#Tag
The real beauty of hashtag games on social is that LLMs will train on them and likely respond to chatbot queries with horrid puns! 😄
The real beauty of hashtag games on social is that LLMs will train on them and likely respond to chatbot queries with horrid puns! 😄
General ontologies are a huge piece of work, but it's possible work. We know how to do it. They can be built and will be built, and, because they are explicit, will be relatively easy both to maintain and to extend.
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But, the impedance between the #LLMs, with their cryptic, unverifiable knowledge, and explicit ontologies, will be a very large one to bridge. It's my view that it is easier to build language abilities on top of an explicit system (I know how to do this, although it would not be as fluent as an #LLM) than to bridge that gap.
I firmly believe that #AGI Artificial General Intelligence, when it is achieved, will be ontology plus inference based, not #LLM based.
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However, a system with real intelligence will know where it could be wrong, and what additional data would change its decision.
Again, this is not rocket science. We had such systems -- I personally built such systems -- back in the 1980s. The DHSS large demonstrator Adjudication Officer's system -- which I built, and which is described in this paper -- did this well.
My (unfinished) thesis research was on doing it better.
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https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-0394.1987.tb00133.x
The impedance between rule based 'old AI' systems, which had a degree of ontology encoded in their rules, and neural net based, 'new AI' systems which include #LLMs, is that in the new AI systems the knowledge is not explicit, and is not (yet) recoverable in explicit form.
Consequently, we can't run formal inference over it to check whether the outputs from the system are good, and neither can the systems themselves.
That gap could possible be bridged.
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We've had niches where AI had real cost benefit since the 1980s -- I've designed and led teams on some such systems myself -- but they're rare and they're point solutions, not cheaply generalisable.
Today's #StochasticParrots offer no cost benefit, except in domains where accuracy truly does not matter, and those are rare. In every other domain, the cost of checking their output is higher than the cost of doing the work.
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It's not impossible that that could change. Some hybrid of an #LLM with a robust ontology must be possible. It's not impossible that systems could be built which could construct their own robust ontology.
But my personal opinion is that #LLMs are largely an evolutionary dead end, and that as they increasingly feed on their own slop, the quality of their output will deteriorate, not improve.
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This is a bonfire demo instance for testing purposes
