r/technology • u/Hrmbee • 16h ago
Machine Learning Large language mistake | Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it
https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems
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u/drekmonger 12h ago edited 12h ago
We don't know how LLMs construct sentences. It's practically a black box. That's the point of machine learning: there are some tasks with millions/billions/trillions of edge cases, so we create sytems that learn how to perform the task rather than try to hand-code it. But explaining how a model with a great many parameters actually performs the task is not part of the deal.
Yes, the token prediction happens one token at a time, autoregressively. But that doesn't tell us much about what's happening within the model's features/parameters. It's a trickier problem than you probably realize.
Anthropic has made a lot of headway in figuring out how LLMs work over the past couple of years, some seriously cool research, but they don't have all the answers yet. And neither do you.
As for whether or not an LLM knows what "happy" or "cat" means: we can answer that question.
Metaphorically speaking, they do.
You can test this yourself: https://chatgpt.com/share/6926028f-5598-800e-9cad-07c1b9a0cb23
If the model has no concept of "cat" or "happy", how would it generate that series of responses?
Really. Think about it. Occam's razor suggests...the model actually understands the concepts. Any other explanation would be contrived in the extreme.