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by prirun
40 days ago
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I don't know much about the AI field, but it seems to me that trying to train any model to be all things to all people is a really dumb idea. It requires huge financial resources and is causing extreme shortages/market distortions in
any resource used by an AI company - RAM, SSDs, data centers, etc. In the real world, you don't hire a plumber and expect him to also do your landscaping, fix your car, and tailor your clothes. It would seem like a much better use of resources if I could download an app that specialized in shell, Python, and C coding for example, or maybe even that would be 3 apps that communicated. Maybe I could even run them on a regular machine with 16GB of RAM. I don't need one huge model that can do that and code in Fortran, COBOL, and Lisp. As humans, we've done pretty well by specializing. I hope this gets explored more with smaller, focused AI models vs the current path of one model to rule them all that can only be run in a data center the size of a country. |
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I would daresay for "coding tasks", you actually _want_ a model that can code "in all languages".
Sure, it might be that outdated language XYZ is really useless to you or the task you want, but being exposed to their limits, philosophy and concerns across environment, framework and organization, among other things, means for example you get insights of your problems from other areas and points of view.
That's afterall how we got Newtonian physics and calculus, right? A person studying physics someday noticed how the "math of the day" wasn't able to calculate some results without a lot of elbow grease. He then "found" the "missing math" and with it was able to generalize what at the time was considered a bunch of isolated phenomena into a cohesive corpus of knowledge.
So for example, I want my code to have mechanical sympathy like Fortran; well defined input/output interfaces, and not-interweaved control structures, like COBOL; stateless, side-effects-free business logic like Lisp.