I think AI is neat.
this post was submitted on 05 Feb 2024
667 points (87.9% liked)
Memes
45660 readers
1142 users here now
Rules:
- Be civil and nice.
- Try not to excessively repost, as a rule of thumb, wait at least 2 months to do it if you have to.
founded 5 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
They're kind of right. LLMs are not general intelligence and there's not much evidence to suggest that LLMs will lead to general intelligence. A lot of the hype around AI is manufactured by VCs and companies that stand to make a lot of money off of the AI branding/hype.
I find this line of thinking tedious.
Even if LLM's can't be said to have 'true understanding' (however you're choosing to define it), there is very little to suggest they should be able to ~~understand~~ predict the correct response to a particular context, abstract meaning, and intent with what primitive tools they were built with.
If there's some as-yet uncrossed threshold to a bare-minimum 'understanding', it's because we simply don't have the language to describe what that threshold is or know when it has been crossed. If the assumption is that 'understanding' cannot be a quality granted to a transformer-based model -or even a quality granted to computers generally- then we need some other word to describe what LLM's are doing, because 'predicting the next-best word' is an insufficient description for what would otherwise be a slight-of-hand trick.
There's no doubt that there's a lot of exaggerated hype around these models and LLM companies, but some of these advancements published in 2022 surprised a lot of people in the field, and their significance shouldn't be slept on.
Certainly don't trust the billion-dollar companies hawking their wares, but don't ignore the technology they're building, either.
You are best off thinking of LLMs as highly advanced auto correct. They don't know what words mean. When they output a response to your question the only process that occurred was "which words are most likely to come next".