2 HN users recommended this podcast episode2 months2024score 2.5
Years with an HN recommendation, 2007–2026
HN cites it for the argument that LLMs capture language form but not meaning, and for how the brain's language network works.
Lex Fridman interviews MIT psycholinguist Edward Gibson on human language, syntax, grammar and how large language models relate to them. The conversation runs nearly three hours.
“I highly recommend listening to this Lex Friedman podcast episode” (HN, 2024)
'It's impossible to plan without an internal monologue.' - Sorry, but I disagree with this. I have no 'internal voice' or monologue - whenever I see a problem, my brain actually and fully models it using images. I believe 25% of the population doesn't have the internal monologue which you're referring to and this has been tested and confirmed. I highly recommend listening to this Lex Friedman podcast episode to get a full grasp on the complexities of modelling language and general modelling present in the human brain: [this video]
Covers complexities of modelling language in the human brain
If you are interested in this topic, I suggest watching the conversation between Edward Gibson and Lex Fridman. In the middle of the conversation [1] Edward talks about how there is a "human language comprehension network" within human brain, which gets activated only when we read or speak in any human languages, but nothing else. For example for those speaking multiple languages, reading or writing in any of those languages activates the network. But reading gibberish or computer code, neither activate the network. [1] [this video]
Explains brain language network that ignores code and gibberish
I find that explaining errors and factual mistakes in LLMs or AI in general as hallucinations to be counter-productive. I don't know if the field has landed on a specific definition of what hallucination is, but I mostly associate it with garbled output from tokens like 'SolidGoldMagicKarp'[0] and 'davidjl'[1]. My understanding is that AI models like GPT have been able to convincingly convey the form of language, but not its meaning. It looks and sounds like how a human would communicate, but the AI is unable to imbue meaning into the words and sentences it produces. My knowledge of this comes from Lex Friedman's episode with Edward Gibson [2]. I think that's the fundamental issue with LLMs at the moment. It has managed to mostly exit the uncanny valley because, as far as most people are concerned, the text produced could just as well have been made by a human. I think this lends some credibility to the text produced by the LLM, because more or less all text ever produced has had a human behind it. This is no longer the case and as such there is bound to be a transitional period where we learn how to deal with this new technology. [0]: [link] [1]: [link] [2]: [this video]
Source for claim that LLMs convey language form but not meaning
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