Ok, I’m not going to go point by point, as this is getting too long. All I’d say is remember where the model for ML came from (McCulloch & Pitts), and that this is the worst AI will ever be.
If this is truly a jump across S-curves in utility, it’s bound to be slightly worse than other methods to begin with. Many of the arguments against the current approach sound like the owners of a hot air balloon business arguing with the Wright brothers.
The whole idea of artificial neurons (from McCulloch and Pitts) sounds for me like modelling a wing-flapping mechanism for airplanes. You can get something fun out of it, but I think that further progress will focus on reserve engineering the software (language as a faculty) instead of trying to mimic the underlying machine (human brains).
that this is the worst AI will ever be.
Probably? I think so, at least. I’m not too eager to make a “hard” statement about future tech, though.
Note that my criticism is not towards the development of language models and natural language processing, but specifically against the current state of art technology (LLM).
Many of the arguments against the current approach sound like the owners of a hot air balloon business arguing with the Wright brothers.
That doesn’t say much about the validity of the arguments. And I bet that a lot of people voicing arguments against Dumont or the Wight brothers were actually correct.
Definitely LLMs have been over promised and/or misrepresented in mainstream media, but even in the last few months their utility is increasing. I’m a big advocate of finding ways to use them to enhance people (thinking partner not replacement for thinking). They are most certainly a tool, and you need to know their limitations and how to use them.
From experience working with naive end users, they are anthropomorphising based on how the models have been reported and that’s definitely not helpful.
As the models get more and more capable (and I’m pretty happy to make that prediction), will they reach a point where they are indistinguishable from the output of a real person? That will give us some challenges. But the interesting thing for me is that when that happens, and the AI can write that report you were paying someone to write, what was the point of the report? You could argue they were some kind of terrible UBI and we’ll end up with just the pointless output without the marginal benefit of someone’s livelihood. That needs a bigger rethink.
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Ok, I’m not going to go point by point, as this is getting too long. All I’d say is remember where the model for ML came from (McCulloch & Pitts), and that this is the worst AI will ever be.
If this is truly a jump across S-curves in utility, it’s bound to be slightly worse than other methods to begin with. Many of the arguments against the current approach sound like the owners of a hot air balloon business arguing with the Wright brothers.
The whole idea of artificial neurons (from McCulloch and Pitts) sounds for me like modelling a wing-flapping mechanism for airplanes. You can get something fun out of it, but I think that further progress will focus on reserve engineering the software (language as a faculty) instead of trying to mimic the underlying machine (human brains).
Probably? I think so, at least. I’m not too eager to make a “hard” statement about future tech, though.
Note that my criticism is not towards the development of language models and natural language processing, but specifically against the current state of art technology (LLM).
That doesn’t say much about the validity of the arguments. And I bet that a lot of people voicing arguments against Dumont or the Wight brothers were actually correct.
Definitely LLMs have been over promised and/or misrepresented in mainstream media, but even in the last few months their utility is increasing. I’m a big advocate of finding ways to use them to enhance people (thinking partner not replacement for thinking). They are most certainly a tool, and you need to know their limitations and how to use them.
From experience working with naive end users, they are anthropomorphising based on how the models have been reported and that’s definitely not helpful.
As the models get more and more capable (and I’m pretty happy to make that prediction), will they reach a point where they are indistinguishable from the output of a real person? That will give us some challenges. But the interesting thing for me is that when that happens, and the AI can write that report you were paying someone to write, what was the point of the report? You could argue they were some kind of terrible UBI and we’ll end up with just the pointless output without the marginal benefit of someone’s livelihood. That needs a bigger rethink.
In fact, see this for some similar hyperbole and sentiment.