Are we sure this is not just harmless and arbitrary information being parroted? Do we have verifiable sources other that anecdote? I find it hard to believe that there is just a single value for water intake across the massive biological spectrum that is humanity and expect to see a range when this conversation comes up. You're also getting water from foods, which I am sure is not being accounted for. Reminds me of the 10k steps a day that just happened to be "correct enough" to be believed and acted on. The truth is much more nuanced and depends on a number of factors in a person's physical health.
Without concrete verifiable findings, the best we can do is learn to pay attention to our bodies and drink maybe a little bit more water than we think we need to.
The European doctor quoted certainly said "3 liters" from both drinks and food (especially vegetables). In Europe I think we drink between 1 and 2 liters per day in actual water, depending on how dry the weather is.
Agreed. Being in the Midwest US, my intake also varies widely, depending on weather or season, physical activity, and the foods I've been eating.
I'm not entirely dismissive of doctors, be they European or American, as most I've encountered do have the patient's best interest at heart. But they are also human, and it is very easy to stick with the safe and easy answer rather than do the work to find the real answer. So when I hear claims like that, I immediately doubt them, assuming it is placeholder information because we do not know the actual answer. Unfortunately, a lot of our media in the US considers such "placeholder information" to be actionable, and ends up convincing the public (including doctors) of its veracity.
I always laugh about those ridiculously large water bottles American carry and how they remind you all the time that you must drink water as if I did need it. I wonder why that happens.
sure, there are different recommended amounts, the EFSA recommendations are 2.5l per day for a grown up man and 2l for a woman[0]. I'm a bit bigger than the average so I got 3l as a recommendation when I was on a diet or when I had specific issues.
But I didn't mean to imply everyone should drink it, just that it's not hard to drink that much. And yes, of course you ingest a lot of water through other means too.
I’ve always been extremely suspicious of constant water consumption. No other mammal seems to do this. Even the ones that require a lot of water like horses will only drink when they’re thirsty or while eating.
Its aggravated because the "water sensor" appears to fail early with age. Elderly people tend to not get the thisty feeling as often, but get dehydrated anyway.
I wonder how much of the effects of ageing are due to cascading failures downstream of alterations like these. For example, it's common for people to lose teeth in advanced ages. How much of this is due to dry mouths from insufficient water intake? Fallen teeth then may become entry points for infections, et cetera. Perhaps fixing a few early causes we can avoid a lot of negative effects and live more, without the need to go full spartan in lifestyle discipline.
I don’t know the exact things. I only know that diabetes would make this loss tremendously. As to teeth loss, it’s mainly because periodontitis but not age though it always goes worse as the age increases because of their life behaviors.
I agree, but in fairness, I don't know of any brand, tech or otherwise, that can completely wall itself off against insider threats. No matter how vigilant you are, someone who knows exactly how you move will find a way around you.
I can understand it's hard to defend against plausibly deniable errors that create backdoors, etc. But this would show a complete lack of code review, no?
> But this would show a complete lack of code review, no?
You'd be surprised how many websites use Google Tag Manager to allow their marketing department to roll out trackers and other JS snippet directly into the site's root context.
GTM et al's sole reason of existence is to provide marketing people with a way to bypass corporate IT.
And I definitely would not rule out something like this being the cause in the end.
Not even that. Bury it in a sufficiently-large PR and there’s a very good chance it’ll be rubber-stamped because no one wants to take the time to carefully review the entire set of changes.
I did the same thing, but realized I was contributing to the problem. If a web app requires Chrome for full functionality, then us switching browsers is giving them permission to continue and expand their invasive practices.
These days, I just navigate away from anything that demands I use Chrome "for best results." One of the sites for a local utility company does this, so instead I just call monthly and pay or manage my service by phone. I'm old enough to remember when that was the preferred way after mailing personal cheques went the way of the dodo, so it does not feel that inconvenient to me, but I can see where it might for other people. Still, nobody said the fight to regaining our agency online would be easy. Or convenient.
Putting everything on a spectrum is what got us into this mess of zero regulation and moving goal posts. It's slippery slope thinking no matter which way we cut it, because every time someone calls for a stop sign to be put up after giving an inch, the very people who would have to stop will argue tirelessly for the extra mile.
What mess are you talking about? The existence of LLMs? I think it's pretty neat that I can now get answers to questions I have.
This is something I couldn't have done before, because people very often don't have the patience to answer questions. Even Google ended up in loops of "just use Google" or "closed. This is a duplicate of X, but X doesn't actually answer the question" or references to dead links.
