The problem is even more fundamental: Today's models stop learning once they're deployed to production.
There's pretraining, training, and finetuning, during which model parameters are updated.
Then there's inference, during which the model is frozen. "In-context learning" doesn't update the model.
We need models that keep on learning (updating their parameters) forever, online, all the time.
> models that keep on learning
These will just drown in their own data, the real task is consolidating and pruning learned information. So, basically they need to 'sleep' from time to time. However, it's hard to sort out irrelevant information without a filter. Our brains have learned over Milenial to filter because survival in an environment gives purpose.
Current models do not care whether they survive or not. They lack grounded relevance.
And, yep! A lot of people absolutely believe it will and are acting accordingly.
It’s honestly why I gave up trying to get folks to look at these things rationally as knowable objects (“here’s how LLMs actually work”) and pivoted to the social arguments instead (“here’s why replacing or suggesting the replacement of human labor prior to reforming society into one that does not predicate survival on continued employment and wages is very bad”). Folks vibe with the latter, less with the former. Can’t convince someone of the former when they don’t even understand that the computer is the box attached to the monitor, not the monitor itself.
Thomas theorem is a theory of sociology which was formulated in 1928 by William Isaac Thomas and Dorothy Swaine Thomas.
https://en.wikipedia.org/wiki/Thomas_theorem