This would matter for bigger versions of the same thing. For side-by-side, the aspect ratio is unimportant since you naturally unfold to have two of them.
I think the person you are responding to is not thinking visually. They basically seem (to me) to think reformat-flowed fixes this, and that having the view of a web page alter because you open a book, is "not a bad thing" when you (and I) are of the view, if you maintain the aspect ratio and simply decimate how many pixels are available to show it, the actual "change" in the layout is kept to a minimum WHICH IS THE INHERENT GOOD HERE -the whole point of the A, B and C series is that the scaling function preserves the ratio of sizes of the contents, under the quality change of bigger or smaller fidelity of presentation.
Unless there is some upsight I am missing, I think the OP just "doesn't get it"
I don't think this is a killer logic either. If you consider that the apps were both running, both full screen on the folded state and alternate which is in view, and now can come into view side by side when you open, surely the "good" is that nothing positional in the layout or styling changes, when you move from swipe-and-select to side-by-side.
>If you unfold a square, you don't get two squares.
yes, you do. Two squares will fold together into the space of one square, and the same with 2 A4 sheets of paper, or two 8½x11 sheets of paper, two of them will fold together to make one of them.
You said: "If you unfold a square, you don't get two squares."
In the context of this discussion, it is reasonable to assume that any "square" available to be unfolded is something approximately like the folding phone, consisting of two layers joined by a hinge. When you unfold that, you go from having a square to having a rectangle equivalent to two adjacent squares, and usable as two adjacent squares.
I see. I agree (obviously, it's a fact), but in the context of this discussion about aspect ratios and why the golden ratio would be relevant to this situation, it is reasonable to assume that the point here is that a full-screen, unfolded image would scale exactly to a half-screen/folded image with no change in aspect ratio.
> None of that matters for a phone.
Of course it does. They show the most basic example right
on the page, which is that you can side-by-side full screen
apps.
which is talking about the case of two separate apps running side-by-side (unfolded), as was shown in one of the pictures of the device.
> it is reasonable to assume that the point here is that a full-screen, unfolded image would scale exactly to a half-screen/folded image with no change in aspect ratio.
Sure, but aside from being interesting from a mathematical standpoint, there are no practical implications from that fact. When the user unfolds the phone and rotates it by 90 degrees, the fact that they're left with about the same aspect ratio (modulo whatever changes with status bars, etc.) means very little for UI design in light of the much more important fact that the screen doubled in size and thus the application's layout should be using the extra screen space to show more information, and should not assume that a majority of the screen is easily reachable by a thumb.
Think of Beethoven's ninth symphony or a foot massage. You probably don't have the sensation of hearing or of your feet being massaged, you just think about it. You can even do stuff like increase the tempo or decrease the pressure, just like aphantasiacs can turn the Eiffel Tower upside down. They just don't have the experience of seeing.
The article doesn't seem to be about replacing nurses staffing their phones with AI. It seems to be about making sure the care they're providing is what the company wants.
I'm not sure the for-profit approach is exactly what's to blame. HMOs like Kaiser are legally forced to spend a certain fraction (80% percent for the worst case, more for large group plans) of their premium revenue on medical services. They can't save and pocket the money like a traditional for-profit enterprise.
This doesn't seem like a money-saving measure exactly. The main AIs the article talks about is making sure nurses on their nurses' lines aren't being assholes. I guess this used to be spot checked before so you save on that? Maybe? It seems like they are trying to solve the problem of some of their nurses staffing their nurses' line not treating their patients the way they're supposed to.
Increasingly health insurance companies and healthcare providers are intertwined. So they may spend 80% on healthcare, but then a big chunk of that could go to the urgent care clinics that they own.
And even if they don’t own the provider, they don’t have much incentive to lower total cost because 20% of a larger number means more total profit.
> Increasingly health insurance companies and healthcare providers are intertwined.
Kaiser is an HMO. They are the insurer and try to have their employees, such as these nursing lines, perform almost all of their care. Shifting from one line of business to another is purely internal and can't game Medical Loss Ratio like your scenario.
> they don’t have much incentive to lower total cost because 20% of a larger number means more total profit
There is some bad incentive here for sure. That being said, insurers do compete on price so they lose customers if they charge more than other insurers. Also, regulatory rate review can decide whether they can raise premiums a given amount.
I’m not talking about Kaiser. I’m talking about companies like UnitedHealth Group Incorporated who own UnitedHealthcare the insurance company and Optum the healthcare provider.
> That being said, insurers do compete on price so they lose customers if they charge more than other insurers.
Yeah but that’s a second order effect. Most companies are incentivized to cut costs because they will directly realize the profit. Insurance companies are incentives to cut costs only to grow market share.
I understand the point of the profit limits, but I don’t think it works very well in practice. I think it would probably be better to just have private companies without that profit cap and add a government insurer to compete with them.
I'm getting a lot of refusals these days from multiple LLMs on multiple fronts for silly stuff, a lot more than I had for a while. If this is where things are really going, I think open weight models have a big future.
"H.R. 148867 makes all large language models subject to safety certification, introduces penalties for unlicensed training and use of uncertified models"
Like the poster, I'm faceblind. It isn't the worst thing: I'm not voice blind, height blind, age blind, hairstyle blind, gender blind, features associated with race and ethnicity blind, attractiveness blind, affect blind, context blind, etc., so I'm mostly good at figuring out who someone is. Within one encounter with a bunch of people, I try to note what someone is wearing.
Every once in a while I don't recognize someone and I go through this whole thing of bringing up every biographical detail about them I remember and all the things we've talked about to show that I'm not an asshole who wasn't paying attention in the past. Fortunately, I have a decent memory for such things.
