This article explains what's missing in terms of two kinds of complexity that oppose: Shannon complexity vs. Kolmogorov complexity.
It introduces the opposition by an example of driving the value of pi as decimal number, which has no pattern and high complexity, and a formula for deriving pi that does have a pattern with low complexity, then observing that mind can work from the patternless high-complexity back to the patterned low-complexity without prior examples, while AI can't.
LLMs encode and retrieve patterns in the training data, and doing so can connect data to the terminology of known principle, but mind can observe inconsistencies in data and to reason from first principles to resolve the inconsistency.
The distinction between these two modes can seem blurry as AI can traverse the patterns of the known in ways that are extraordinarily revealing, but it's not structured to reason about the unknown.
Inference is not sufficient for reason.
For example, a conventional algorithm can search for patterns in text at a scale many orders of magnitude beyond a mind's capacity, and this can be very revealing, but to do so this algorithm need not read the text with comprehension.
Regarding the question: can genAI be enhanced to reason? The answer is assumed to be "no", due to the categorical opposition of the two kinds of complexity and the lack of understanding of structures within genAI to handle the reasoning.
Read the article, which includes other examples including a jump from Newtonian to Einstein physics in the history of astronomy, and a noodling on how to talk about the edge of the unknowable in AI.
https://medium.com/@vishalmisra/shannon-got-ai-this-far-kolm...
This article explains what's missing in terms of two kinds of complexity that oppose: Shannon complexity vs. Kolmogorov complexity.
It introduces the opposition by an example of driving the value of pi as decimal number, which has no pattern and high complexity, and a formula for deriving pi that does have a pattern with low complexity, then observing that mind can work from the patternless high-complexity back to the patterned low-complexity without prior examples, while AI can't.
LLMs encode and retrieve patterns in the training data, and doing so can connect data to the terminology of known principle, but mind can observe inconsistencies in data and to reason from first principles to resolve the inconsistency.
The distinction between these two modes can seem blurry as AI can traverse the patterns of the known in ways that are extraordinarily revealing, but it's not structured to reason about the unknown.
Inference is not sufficient for reason.
For example, a conventional algorithm can search for patterns in text at a scale many orders of magnitude beyond a mind's capacity, and this can be very revealing, but to do so this algorithm need not read the text with comprehension.
Regarding the question: can genAI be enhanced to reason? The answer is assumed to be "no", due to the categorical opposition of the two kinds of complexity and the lack of understanding of structures within genAI to handle the reasoning.
Read the article, which includes other examples including a jump from Newtonian to Einstein physics in the history of astronomy, and a noodling on how to talk about the edge of the unknowable in AI.