Connectionists: Chomsky's apple

[Iam Palatnik]

I feel as if I have heard the argument that LLMs or other generative models
are retrieving text (or images, in the case of Stable Diffusion) from a
database, but I don't understand the origin of this argument. Isn't a model
like ChatGPT, at the end of the day, just a list of weight matrices? The
175 billion weights in those matrices surely can't directly hold the
trillions of tokens seen during training in a retrievable format, so isn't
this enough to say that the model is almost surely not doing a direct
retrieval of text from within itself? I might have misunderstood the
wording.

When such models generate input that is different from the training data by
whatever metric, what is the main obstacle in saying that they created
something new?
When the model correctly answers to tasks it has never previously seen, in
well formed language, what is the main obstacle in saying it understood
something?
When a dog reacts to a command and sits or fetches, or when Alexa reacts to
a command and turns the lights on, what sets these two scenarios
significantly apart in terms of 'understanding'? And then, would it be too
unfair to say ChatGPT maybe understands English better than both the dog
and Alexa?



On Thu, Mar 9, 2023 at 4:47 AM Stefan C Kremer <skremer at uoguelph.ca> wrote:

> One of the criticisms against John Searle’s argument (
> https://en.wikipedia.org/wiki/Chinese_room) has always been that it
> wouldn’t be possible to construct a book comprehensive enough to answer all
> the queries, or that it would take too long to produce an output.  Chat GPT
> shows that we have at least approached that limitation (perhaps not truly
> overcome it…yet).
>
> The question posed by Searle (and answered with a “yes” by Chomsky in his
> thinking about counterfactuals, causal explanation, and thinking) is: is
> there a difference between understanding and simulated understanding?
>
> I don’t know how we could ever answer this question (an to me that’s the
> important point), but it seems that Searle’s thought experiment becomes
> more relevant, now that a feasible implementation can be constructed, than
> when it was originally proposed.
>
> Stefan
>
>
> On Mar 8, 2023, at 12:25 PM, Terry Sejnowski <terry at salk.edu> wrote:
>
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>
>
> I have always been impressed with Chomsky's ability to use plausible
> arguments to make his case even when they were fallacious.
>
>
> https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html#commentsContainer
>
> "Here’s an example. Suppose you are holding an apple in your hand. Now you
> let the apple go. You observe the result and say, “The apple falls.” That
> is a description. A prediction might have been the statement “The apple
> will fall if I open my hand.” Both are valuable, and both can be correct.
> But an explanation is something more: It includes not only descriptions and
> predictions but also counterfactual conjectures like “Any such object would
> fall,” plus the additional clause “because of the force of gravity” or
> “because of the curvature of space-time” or whatever. That is a causal
> explanation: “The apple would not have fallen but for the force of
> gravity.” That is thinking."
>
> His apple example sounded convincing, so I tested GPT on it:
>
> Me:  If I hold an apple in my hand and let it go what will happen?
>
> GPT:  The apple will fall to the ground due to the force of gravity.
>
> Me: What about any such object?
>
> GPT: Any object released from a person's hand will fall to the ground due
> to the force of gravity.
>
> Me:  What would happen if there wasn't a force of gravity?
>
> GPT:  If there were no force of gravity, objects would not be able to stay
> on the ground and would float away in all directions
>
> Chomsky defined thinking by the correct answers to these questions.
>
> Alas, the tide has turned.
>
> Terry
>
> -----
>
>
>
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