I would think you really need to get into action almost non-stop, maybe a couple all-nighters, since 2024 is almost here ;)

Well over 40 years ago I certainly wasn't working on language models with only 16 kilobytes of memory and a 1MHz microprocessor.

No "high-level" or human-readable language anyway.

OTOH I always attempted to use the electronics further toward the limit of what it could provide, compared to average, and once the resource limit was reached (which occurred fairly quickly with only 16k) then the entire effort concentrated on maximizing the amount of machine learning that could be accomplished by code that fit in the memory.

There was no distraction preparing for more powerful hardware to come, it wasn't going to be coming during the time period needed.

No real artifical intelligence evolved, and there was nothing "general" about it.

The idea was to select & collect the desired most useful inferences from the raw data and make them available to the operator's natural intelligence for all the high-level decision-making.

Definitely no room to have data in memory, since it would waste the space you need for more thoughtful code. You can't have the resulting factors building up in memory either so they had to go to external storage as they were generated. Naturally to be used later by completely different code which is geared to process the rudimentary findings in relation to new data, and present that to the operator in order to enhance their pattern recognition and decision-making efforts.

I could only imagine what it would be like if memory came in megabytes rather than merely the lowly kilobytes.

One thing I think might still be true today, whatever amount of memory you have, you should be able to handle so much raw data that it makes the amount of memory look insignificant.

OTOH, if you can't highly leverage a naturally intelligent operator without some huge resource requirements, you might not be on the right track when it comes to maximizing hardware utilization.

And then there's the concept of analog noise amplification. You really need to be careful that there is nothing wrong or unrelated in the fundamental data set you are using at the time. Starting with a raw input signal, each stage of amplification will increase the amount of noise proportionally, and depending on the number of layers of amplification, any noise can cascade into top prominence when the desired signal is unfortunately weak. But the same level of noise-in-place-of-signal does not go away when it is dwarfed by a strong signal, the noise is merely masked during the high-signal passages but remains a considerable component.

Now when the raw data only gets one initial pass, anything that's missed the first time is lost forever, and if the missing nuance is something important that would seem like the type of thing that when a strong need is there, anything related to that nuance would be unreliable, incorrect, false, or downright hallucinatory if the performance was advanced enough.

GIGO is still the name of the game so I would think if it's custom training you have to step up to the plate and take the good with the bad. You've got to laboriously handle all the training data yourself anyway, so might as well take the opportunity to seriously babysit that data thoroughly in advance like you would never be able to do if you were only dealing with somebody else's already trained model.

Plus you can't usually take an adequately huge data set and in one pass remove all the undesired artifacts. And if one pass results in a processed dataset which can no longer be considered very huge at all, you've probaly lost too much valuable information and you may still not have eliminated all the undesirable noise.

This is somewhat analogous to lossy digital data compression, but focused on retaining only the most prominent meaning that can be gleaned from the data. As opposed to plain compression which retains the most prominent data regardless of meaning.

The more intelligently the raw input data is handled, the more realistically intelligent you can expect the final outcome to turn out.