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The use case you described can be achieved with a subset of NLP, and has basically already been possible for years. Named-Entity Recognition (NER) and Text Cla

The use case you described can be achieved with a subset of NLP, and has basically already been possible for years. Named-Entity Recognition (NER) and Text Classification will allow you to figure out what kind of text you're looking at and extract structured data. LLMs are not good at this because they're not specialized for it, but you can build a specialized NER model to extract custom entities from unstructured data today. That said, I don't really think this is some yet untapped potential of AI so much as an area of ML that just hasn't been applied enough. ETA: also, in general I think AI is going in the direction of basically just having an LLM route tasks to more specialized ML models (for corporate tasks at least). That's what Google's Vertex AI agents sort of do (and I am guessing the GPT 4 agents as well).

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 ago
  If 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 ago
    I 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.
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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 ago
If 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 ago
  I 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.

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