prolyxis

I suppose the problem in multiplayer is that everyone has the same wall clock time, so you couldn't easily have consistent time dilation and related effects such as the twin paradox.

Arb Research and Renaissance Philanthropy made an excellent list this year! https://frontier.renaissancephilanthropy.org/

Except that as far as I understand, one of the inspirations for the Turing machine is to explain precisely the computations a human computer could perform with (potentially a lot of) pen and paper.

Note that the $15.99 is for the 64 GB version. The 1 TB version sells for above $100.

Or a person could have the program either critique their flashcards as they write them, or suggest new sorts of flashcards to create without doing the work for them by automatically generating them.

I think one should be somewhat skeptical of the claim that schizophrenia is completely absent in blind people; it might merely be more difficult to diagnose. Combined with the population of congenitally blind people being sufficiently small, the cases that might exist could escape notice. There was an informative post on Less Wrong about this https://www.lesswrong.com/posts/z9Syf3pGffpvHwfr4/i-m-mildly...

It seems like it stems from a 2019 philosophy article written by Perry Zurn, titled "Busybody, Hunter, Dancer: Three Historical Modes of Curiosity."

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 human brain, the authors argue, in fact uses multiple networks when interpreting and producing language. These include:

- 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?

Yes. This was a big part of the GPT-4o demos. But likely they've specifically fine-tune it not to show negative emotions in a serious fashion.

I don't think neural correlates of consciousness have been identified, so clearly there remains research to be done. I'm not in touch with the neuroscience literature, but acknowledging the hard problem of consciousness means one should accept there is a lot of work remaining. That being said, I believe the hard problem is surmountable. In my mind the situation is similar to computer science before Turing's description of the Turing machine: there were imprecise notions abound about what computation meant that needed to be clarified through a concrete model. My view is simply that finer control over conscious experience would aid understanding enormously. But you're right, I should probably skim the real research more.

I am hopeful that advances in brain-computer interfaces will start to provide a partial answer to the question of "what's there" and why it's there. It seems to me the ability to controllably augment one's own consciousness with precision will tremendously clarify the necessary ingredients for consciousness.

The invited speaker was Fifth Circuit Judge Kyle Duncan, whom, according to Wikipedia, has "a history of anti-LGBT activism" https://en.wikipedia.org/wiki/Kyle_Duncan_(judge)#Opposition... . It seems Judge Duncan was invited by the students from the Federalist Society to speak at Stanford Law (see https://stanforddaily.com/2023/03/11/law-school-activists-pr... for more context).

This mostly just left me with a greater appreciation for the seamlessness of the original rabbit-duck illusion.

The physical act of comparing two objects or testing one object against another would typically involve some physical contact or proximity, so even in the case of testing some code on something, it is natural in English to by analogy use the word "against." But I think you are right in this case that "against" has significant adversarial meaning in the act of testing something: often, one introduces stress scenarios or edge cases that could cause some software to fail. Tests are in large part adversarial to the object under test.

There are browser add-ons that can do this (for example, uBlacklist).

Those two titles are good examples of when click-bait is stretched to the point of complete inaccuracy.

A simple example of a function with a singularity is f(t)=1/t. Note that at t=0, f(t) is undefined due to division by zero. On either side of zero, the absolute value of f(t) approaches infinity.

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.

What's your metric for the "percentage the code is wrong"? Is it how many lines of code were wrong, or how many test cases the code fails?

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.

I log in mostly for the custom CSS.