[ExI] GPT-4 on its inability to solve the symbol grounding problem

Will Steinberg steinberg.will at gmail.com
Mon Apr 3 18:54:34 UTC 2023

This is insane.  You can't give a good reason for why our qualia aren't
also formed by pattern inference.  A leading theory of vision, the opponent
process,  involves exactly that.  There is legitimate proof that our
perception of color is not a result of individual particular signals, but
the differences and relations between multiple signals.  I don't see how
this is any difference besides the fact that one set of these signal
relations comes from the retina and one set comes from text.

On Thu, Mar 30, 2023, 4:02 PM Gordon Swobe via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> GPT-4 explained to me that it could not solve the symbol grounding for
> itself (I reported this in another thread). I asked it to explain the
> significance.
> My prompt: What is the significance of the fact that ChatGPT cannot solve
> the symbol grounding problem for itself and can only simulate that it has
> solved it? How does this put ChatGPT at a disadvantage compared to humans?
> GPT-4: The symbol grounding problem refers to the difficulty in connecting
> abstract symbols (such as words or concepts) to their real-world referents.
> While ChatGPT can simulate understanding by generating human-like text
> based on its training data, it doesn't possess the direct experience or
> sensory input that humans have to ground these symbols in reality.
> The significance of this limitation has several implications:
> Lack of true understanding: ChatGPT doesn't have a genuine understanding
> of the world, as it relies on patterns and associations found in the text
> it was trained on. It can't form new connections based on direct
> experience, which may result in limitations in problem-solving or abstract
> reasoning.
> Difficulty with context: Without grounding in real-world experiences,
> ChatGPT may struggle to fully understand context or cultural nuances. It
> may provide answers that are technically correct but inappropriate or
> irrelevant in a specific situation.
> Inability to learn from sensory input: Humans learn through direct
> experience and sensory input, which allows them to adapt and understand new
> situations better. ChatGPT can't update its knowledge through real-world
> experiences, making it less adaptable to novel contexts.
> Absence of common sense: Although ChatGPT can generate text that appears
> to show common sense understanding, it might still make errors that a human
> would easily avoid. This is because its responses are based on patterns in
> the data, rather than a true comprehension of the concepts.
> Limited creativity: ChatGPT's creativity is confined to the scope of its
> training data. While it can generate new text based on patterns it has seen
> before, it can't create truly novel ideas or solutions in the same way that
> humans, with their real-world grounding, can.
> In summary, the inability to solve the symbol grounding problem puts
> ChatGPT at a disadvantage compared to humans in terms of true
> understanding, adaptability, context-awareness, common sense, and
> creativity. However, it is essential to remember that ChatGPT is an AI
> language model designed to assist and augment human capabilities, not to
> fully replicate human cognition.
> ---
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