Now that it's been corraled by sci-fi and marketers, we are free to come up with new metaphors for algorithms that reliably replace human effort. Metaphors which don't smuggle in all our ignorance about intelligence and personhood. I ended up feeling pretty happy about that.
The reality is that our labeling of something as artificial, general, or intelligent is better understood as a social fact than a scientific fact - even if purely the role of operationalization of each of these is a free parameter in their respective groundings which makes it near useless when taking them as "scientifically" measurably qualities. Any scientist who assumes an operationalization without admitting such isn't doing science - they may as well be astrology at that point.
intelligence is what allows one to understand phrases and then construct meaning from it. e.g. the paper is yellow. AI will need to have a concept of paper and yellow. and the to be verb. LLMs just mash samples and form a basic map of what can be throw in one bucket or another with no concept of anything or understanding.
basically, AI is someone capable of minimal criticism. LLMs are someone who just sit in front of the tv and have knee jerk reactions without an ounce of analytical though. qed.
Ditto the sentiments. What about other machine learning modalities, like image detection? Will I need a license for my mask rcnn models?. Maybe it is just me, but the whole thing reeks of control
That doesn't clarify anything, you've ever only shuffled the confusion around, moved it to 'understand' and 'meaning'. What does it mean to understand yellow? An LLM or another person could tell you things like "Yellow? Why, that's the color of lemons" or give you a dictionary definition, but does that demonstrate 'understanding', whatever that is?
It's all a philosophical quagmire, made all the worse because for some people its a matter of faith that human minds are fundamentally different from anything soulless machines can possibly do. But these aren't important questions anyway for the same reason. Whether or not the machine 'understands' what it means for paper to be yellow, it can still perform tasks that relate to the yellowness of paper. You could ask an LLM to write a coherent poem about yellow paper and it easily can. Whether or not it 'understands' has no real relevance to practical engineering matters.
That's one, out of many, definitions of "intelligence". But there's no particular reason to insist that that is the definition of intelligence in any universal, objective sense. Especially in terms of talking about "artificial intelligence" where plenty of people involved in the field will allow that the goal is not necessarily to exactly replicate human intelligence, but rather simply to achieve behavior that matches "intelligent behavior" regardless of the mechanism behind it.
That's not the definition of AI or intelligence
You're letting your understanding of how LLMs work bias you. They may be at their core a token autocompleter but they have emergent intelligence
What kind of ‘understanding’ are you looking for?