But how will that prove that it's more than a stochastic parrot, honestly curious?
Isn't it just like any kind of conversion or translation? Ie. a relationship mapping between diffrent domains and just as much parroting "known" paths between parts of different domains?
If "sun" is associated with "round", "up high", "yellow","heat" in english that will map to those things in SVG or in whatever bizarre format you throw at with relatively isomorphic paths existing there just knitted together as a different metamorphosis or cluster of nodes.
On a tangent it's interesting what constitutes the heaviest nodes in the data, how shared is "yellow" or "up high" between different domains, and what is above and below them hierarchically weight-wise. Is there a heaviest "thing in the entire dataset"?
If you dump a heatmap of a description of the sun and an SVG of a sun - of the neuron / axon like cloud of data in some model - would it look similar in some way?
Isn't it just like any kind of conversion or translation? Ie. a relationship mapping between diffrent domains and just as much parroting "known" paths between parts of different domains?
If "sun" is associated with "round", "up high", "yellow","heat" in english that will map to those things in SVG or in whatever bizarre format you throw at with relatively isomorphic paths existing there just knitted together as a different metamorphosis or cluster of nodes.
On a tangent it's interesting what constitutes the heaviest nodes in the data, how shared is "yellow" or "up high" between different domains, and what is above and below them hierarchically weight-wise. Is there a heaviest "thing in the entire dataset"?
If you dump a heatmap of a description of the sun and an SVG of a sun - of the neuron / axon like cloud of data in some model - would it look similar in some way?