Readit NewsWell, I've never been on "social media", but e.g. at night before bed some times I scroll on HN for a long time before falling asleep (30min-1hr). If I commit myself not to, I read instead.
The thing we should be talking about is forms of entertainment, and social media is just one type of entertainment. We should be discussing pros and cons of different forms of entertainment. Instead the discussion is "social media bad", which is a great starting point, but has the problem that allows us to avoid having to talk about the underlying mechanisms.
For example, one of the people responding here says "if I don't go on social media I go on youtube instead." If you try and think past "social media bad", what is actually going on?
How do you measure this? What is this claim founded in?
You could indeed say that inflation should be defined by the asset prices. This would couple fiat and asset prices definatorically.
Do plants sleep? Don't some insects, like flies, live without any sleep?
Who gave anyone the right to judge who needs or needs not to be saved? What if people don’t want to be saved?
- Massive time sink. Those of us at senior/PI levels devote a lot of time to grant writing, often more than to actual research.
- Not something that you really get much useful learning or enrichment from (apart from learning to write better grant proposals the next time). The part of brainstorming and structuring ideas is useful but you would mostly do it without grant writing anyway, all the actual writing and polishing (which is 95% of the time) isn't. Definitely not an efficient use of the amount of hours it takes.
- I don't know specifically for NIH, but in my (non-US) context, grant proposals are full of formulaic sections that aren't really useful (Gantt chart, data management plan, etc.) When I'm in an evaluator role, I tend to outright skip many of them, not out of neglect or laziness but because they're just useless ritual fluff.
- As a consequence of the above three points, most of us dislike or even hate this part of our work.
- The meta for most funding agencies I know has long been to overhype and to use exaggeratedly positive language and takes. Exactly what LLMs are naturals at.
- If you're a non-native English speaker and write grant requests in English (common in Europe), the LLM also helps you level the playing field with native speakers, which is quite a big deal. From a naive outside standpoint you might think that scientific grant evaluation is all about the actual ideas and CVs, but the truth is that in practice, ability to pitch your ideas better than other competitors in your call is key.
- Honestly in the last grant I wrote, Gemini came up with some paragraphs that I consider to be clearly better than what I would have written by myself. Clear, concise, attractive to read, etc. It's just very good at writing. I'm better than it at the actual research, but at writing, let alone in English where I'm not a native? I don't have a chance.
As a result of this... good luck convincing scientists not to use LLMs for this. I'm pretty sure that if you ask, you will find two types of scientists: those that tell you that they use LLMs for grant writing and those who are hypocrites and deny it. I wouldn't even trust a scientist who didn't use LLMs for this (unless it's out of some very deep quasireligious conviction): why waste your time? Don't you want to have more time to do actual science?
I believe that LLMs can be very useful to identify this stuff in our processes. The solution shouldn't be then to fill them with LLMs but strip them entirely away. I tend to think the same about everyone freaking out about LLM misuse in education.
As horizon increases, the number of possible states (usually) increases exponentially. This means you require exponentially increasing data to have a hope of training a Q that can handle those states.
This is less of an issue for on policy learning, because only near policy states are important, and on policy learning explicitly only samples those states. So even though there are exponential possible states your training data is laser focused on the important ones.
That's not exponentially more (which would be a preposterous 2^1000 or 10^1000 years old). It's just 100 times more. Should I stop being annoyed at how media use the word and just accept their alternative meaning of "a lot" ?