> my concern is convergence computing will reduce the importance of desktop interfaces and the freedom we have to install whatever applications we want
Yep, it absolutely will I expect. All the pieces are being or have been laid to build the new world where only a "trusted" device will be able to use the internet. Us nerds can still have our Linux, but it won't work with much of the internet because we won't be able to pass attestation.
Building to that future is exactly what I would expect from Apple, but Google doing so has surprised me. Google doing so is also the thing that will bring it to pass, so there's a special seed of hatred for them germinating in my heart right now. Hopefully I'm just being alarmist and paranoid, but I really don't think I am.
I think tech companies are realizing that the biggest "mistake" they have ever made was giving so much freedom to the desktop user. They hate that we can look into, modify, and delete files, hate that we can add custom-made software, and hate that we can identify and turn off tracking/telemetry. They realized this with the mobile platform and locked everything down, but by that time it was already too late.
Authoritarian governments (that is, what unfortunately all governments want to be) also love this, since if a few big companies control all computing, they can regulate them to control the public.
Fortunately, there are many computers already in the public's hands (which they can use to perform any computation without government restrictions and without paying/sending data to a company); but more and more people are switching to mobile platforms (and kids start out on these platforms) that I'm worried about the future.
I'm probably not a typical case, but I felt like my privacy was massively invaded. The concept was cool, but I felt like every muscle twitch was being scrutinized and recorded forever. I was also in constant fear that the computer would charge me for things I didn't buy and getting it corrected would be a nightmare. I also felt like if there was a bug or malfunction in the system and it didn't charge me for something (which I wouldn't know about immediately) they would come after me as a shoplifter with the full force of a mega corporation with unlimited resources. It felt like there were a thousand high powered lawyers that I couldn't see, watching my every move waiting for some mess up (even though I have no intention whatsoever other than finding and paying for the product I wanted).
So no I didn't feel like I was a thief. But I felt like they assumed I was a thief. My guess is most stores are heavily surveilled nowadays, so it might be unreasonable for me to feel this way with Amazon but not Walmart or Target or Kroger, but that's how it felt.
Walmart and Kroger near me now have one way metal cattle gates that you have to pass through when you enter. Makes me feel like cattle and that their assuming I am a thief. Trips to those locations have dropped.
The Home Depot cameras and screens that "BING BONG" loudly as you pass by to get you to notice them showing that they are recording you are also highly annoying.
I wish there was a greater variety of hardware stores near me...
A giant, multinational, multi-trillion-dollar corporation that will only bargain with individual people living paycheck-to-paycheck? Huh, what a weird power imbalance!
Surely it doesn't have anything to do with their documented history of treating their blue-collar workforce like utter garbage.
I think Amazon are largely shitheads to their low level workers (and still assholes even to mid-level workers), and I am in no way defending them. I'm in fact sickened by them. I will never work for Amazon.
But the implication above was that the non-union employee is the "sub-human" option. I find that attitude pretty gross too. Humans are human whether they are union members or not.
> Despite having access to my weight, blood pressure and cholesterol, ChatGPT based much of its negative assessment on an Apple Watch measurement known as VO2 max, the maximum amount of oxygen your body can consume during exercise. Apple says it collects an “estimate” of VO2 max, but the real thing requires a treadmill and a mask. Apple says its cardio fitness measures have been validated, but independent researchers have found those estimates can run low — by an average of 13 percent.
There's plenty of blame to go around for everyone, but at least for some of it (such as the above) I think the blame more rests on Apple for falsely representing the quality of their product (and TFA seems pretty clearly to be blasting OpenAI for this, not others like Apple).
What would you expect the behavior of the AI to be? Should it always assume bad data or potentially bad data? If so, that seems like it would defeat the point of having data at all as you could never draw any conclusions from it. Even disregarding statistical outliers, it's not at all clear what part of the data is "good" vs "unrealiable" especially when the company that collected that data claims that it's good data.
The trick with the vo2 max measurement on the apple watch though is that the person can not waste any time during their outdoor walk and needs to maintain a brisk pace.
Then there's confounders like altitude, elevation gain that can sully the numbers.
It can be pretty great, but it needs a bit of control in order to get a proper reading.
Seems like Apple's 95% accuracy estimate for VO2 max holds up.
Thirty participants wore an Apple Watch for 5-10 days to generate a VO2 max estimate. Subsequently, they underwent a maximal exercise treadmill test in accordance with the modified Åstrand protocol. The agreement between measurements from Apple Watch and indirect calorimetry was assessed using Bland-Altman analysis, mean absolute percentage error (MAPE), and mean absolute error (MAE).
Overall, Apple Watch underestimated VO2 max, with a mean difference of 6.07 mL/kg/min (95% CI 3.77–8.38). Limits of agreement indicated variability between measurement methods (lower -6.11 mL/kg/min; upper 18.26 mL/kg/min). MAPE was calculated as 13.31% (95% CI 10.01–16.61), and MAE was 6.92 mL/kg/min (95% CI 4.89–8.94).
These findings indicate that Apple Watch VO2 max estimates require further refinement prior to clinical implementation. However, further consideration of Apple Watch as an alternative to conventional VO2 max prediction from submaximal exercise is warranted, given its practical utility.
