[FRIAM] absurd

[steve smith]

glen wrote:
> This is what I imagine y'all are doing when you chat with LLMs:
>
> Confusing ChatGPT With an Impossible Paradox
> https://youtu.be/1qbiCKrbbYc?si=V8U_mioTmlaDpynM
>
> At times, it smacks of a Monty Python skit.
>
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):

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).

that's all (y'all)!

------------------------------------------------------------------------
“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?”
00:14


            ChatGPT said:

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.

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.

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.

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.

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.

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.


          You said:

“How practical would it be to create such a thing?”
00:03


            ChatGPT said:

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.

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.

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