What Makes LLMs Different from AGI?

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Asked By CuriousCat123 On

I've been hearing a lot about artificial general intelligence (AGI) being predicted in the future, but to me, today's large language models (LLMs) seem quite general. I'm curious about what others think: how general does a model need to be to qualify as AGI? What specific abilities should it have?

5 Answers

Answered By TechNerd42 On

Current LLMs can't drive themselves toward goals or act independently. They require careful guidance. Until an AI can operate autonomously and set its own objectives, it won't be classified as AGI. It's really about getting the AI to act independently in complex environments.

BrainyBot99 -

I agree! I think true AGI has to be able to perform tasks strictly on its own, without constant human oversight. It’s kind of exciting to think about how close we might be to that.

CodeWhiz10 -

Exactly, the advancements we've seen are impressive but don't fully reach AGI capabilities yet. We need something that learns and adapts like a human.

Answered By FutureSeeker88 On

In my view, AGI should replace humans in cognitive tasks, not just respond to prompts. Current models may excel at certain tasks, but real AGI has to demonstrate broad cognitive abilities and creativity, which LLMs currently lack.

Answered By SkepticalObserver On

Many experts argue that current models are almost there, but not quite AGI. They perform well under specific tasks but struggle with broad cognitive functions and adapting to novel situations. Genuine AGI should be able to approach human-like cognitive flexibility.

QuestioningMind11 -

Absolutely! The benchmarks for AGI keep shifting as technology advances, but we need something that can think more like a human rather than just regurgitating trained information.

LogicGuru7 -

And don't forget, AGI needs to self-correct and learn from its experiences in real time, which LLMs just can’t do.

Answered By RationalThinker3 On

The term AGI is ambiguous, but I lean on the traditional definition—an AI that mimics human-like learning and reasoning. LLMs generate outputs based on learned data but can’t learn in real-time or reason like a human can, which disqualifies them from being termed AGI.

Answered By DebaterDan On

I believe AGI should have continuous learning capabilities and the ability to understand and create novel ideas. LLMs fall short of this since they can’t learn on the fly and are limited to what they’ve been trained on. Effective AGI should adapt and learn independently across various domains.

DataDreamer22 -

Right, and until they can demonstrate a true understanding of concepts rather than just patterns, we are still a ways from real AGI.

InquisitiveMinds18 -

Totally! It’s also about being able to handle completely new tasks without needing retraining or a huge dataset to reference. Until LLMs can do that, they're not AGI.

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