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<div class="moz-cite-prefix">glen wrote:<br>
</div>
<blockquote type="cite"
cite="mid:f7c81d89-de47-465b-9852-05e1b467b7c6@gmail.com">This is
what I imagine y'all are doing when you chat with LLMs:
<br>
<br>
Confusing ChatGPT With an Impossible Paradox
<br>
<a class="moz-txt-link-freetext" href="https://youtu.be/1qbiCKrbbYc?si=V8U_mioTmlaDpynM">https://youtu.be/1qbiCKrbbYc?si=V8U_mioTmlaDpynM</a>
<br>
<br>
At times, it smacks of a Monty Python skit.
<br>
<br>
</blockquote>
<p>Loved the skit... and/but it made me think about the broader
implications which made me "look in the horse's mouth" for an
answer (to mix a parable or a metaphor):</p>
<p>The point was to take GPT beyond trying to relate it's own
"gathered rumours" about how "a human claps hands" and consider
what a (more broadly capable) AI system might need to do to get
one step closer to actually "understanding" the clapping of
hands. Many here may discount Hawkin's 1K brains model so this
may be an(other) empty gesture but I find it a compelling model
for thinking about how spatio-temporal (esp. tactile to visual)
(functional) understanding of the physical world that brains with
neocortices do. And maybe how that translates into understanding
abstract concepts (a bit more of a stretch).</p>
<p>that's all (y'all)!<br>
</p>
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<hr width="100%" size="2">“What would it take
for an AI to have an internal model, much like
Jeff Hawkins' 1,000 Brains and Cortical
Columns, to emulate the clapping of hands?”</div>
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<h6 class="sr-only">ChatGPT said:</h6>
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<p data-start="0" data-end="183" class="">To
emulate something like the clapping of hands
using an internal model inspired by Jeff
Hawkins' "Thousand Brains" theory and cortical
columns, an AI would need a few key components.</p>
<p data-start="185" data-end="508" class="">First,
it would require a distributed representation of
the environment and the concept of hands,
allowing it to form a model of how hands move
and interact. This involves creating multiple
parallel models (like cortical columns) that
each predict and update their understanding of
the hand's position, shape, and movement.</p>
<p data-start="510" data-end="755" class="">Second,
the AI would need a sensorimotor integration
mechanism, enabling it to predict and adjust its
model based on sensory feedback—just as humans
constantly refine their understanding of their
hand's position through touch and
proprioception.</p>
<p data-start="757" data-end="972" class="">Third,
it would need a timing and sequence prediction
capability to anticipate the sequence of events
that constitute a hand clap: bringing the hands
together, the moment of impact, and the
resulting sound and feel.</p>
<p data-start="974" data-end="1162" class="">Finally,
it would need a way to compare its predictions
to the actual outcome and refine its internal
models accordingly, improving its understanding
and emulation of the action over time.</p>
<p data-start="1164" data-end="1334" class="">In
essence, it would involve a complex, layered
network of predictive models working in tandem
to generate and refine a coherent and dynamic
representation of the action.</p>
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<h5 class="sr-only">You said:</h5>
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<div class="whitespace-pre-wrap">“How practical
would it be to create such a thing?”</div>
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<h6 class="sr-only">ChatGPT said:</h6>
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<p data-start="0" data-end="415" class="">Creating
such a system is theoretically possible, but
it's a massive challenge in practice. Mimicking
the "Thousand Brains" approach would require a
highly advanced architecture capable of running
many parallel models, each constantly predicting
and updating in real time. This would
necessitate significant computational resources,
advanced algorithms for sensorimotor
integration, and a robust feedback mechanism.</p>
<p data-start="417" data-end="880" class="">While
some aspects of this are already being explored
in neuroscience-inspired AI and robotics,
achieving the full complexity of human-like
sensorimotor predictions and adaptability is
still at the frontier of research. Over time, as
our understanding of brain-like computation and
hardware capabilities improve, building
something close to this concept could become
more practical. For now, though, it's more of a
long-term goal rather than an immediate reality.</p>
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