The cool thing about AI that I'm seeing as an outsider/non-academic, is that it's relatively cheap to clone. Sleeping/resting could be done by a "clone" and benefits could be distributed on a rolling schedule, right?
I can't be certain, I'm not at all an AI engineer or math guy, but I think at the "wake up" point you equalize instances.
Like during 'sleep' some list of functions/operations `m` are applied to model weights `n` producing a new model, `n + 1`.
Wouldn't you just clone `n + 1`, send it to work, and start a new training run `m + 1` to make `n + 2`?
This was my first idea as well. Keep training continuously and redeploy clones after each cycle. From a layman perspective this seems reasonable :thinking:
You can't realistically keep training the same model forever, or it will start forgetting things it knew before. The proper name for this is "catastrophic forgetting".
But the clone couldn't run without sleeping? So that's more of a teammate than a clone.
1 works while the other sleeps and then swap.
If this method ever worked our current alignment methods get chucked out the window those would be two completely different AI.