LLMs and Their Role in the Quest for AGI

It’s been clear for some time: LLMs (when used through prompting) can’t handle situations that are markedly different from what’s in their training data. In other words, LLMs don’t really have general intelligence in any significant way.

Where LLMs could be useful is as knowledge/routine repositories for a real AGI. They’re basically a memory - a representation of a dataset - and memory is a key part of intelligence. But don’t forget, intelligence isn’t just about memory.

To elaborate: LLMs are glorified pattern matchers. They’re great at tasks that closely resemble their training data, but they fall apart when faced with truly novel scenarios or abstract reasoning that goes beyond their dataset.

This limitation shows the huge gap between current AI and actual general intelligence. Sure, LLMs can spit out human-like text, but they lack the flexible, adaptive thinking that defines real intelligence.

That said, LLMs aren’t useless in the quest for AGI. They could serve as powerful knowledge bases for more advanced AI systems. Think of them as massive, queryable information stores.

But here’s the kicker: while having access to information (memory) is necessary for intelligence, it’s not enough on its own. Real intelligence also involves:

• Abstract reasoning
• Solving new problems
• Understanding and creating novel concepts
• Adapting to unfamiliar situations

The real challenge for AGI researchers is to build systems that can not only tap into vast information stores (like LLMs) but also manipulate and apply this info in flexible, context-appropriate ways across a wide range of domains.

So, where do we go from here? How do we bridge the gap between LLMs’ impressive but limited abilities and the more flexible, adaptive intelligence we’re shooting for with AGI? That’s the million-dollar question.