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170 points PaulHoule | 1 comments | | HN request time: 0s | source
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measurablefunc ◴[] No.45120049[source]
There is a formal extensional equivalence between Markov chains & LLMs but the only person who seems to be saying anything about this is Gary Marcus. He is constantly making the point that symbolic understanding can not be reduced to a probabilistic computation regardless of how large the graph gets it will still be missing basic stuff like backtracking (which is available in programming languages like Prolog). I think that Gary is right on basically all counts. Probabilistic generative models are fun but no amount of probabilistic sequence generation can be a substitute for logical reasoning.
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vidarh ◴[] No.45121215[source]
> Probabilistic generative models are fun but no amount of probabilistic sequence generation can be a substitute for logical reasoning.

Unless you either claim that humans can't do logical reasoning, or claim humans exceed the Turing computable, then given you can trivially wire an LLM into a Turing complete system, this reasoning is illogical due to Turing equivalence.

And either of those two claims lack evidence.

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voidhorse ◴[] No.45125727[source]
Such a system redefines logical reasoning to the point that hardly any typical person's definition would agree.

It's Searle's Chinese Room scenario all over again, which everyone seems to have forgotten amidst the bs marketing storm around LLMs. A person with no knowledge of Chinese following a set of instructions and reading from a dictionary translating texts is a substitute for hiring a translator who understands chinese, however we would not claim that this person understands Chinese.

An LLM hooked up to a Turing Machine would be similar wrt to logical reasoning. When we claim someone reasons logically we usually don't imagine they randomly throw ideas at the wall and then consult outputs to determine if they reasoned logically. Instead, the process of deduction makes the line of reasoning decidedly not stochastic. I can't believe we've gotten to such a mad place that basic notions like that of logical deduction are being confused for stochastic processes. Ultimately, I would agree that it all comes back to the problem of other minds and you either take a fully reductionist stance and claim the brain and intellection is nothing more than probabilistic neural firing or you take a non-reductionist stance and assume there may be more to it. In either case, I think that claiming that LLMs+tools are equivalent to whatever process humans perform is kind of silly and severely underrated what humans are capable of^1.

1: Then again, this has been going on since the dawn of computing, which has always put forth its brain=computer metaphors more on grounds of reducing what we mean by "thought" than by any real substantively justified connection.

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1. bopjesvla ◴[] No.45126037[source]
The Chinese Room experiment has always been a hack thought experiment that was discussed in other forms before it was posited by Searle, most famously in Turing's "Can machines think?". Searle only superficially engaged with existing literature in the original Chinese Room paper. When he was forced to do so later on, Searle claimed that if you'd precisely simulate a Chinese human brain in a human-like robot, that brain still wouldn't be able to think or understand Chinese. Not a useful definition of thinking if you ask me.

From Wikipedia:

Suppose that the program simulated in fine detail the action of every neuron in the brain of a Chinese speaker.[83][w] This strengthens the intuition that there would be no significant difference between the operation of the program and the operation of a live human brain. Not a useful definition of thinking if you ask me.

Searle replies that such a simulation does not reproduce the important features of the brain—its causal and intentional states. He is adamant that "human mental phenomena [are] dependent on actual physical–chemical properties of actual human brains."[26]