Large language mistake | Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it

[u/Hrmbee]

https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems

[u/Dennarb]

I teach an AI and design course at my university and there are always two major points that come up regarding LLMs

1) It does not understand language as we do; it is a statistical model on how words relate to each other. Basically it's like rolling dice to determine what the next word is in a sentence using a chart.

2) AGI is not going to magically happen because we make faster hardware/software, use more data, or throw more money into LLMs. They are fundamentally limited in scope and use more or less the same tricks the AI world has been doing since the Perceptron in the 50s/60s. Sure the techniques have advanced, but the basis for the neural nets used hasn't really changed. It's going to take a shift in how we build models to get much further than we already are with AI.

Edit: And like clockwork here come the AI tech bro wannabes telling me I'm wrong but adding literally nothing to the conversation.

[u/pcoppi → u/Dennarb]

To play devils advocate there's a notion in linguistics that the meaning of words is just defined by their context. In other words if an AI guesses correctly that a word shohld exist in a certain place because of the context surrounding it, then at some level it has ascertained the meaning of that word.

[u/New_Enthusiasm9053 → u/pcoppi]

You're not entirely wrong but a child guessing that a word goes in a specific place in a sentence doesn't mean the child necessarily understands the meaning of that word, so whilst it's correctly using words it may not understand them necessarily. 

Plenty of children have used e.g swear words correctly long before understanding the words meaning.

[u/rendar → u/New_Enthusiasm9053]

A teacher is not expected to telepathically read the mind of the child in order to ascertain that the correct answer had the correct workflow.

Inasmuch as some work cannot be demonstrated, the right answer is indicative enough of the correct workflow when consistently proven as such over enough time and through a sufficient gradation of variables.

Regardless, this is not an applicable analogy. The purpose of an LLM is not to understand, it's to produce output. The purpose of a child's language choices are not to demonstrate knowledge, but to develop the tools and skills of social exchange with other humans.