[ot][spam][crazy] Quickly autotranscribing xkcd 4/1 correctly

[Undiscussed Horrific Abuse, One Victim of Many]

uhhhhhhhhhh okay my idea was to have two different structures that
formed confidence metrics around the data, then to combine the
confidence metrics so as to improve the structures, in feedback.

would this work with limited labels?

the model generates output, possibly with confidence metrics. the
labels are few and correct, and provided in an order over time.

something's missing here. there's only one algorithm. (part of the
issue is i don't remember my idea).

for inclusion, here is a very basic idea i haven't heard mentioned
much: the model could treat each label as a new train/test set, and
keep training on the old labels until it improved at the next labels.
this will only work if done a certain way, it doesn't work as an
overall strategy because the model isn't good at 'going back' and
'undoing' things it learned wrongly.

the 'going back' and 'undoing' is often addressed by having a lot of
diverse data. lots and lots, as diverse as possible.

if we could consider the properties the model is learning a little, we
could maybe emulate something similar on a small scale. for example,
just a single word from each set of labels could be considered, and
the model would treat surrounding words as test material. this aligns
things a little better.

mm.

anyway, it seems to help to learn to finetune one of these
speech-to-text models around data a little. maybe i can see if i can
pull that off, just as an experiment.