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But this is the thing, that requires expertise and infrastructure. "AGI" LLM's can handle all the nuance, quite simply, they don't need specialist infrastructu
by KoolKat23 2y ago
But this is the thing, that requires expertise and infrastructure.
"AGI" LLM's can handle all the nuance, quite simply, they don't need specialist infrastructure or specialist programming, way cheaper, upfront cost. Way easier to scale.
Individual employees can ask it for specific things that make their lives easier and it'll give it to them/or do it. No need to ask your manager, motivate for funding and hire an engineer/purchase new software/equipment.
- megadal 2y agoIf I'm not mistaken LLMs contain NER and text classification pipelines so you can leverage the exact same infrastructure. I can't see how AGI from that perspective _isnt_ just an LLM routing tasks to more specialized NLP models to be honest. Unless you're proposing that a bigger LLM (training data, neural network, etc..) will develop the emergent capability to accomplish this without the need for specialized agents. In which case, I can't see how that would perform more efficiently than an LLM routing requests appropriately amongst agents, as it necessarily requires processing much more data. But even if somehow it did perform more efficiently while needing much more data, I don't think the no-agent AGI approach will cover all use cases appropriately. It might be an easier drop in solution, but if I need it to behave a specific way in a specific context, I don't see how an AGI is going to consistently do that more accurately than fine tuning a model for the specific use case and having an LLM routing to it.
- KoolKat23 2y agoI agree that's probably how they will work best.my point was that AGI allows everyone to leverage those tools without the steep learning curve or having to custom build each component for each specific use case themselves.