Are there downsides to this? Sure, but imo AI is useful.
It's just repackaged Google results masquerading as an 'answer.' PageRank pulled results and displayed the first 10 relevant links and the LLM pulls tokens and displays the first relevant tokens to the query.
1. LLMs can translate text far better than any previous machine translation system. They can even do so for relatively small languages that typically had poor translation support. We all remember how funny text would get when you did English -> Japanese -> English. With LLMs you can do that (and even use a different LLM for the second step) and the texts remain very close.
2. Audio-input capable LLMs can transcribe audio far better than any previous system I've used. They easily understood my speech without problems. Youtube's old closed captioning system want anywhere close to as good and Microsoft's was unusable for me. LLMs have no such problems (makes me wonder if my speech patterns are in the training data since I've made a lot of YouTube videos and that's why they work so well for me).
3. You can feed LLMs local files (and run the LLM locally). Even if it is "just" pagerank, it's local pagerank now.
4. I can ask an LLM questions and then clarify what I wanted in natural language. You can't really refine a Google search in such a way. Trying to explain a Google search with more details usually doesn't help.
5. Iye mkx kcu kx VVW dy nomszrob dohd. Qyyqvo nyocx'd ny drkd pyb iye. - Google won't tell you what this means without you knowing what it is.
LLMs aren't magic, but I think they can do a whole bunch of things we couldn't really do before. Or at least we couldn't have a machine do those things well.
Generalizing with "everything", "all", etc exclusive markers is exactly the kind of black/white divide you're arguing against. What happened to your nuanced reality within a single sentence? Not everything is black and white, but some situations are.
The person he's replying to argued against putting things on a spectrum. Does that not imply painting everything in black and white? Thus his response seems perfectly sensible to me.
He argued against putting things in a spectrum in many instances where that would be wrong, including the case under the question. What's your argument against that idea? LLM'ed too much lately?
Right. But HN, among other platforms, is full of users who will confidently run their mouths about something they don't fully understand while believing they do. I think the previous commenter was being too shy in pointing out that even exceptionally smart people sometimes forget where the limits of their own knowledge are, not to mention consider themselves immune to any propaganda that surrounds the subject at hand.
The Opus 4.6 thread was full of "very smart" and experienced SWEs likening model weights to neurons. And again, any DL curriculum worth its salt will thoroughly debunk that comparison, i.e. Justin Johnson. In this day and age it seems the Darios and Altmans have successfully waged the most damaging propaganda campaign in modern time. Even the Pentagon is lining up to relegate its decision making to black box stochastic ML models. Tech as an industry is unfortunately extremely gullible, all the more so when pressured by the market, VCs, clueless PE analysts, the tech blogger/grifter complex. Foundation model makers can get away with hiding training data while proclaiming they are building a "moral" neural network while no one bats an eyelash.
>Right. But HN, among other platforms, is full of users who will confidently run their mouths about something they don't fully understand while believing they do.
This is honestly funny and kind of ironic.
If this:
'The "reasoning" is two matrix transformations based on how often words appear next to each other.'
is what byang364 has to say, then he's part of the people you mention.
They could admittedly be more defined, but I think the original commenter missed a key word. It really boils down to whether or not you are offloading your critical thinking.
The word "thinking" can be a bit nebulous in these conversations, and critical thinking perhaps even more ambiguously defined, so before we discuss that, we need to define it. I go with the Merriam-Webster definition: the act or practice of thinking critically (as by applying reason and questioning assumptions) in order to solve problems, evaluate information, discern biases, etc.
LLMs seem to be able to mimic this, particularly to those who have no clue what it means when we call an LLM a "stochastic parrot" or some equally esoteric term. At first I was baffled that anyone really thought that LLMs could somehow apply reason or discern its own biases but I had to take a step back and look at how that public perception was shaped to see what these people were seeing. LLMs, generative AI, ML, etc are all extremely complex things. Couple that with the pervasive notion that thinking is hard and you have a massive pool of consumers who are only too happy to offload some of that thinking on to something they may not fully understand but were promised that it would do what they wanted, which is make their daily lives a bit easier.
We always get snagged by things that promise us convenience or offer to help us do less work. It's pretty human to desire both of those things, but proving to be an Achilles Heel for many. How we characterize AI determines our expectations of it; so do you think of it as a bag of tools you can use to complete tasks? Or is it the whole factory assembly line where you can push a few buttons and an pseudo-finished product comes out the other side?
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