This is fascinating. So you know that you've met someone before. You know things about them. But you don't recognize them? What does this mean? You don't remember their name? You don't know why or how you know them?
I'm not the OP neither have their problem, but I can't remember names. I know who the person in front of me is, we can talk pleasantly about everything - I have a good memory otherwise - but I can't remember their name to save my life. Luckily they very seldom notice that, if ever. And I won't tell, obviously.
I'm similar, and yes, there are easy options to replace person's name with, as long as you're putting a bit of thought into what you're saying so as not to paint yourself into a name corner.
Ironically, I'm insanely good at remembering faces. But it's kinda useless because of the name thing (and equally, the face context is also difficult).
A trick helping momentarily is introducing them to another person. Then I pick up their name from the conversation and can use it myself - at least for 10 minutes until I forget it again.
"Hi there, I'm ABC, nice to meet you, what's your name"
"...Huh? I'm XYZ. We've met before."
"Oh right...sorry, I promise I remember you! We knew each other from there, and we've worked on this and that together, and etc. etc. etc. I'm just terrible with faces, I'm so sorry!"
It's not "you know things about them without recognizing them"; it's "you don't recognize them at first, it gets awkward, and so you recite facts about them prove that you didn't forget who they were"
My aunt said tells neighbors/acquaintances to just introduce themselves by name every time when they start chatting with her, and reports great success with this — but my entire family is also rather 'take it or leave it' re: social stuff, so the people that don't introduce themselves don't get remembered, which seems perfectly fair.
That I don't know if they're a stranger or not. I introduce myself to acquaintances fairly regularly (sometimes annoying them that I apparently think they're so unmemorable) because I'm the opposite of 'I never forget a face'.
I think normal people are more likely to have the experience where they can't remember a name and why/how/where they know someone from. I of course forget things like anyone, but that's unrelated.
It's funny because I am the opposite. I can easily recognize a face, but if you asked me things like hair color or what they were wearing, I likely would be unable to answer.
General body shape and height are ok. Hair, clothes, make-up, etc are not.
Context is everything. Where are they when we meet? If it is someone from work, at work, this is very easy. If it is someone from work in a shopping centre, this is very difficult unless I know them well.
I make an active point of trying to remember people's faces so I can place them out of context, because it shouldnt be this hard, and they deserve to feel valued in so far as I remember them. Its an uphill battle.
LLMs are great with minority languages compared to almost anything else. Including better than the by the natural language generation employed to use Abstract Wikipedia, which whiffs at relatively large languages like Zulu and Xhosa, let alone many of the rarer languages that popular LLMs speak fluently.
Other correctly point out it does matter what language the code is in since the human does sometimes need to read and understand it.
But also, I suspect the article is just wrong. "The hard languages got easy first" isn't true in practice and the impressive examples given are not representative or as magical as the poster makes them out to be.
The takeaway might be right in the end, but the post isn't right in the beginning.
They don't, and they can't cheat physical realities either.
Plants only filter out very small amounts of CO2 from the air over relatively long timeframes. That's why crop-based biofuels require such enormous amounts of space.
"The amount of CO2 removed from the atmosphere via photosynthesis from land plants is known as Terrestrial Gross Primary Production, or GPP. It represents the largest carbon exchange between land and atmosphere on the planet. GPP is typically cited in petagrams of carbon per year. One petagram equals 1 billion metric tons, which is roughly the amount of CO2 emitted each year from 238 million gas-powered passenger vehicles."
Man-made carbon emissions amount to over 40 billion metric tons annually, according to a quick Google search. Worldwide terrestrial plant carbon exchange amounts to less than 2.5% of the CO2 humans release, if plants take in 1 billion tons per year.
From the perspective of averting climate change it is indeed very small.
A team of scientists led by Cornell University, with support from the Department of Energy’s Oak Ridge National Laboratory, used new models and measurements to assess GPP from the land at 157 petagrams of carbon per year, up from an estimate of 120 petagrams established 40 years ago and currently used in most estimates of Earth’s carbon cycle.
Whether 157 billion tons or 120 billion tons, these numbers are large compared to anthropogenic releases. Of course most of this carbon is quickly cycled back out from land plants due to animals/bacteria/fungi consuming the biomass produced by land plants.
You still need to turn incredible amounts of biomass into charcoal or other stable forms of carbon to make a dent in atmospheric co2. It would take decades of hard work on gigantic scales to unburn and bury the fossil fuels we used.
That's the pay-off of our 150-year rush to monetize as much of the Earth's natural resources as possible -- while making stringent efforts to keep quiet knowledge - or suppress any efforts - to utilize the benefits of free solar energy.
Having polluted and despoiled much of the biosphere, of course we'll be donating our supposed wisdom and that hard work to the future generations that will enjoy the fruits of our labors and entreasurement.
They're pretty amazing for the amount of capital cost. $50 in seed and an acre of land can sequester several to over a dozen tons of carbon per year. It might not be space efficient but it requires basically zero infrastructure.
Which is something that when I try to explain to some 'environmentalists' do not get the point.
The other benefits of a biodiverse green belt are great, but if tomorrow I have a concrete system that captures CO2 at 10x the level of trees over lifetime in a similar density, guess what I would like my futuristic city to look like.
Again, it's not that all telehealth doctors are great at this, it's that LLMs are awful at caving in to saying something with warnings the reader will opt to ignore instead of being adamant things are just too uncertain to say anything of value when continually prompted.
This is largely because an LLM guessing an answer is rewarded more often than just not answering, which is not true in the healthcare profession.
LLMs almost never reply with I don’t know. There’s been mountains of research as to why this is, but it’s very well documented behavior.
Even in the rare case where an LLM does reply with I don’t know go see your doctor, all you have to do is ask it again until you get a response you want.
