Zurn does write "At their most basic level, a busybody is someone who is curious about other people's business," but develops the concept a bit further. Zurn says "The busybody's ideational sphere, for example, is characterized by quick associations, discrete pieces of information, and loose knowledge webs. They are interested in conceptual rarities: whatever lies outside of their knowledge grids."

Whereas the research article Zhou et al. (2024) states "Hunters build tight, constrained networks whereas busybodies build loose, broad networks." So it seems their conception of busybody roughly matches Zurn's description.

See the methods section https://www.science.org/doi/10.1126/sciadv.adn3268#sec-4 , for a description of how Zhou et al. (2014) aggregate graph theoretic metrics to define "busybody" and "hunter" styles of navigating Wikipedia.

- the language network, which delivers formal linguistic competence - the multiple demand network, which provides reasoning ability - the default network, which tracks narratives above the clause level - the theory of mind network, which infers the mental state of another entity

This leads to their argument that a modular structure would lead to enhanced ability for an LLM to be both formally and functionally competent. (While LLMs currently exhibit human-level formal linguistic competence, their functional competence--the ability to navigate the real world through language--has room for improvement.)

Transformer models, they note, have degree of emergent modularity through "allowing different attention heads to attend to different input features."

I was wondering, is it possible to characterize the degree of emergent modularity in current systems?

Comments URL: https://news.ycombinator.com/item?id=34526308

Points: 1

# Comments: 1

In this case, we are tracking the flow of an incompressible fluid over time. This flow is represented by a velocity field evolving over time, under the constraint of no net inflow/outflow of material into any region of space. Thus, the singularity corresponds to a portion of fluid speeding up and approaching an infinite speed as you approach some finite time.

Because the fluid cannot be compressed, the only way the singularity can be produced is for a portion of the liquid to swirl, increasingly rapidly, about some point: hence the discussion in the article about vorticity.

As isoprophlex pointed out, this undefined value of the velocity field prevents you from (or at least complicates) computing the further evolution of the fluid.

Presumably if AI-generated code passes every test case, but would fail on edge cases that some human programmer(s) did not anticipate in their suite of tests, the humans potentially might have made similar coding mistakes as the AI if they had had to personally write the code.