They are both different parts of the same problem. Prolog can solve logical problems using symbolism. ChatGPT cannot solve logical problems, but it can approximate human language to an astonishing degree. If we ever create an AI, or what we now call an AGI, it will include elements of both these approaches.
In “Computing Machinery and Intelligence”, Turing made some really interesting observations about AI (“thinking machines” and “learning machines” as they were called then). It demonstrates stunning foresight:
An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside… This is in clear contrast with normal procedure when using a machine to do computations: one’s object is then to have a clear mental picture of the state of the machine at each moment in the computation. This object can only be achieved with a struggle.
Intelligent behaviour presumably consists in a departure from the completely disciplined behaviour involved in computation, but a rather slight one, which does not give rise to random behaviour, or to pointless repetitive loops.
You can view ChatGPT and Prolog as two ends of the spectrum Turing is describing here. Prolog is “thinking rationally”: It is predictable, logical. ChatGPT is “acting humanly”: It is an unpredictable, “undisciplined” model but does exhibit very human-like behaviours. We are “quite ignoerant of what is going on inside”. Neither approach is enough to achieve AGI, but they are such fundamentally different approaches that it is difficult to conceive of them working together except by some intermediary like Subsumption Architecture.
This is what I expect too. And hope - LLMs are way too unpredictable to control important things on their own.
I often say LLMs are doing for natural language what early computation did for mathematics. There’s still plenty of mathy jobs computers can’t do, but the really repetitive ones are gone and somewhat forgotten - nobody thinks of “computer” as a title.
yeah, they’re really in the wrong to think that we’d have some technical advancement within the last 40 years and we should expect more than a probabilistic text generator. 🙃
I know how ML works, my comment was a persiflage on over-simplifying the topic of AI and logic. I originally marked it with an /s to indicate sarcasm, but I think this gets lost with newer generations, so now I replaced the /s with the upside down emoji (🙃) which also seems to indicate sarcasm.
That is correct, AI has always been able to do everything “right now in the future”. ML, NNs, GPT, etc. are all terms to distinguish the actual algorithms, from the abstract future goal of “AI”.
That is my thought as well. We’ll continuously change the definition of intelligence in order to preserve the notion that intelligence is inherently human. Until we can’t.
People now “ChatGPT isn’t real AI because it says dumb shit all the time”. People then: “Prolog is AI because it can solve logic problems”.
Something with moving goalposts or something
They are both different parts of the same problem. Prolog can solve logical problems using symbolism. ChatGPT cannot solve logical problems, but it can approximate human language to an astonishing degree. If we ever create an AI, or what we now call an AGI, it will include elements of both these approaches.
In “Computing Machinery and Intelligence”, Turing made some really interesting observations about AI (“thinking machines” and “learning machines” as they were called then). It demonstrates stunning foresight:
You can view ChatGPT and Prolog as two ends of the spectrum Turing is describing here. Prolog is “thinking rationally”: It is predictable, logical. ChatGPT is “acting humanly”: It is an unpredictable, “undisciplined” model but does exhibit very human-like behaviours. We are “quite ignoerant of what is going on inside”. Neither approach is enough to achieve AGI, but they are such fundamentally different approaches that it is difficult to conceive of them working together except by some intermediary like Subsumption Architecture.
This is what I expect too. And hope - LLMs are way too unpredictable to control important things on their own.
I often say LLMs are doing for natural language what early computation did for mathematics. There’s still plenty of mathy jobs computers can’t do, but the really repetitive ones are gone and somewhat forgotten - nobody thinks of “computer” as a title.
yeah, they’re really in the wrong to think that we’d have some technical advancement within the last 40 years and we should expect more than a probabilistic text generator. 🙃
Like this?
ChatGPT: 30 Year History
I know how ML works, my comment was a persiflage on over-simplifying the topic of AI and logic. I originally marked it with an
/sto indicate sarcasm, but I think this gets lost with newer generations, so now I replaced the/swith the upside down emoji (🙃) which also seems to indicate sarcasm.Is /s way older than I thought it was?
No need to
%s/\/s/🙃/gon my account… but the comment is ambiguous either way, and I think that video is pretty decent, so… 🤹Same old story: anything a computer can do, is an “algorithm”; anything it can not yet do, is “AI”… 🙄
if you listen to marketing of companies using Machine Learning, AI can do everything right now.
That is correct, AI has always been able to do everything “right now in the future”. ML, NNs, GPT, etc. are all terms to distinguish the actual algorithms, from the abstract future goal of “AI”.
That is my thought as well. We’ll continuously change the definition of intelligence in order to preserve the notion that intelligence is inherently human. Until we can’t.