jal278

appears it's just a theory, sort of surprised this lifted up so far on hn

not sure w/o context why this is important -- no results/implementation & i believe there are prior combinations of transformers/RNNs. but perhaps I'm missing the relevance/insight

The fundamental premise of this paper seems flawed -- take a measure specifically designed for the nuances of how human performance on a benchmark correlates with intelligence in the real world, and then pretend as if it makes sense to judge a machine's intelligence on that same basis, when machines do best on these kinds of benchmarks in a way that falls apart when it comes to the messiness of the real world.

This paper, for example, uses the 'dual N-back test' as part of its evaluation. In humans this relates to variation in our ability to use working memory, which in humans relates to 'g'; but it seems pretty meaningless when applied to transformers -- because the task itself has nothing intrinsically to do with intelligence, and of course 'dual N-back' should be easy for transformers -- they should have complete recall over their large context window.

Human intelligence tests are designed to measure variation in human intelligence -- it's silly to take those same isolated benchmarks and pretend they mean the same thing when applied to machines. Obviously a machine doing well on an IQ test doesn't mean that it will be able to do what a high IQ person could do in the messy real world; it's a benchmark, and it's only a meaningful benchmark because in humans IQ measures are designed to correlate with long-term outcomes and abilities.

That is, in humans, performance on these isolated benchmarks is correlated with our ability to exist in the messy real-world, but for AI, that correlation doesn't exist -- because the tests weren't designed to measure 'intelligence' per se, but human intelligence in the context of human lives.

The function of news is to help a democratic citizenry be critically informed, and that this kind of statistic doesn't accomplish what it set out to do, although it's certainly interesting for its own sake. I think it's a challenge of our age to figure out how to create institutions that are wise and don't simply bend to distorting pressures (money, politics, psychology).

For example, we do want terrorism over-represented relative to old-age-deaths. However, a responsible and self-aware media would really attempt to counteract 'availability bias' -- e.g. that due to the human mind what is repeated we tend to assume is actually more prevalent. But we don't have wise institutions at the moment.

The more general problem is that it is hard to quantitatively demonstrate the ways in which media fails at fulfilling its complex societal role, because it is a qualitative failure in general, although we can poke at it's edges for sure (e.g. fearmongering language probably has gone up, as has polarization on both sides of the aisle, and the amount of information-free 'babbling and speculating' in the immediate aftermath of some event has likely gone up over time).

Yeah -- I don't get why this is front-page -- reads like LLM quasi-insight:

"Through activation, lifeless equations became living systems. The neuron was no longer a mere calculator; it was a decider - a locus of transformation where signal met significance." -- wtf

The idealized (Science 1) / realpolitik (Science 2) dichotomy is both real and at first depressing. I also did a PhD in machine learning, and became quite disillusioned after seeing how the sausage was made, and how different the process is from how I had imagined it. At the same time -- engaging in 'game change' within Science 2 (perhaps not as a PhD, but after you have some security), is I think one of science's highest moral callings. The aim is not necessarily to inch Science 2 towards an impossible Science 1, but to help science to take itself more seriously (it really is a messy social process & there are ways that social process can work better or worse towards the public good -- itself a scientific question) -- and contribute towards science 2 becoming a better (and ideally better-at-self-improving) science 2.

This is naive 'populism': There's no way to avoid 'allusion' writ large -- e.g. do you object to biblical references, or to references to particular experiences that only some people have (heartbreak, death of a father)? Sure, some communities basically 'write for themselves' in a way that becomes inaccessible to outsiders w/o a lot of work. But that's fine -- I like a McDonald's hamburger as well as really nuanced flavors (for whatever reason I like nuance in how I make oatmeal that likely few others probably appreciate). Film buffs like the nuance/allusions in that medium; etc. Your comment seems like: "The stuff I like is the best and does the most for humanity" -- I think there is indeed an argument for art that is broadly appreciable, but your comment is a form of the 'gatekeeping' you criticize -- it's gatekeeping for art that doesn't require a lot of effort (for you, and those like you) to appreciate.

> High intake of sweetened beverages was associated with higher risk for most of the studied outcomes, for which positive linear associations were found. In contrast, a low intake of treats was associated with a higher risk of all the studied outcomes.

Not sure what to make of this -- some kind of other latent explanation (e.g. that many of those with the lowest intake of treats were on a diet due to bad health?).

From the discussion section:

> One aspect to take into consideration is however that there is a social tradition of “fika” in Sweden, where people get together with friends, relatives, or coworkers for coffee and pastries (41). Thus, one could hypothesize that the intake of treats is part of many people's everyday lives without necessarily being related with overall poor dietary or lifestyle patterns, and that it might be a marker of social life.