One issue is heterogeneity in effects across individuals. Even with traditional antidepressants, it's common for people to respond to one drug class but not another. So it's conceivable, just based on this study, there are people who might respond to psychedelics who might not respond to other things, and vice versa. So even if the effect in the general population, on average, is the same, it might be useful for a different subgroup, which is still differentially useful.
I suspect a lot of these effects have been overhyped, but the same could be said of any depression treatment, and there's good evidence for a lot of treatments even if the effects are smaller than they're sometimes made out to be.
FWIW, I like her videos but I usually prefer essays or blog posts in general as they're easier to scan and process at my own rate. It's not about this particular video, it's about videos in general.
I get a similar feeling for when friends send me 2minute+ Instagram reels, it's as if my brain can't engage with the content. I'd much rather read a few paragraphs about the topic, and It'd probably take less time too.
Same; thanks to modern technology, videos can be transcribed and translated into blog posts automatically though. I wish that was a default and / or easier to find though.
For years I've been thinking "I should watch the WWDC videos because there's a lot of Really Important Information" in there, but... they're videos. In general I find that I can't pay attention to spoken word (videos, presentations, meetings) that contain important information, probably because processing it costs a lot more energy than reading.
But then I tune out / fall asleep when trying to read long content too, lmao. Glad I never did university or do uni level work.
It is not carrying a lot of weight. Macroeconomics are different from microeconomics. On a micro scale agents have enough weight on the system where a specific action might break a model. On a macro scale each individual agent's action carries less weight and therefore the system becomes predictable.
On a micro scale it is possible, and sometimes favorable, to intervene. On a macro scale to intervene economically becomes impossible due to the economic calculation problem. It is widely accepted in modern economics that the unit of maximum extent where economical intervention is possible is a business/company/enterprise. Or in sociological terms the maximum unit is the family. Anything broader than that and the compound effect of the economic calculation problem becomes apparent and inefficiencies accumulate. Autonomous decentralized mechanisms (like a free market) are the only solution to it, but not the most optimal.
I wish I could read this, and I wish even more it had been open access or otherwise published in an open way — archive.today can't get behind the firewall, and I can't access it at my university (an R1 institution) either because they don't subscribe to it. It would have been better to post it on a blog or on an archive server, which is a bit ironic maybe given the apparent point of the commentary.
I think some of the results (e.g., in math) that have been achieved using LLMs point to the value of LLMs but the basic idea of this commentary stub is worth taking more seriously. The pressure to publish, not just by hiring and promotion committees, but also by the din of communicative noise due to the number of academics involved, leads to a lot of unsurprising and predictable output.
There's a position that LLMs will supercede humans in academic work and render them unnecessary, but also a position that maybe some of that work, the work replicated by LLMs, is superfluous anyway, something we should be reducing for everyone's benefit.
It depends on the program, and even more so, the student and the mentor. It can also vary over time, with more direction early on in a graduate program, and less direction later. Some mentors are very directive, and basically treat students as labor executing tasks they don't have time or want to do. Other times, the student is coming up with all the ideas and the mentor is facilitating it with resources or even nothing but uncertain advice or permissions now and then.
This can lead to a lot of problems as I think in some fields, by some academics, the default assumption is the former, when it's really the latter. This leads to a kind of overattribution of contribution by senior faculty, or conversely, an underappreciation of less senior individuals. The tendency for senior faculty be listed last on papers, and therefore, for the first and last authors to accumulate credit, is a good example of how twisted this logic has become.
It's one tiny example of enormous problems with credit in academics (but also maybe far afield from your question).
Or maybe they are? I'm not an expert in this and reading through some of the government literature there's no mention of this.
Then at least you would know that a given price marker is a good empirical index of how other prices are changing also, at least for a given dimension/component.
The thing about arts and AI is that it's one place where the stochastic parrot criticism has never really stopped being accurate to me.
I think backlash against the stochastic parrot idea has always been based on a bit of a strawman — what's being parroted can be pretty abstract and broad in scope, down to reasoning strategies and personality. But with art in particular, I always feel like it's broadly imitating something from elsewhere.
In those AB tests in the linked essay for example, the options are all pretty prototypical genre music samples being compared against one another. Even the fact that they have pretty accurate prototypical labels says something about how prototypical they are, and you can always find something pretty stereotypical of a genre to compare against to play "gotcha" or make it more difficult. It's just not interesting or relevant to me to test whether you can tell the difference between cliched music samples from a human or cliched music samples from AI.
Real art is often like that — you have writers and musicians who follow genre conventions for a variety of reasons — but the most interesting art happens in the margins in ways that are unexpected.
The essay is worth reading for its argument that music is fundamentally a participatory activity — this is something else on my mind lately about live performance art in general, and the implications of that for understanding certain societal trends. But sometimes I think the discussion of AI and art is really missing the boat in other ways as well, ways that apply to AI in general.
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