store and query data, it would be trivial for it to timestamp everything according to the clock, no?