this post was submitted on 25 Nov 2023
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Machine Learning
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Research papers have also observed diminishing returns issues as models grow.
Hell maybe even GPT-4 was hit by this and that's why GPT-4 is not a single giant language model but running a mixture of experts design of eight 220B models trained for subtasks.
But I think even this architecture will run into issues and that it's more like a crutch. I mean, you'll eventually grow each of these subtask models too large and might need to split them as well, but this might mean you run into too small/niche fields per respective model and that sounds like the end of that road to me.
I mean, that is literally how any form of AGI will work. No one in the field has ever thought one model will be capable of reaching AGI. All these models are highly specialized for the task in which they are trained. Any move towards an AGI will be getting many of these highly specialized AI's to work in conjunction with one another. Much like how our own brains work.