That’s saying that they’re 95% confident that the mean measurement is lower than the treadmill estimate, not that the watch is 95% accurate. In other words they’re confident that the watch underestimates VO2 max.
> I think the blame more rests on Apple for falsely representing the quality of their product
There was plenty of other concerning stuff in that article. And from a quick read it wasn't suggested or implied the VO2 max issue was the deciding factor for the original F score the author received. The article did suggest many times over the ChatGPT is really not equipped for the task of health diagnosis.
> There was another problem I discovered over time: When I tried asking the same heart longevity-grade question again, suddenly my score went up to a C. I asked again and again, watching the score swing between an F and a B.
The lack of self-consistency does seem like a sign of a deeper issue with reliability. In most fields of machine learning robustness to noise is something you need to "bake in" (often through data augmentation using knowledge of the domain) rather than get for free in training.
> There was plenty of other concerning stuff in that article.
Yeah for sure, I probably didn't make it clear enough but I do fault OpenAI for this as much as or maybe more than Apple. I didn't think that needed to be stressed since the article is already blasting them for it and I don't disagree with most of that criticism of OpenAI.
Well if it doesn't know the quality of the data and especially if it would be dangerous to guess then it should probably say it doesn't have an answer.
I don't disagree, but that reinforces my point above I think. If AI has to assume the data is of poor quality, then there's no point in even trying to analyze it. The options are basically:
1. Trust the source of the data to be honest about it's quality
Or
2. Distrust the source
Approach number 2 basically means we can never do any analysis on it.
Personally I'd rather have a product that might be wrong than none at all, but that's a personal preference.
> Should it always assume bad data or potentially bad data? If so, that seems like it would defeat the point of having data at all as you could never draw any conclusions from it.
Yes. You, and every other reasoning system, should always challenge the data and assume it’s biased at a minimum.
This is better described as “critical thinking” in its formal form.
You could also call it skepticism.
That impossibility of drawing conclusions assumes there’s a correct answer and is called the “problem of induction.” I promise you a machine is better at avoiding it than a human.
Many people freeze up or fail with too much data - put someone with no experience in front of 500 ppl to give a speech if you want to watch this live.
I mostly agree with you, but I think it's important to consider what you're doing with the data. If we're doing rigorous science, or making life-or-death decisions on it, I would 100% agree. But if we're an AI chatbot trying to offer some insight, with a big disclaimer that "these results might be wrong, talk to your doctor" then I think that's quite overkill. The end result would be no (potential) insight at all and no chance for ever improving since we'll likely never get a to a point where we could fully trust the data. Not even the best medical labs are always perfect.
> What would you expect the behavior of the AI to be? Should it always assume bad data or potentially bad data? If so, that seems like it would defeat the point of having data at all as you could never draw any conclusions from it.
Well, I would expect the AI to provide the same response as a real doctor did from the same information. Which the article went over the doctors were able to.
I also would expect the AI to provide the same answer every time to the same data unlike what it did (from F to B over multiple attempts in the article)
OpenAI is entirely to blame here when they are putting out faulty products, (hallucinations even on accurate data are a fault of them).
I have been sitting and waiting for the day these trackers get exposed as just another health fad that is optimized to deliver shareholder value and not serious enough for medical grade applications
I don't see how they are considered a health fad, they're extremely useful and accurate enough. There are plenty of studies and real world data showing Garmin VO2Max readings being accurate to 1-2 points different to a real world test.
There is this constant debate about how accurately VO2max is measured and its highly dependent on actually doing exercise to determine your VO2max using your watch. But yes if you want a lab/medically precise measure you need to do it a test that measures your actual oxygen uptake.
Touching one of those caps was a hell of an experience. It was similar in many ways to a squirrel tap with a wrench in the auto shop (for those who didn't do auto shop, a squirrel tap with a wrench is when somebody flicks your nut sack from behind with a wrench. Properly executed it would leave you doubled over out of breath).
You can't downvote a post, so that's not a factor.
Also it's not as powerful as you think. In the past I have spent a lot of time looking at /new, and upvoting stories that I think should be surfaced. The vast majority of them still never hit near the front page.
It's a real shame, because some of the best and most relevant submissions don't seem to make it.
If you are in a company like e.g. ClickHouse and share a new HN Submission of ClickHouse via the internal Slack to #general, then you easily get enough upvotes for the front page.
Are you sure that's the big lesson? That wolves don't exist?
To me the big lesson was that wolves do actually exist, and if you repeatedly claim that they are here when they are not, then nobody will believe you when they actually are here.
Seriously. I've known for a very long time that our community has a serious problem with binary thinking, but AI has done more to reinforce that than anything I can think of in modern memory. Nearly every discussion I get into about AI is dead out of the gate because at least one person in the conversation has a binary view that it's either handwritten or vibe coded. They have an insanely difficult time imagining anything in the middle.
Vibe coding is the extreme end of using AI, while handwriting is the extreme end of not using AI. The optimal spot is somewhere in the middle. Where exactly that spot is, I think is still up for debate. But the debate is not progressed in any way by latching on to the extremes and assuming that they are the only options.
The "vibe coding" term is causing a lot of brain rot.
Because when I see people that are downplaying LLMs or the people describing their poor experiences it feels like they're trying to "vibe code" but they expect the LLM to automatically do EVERYTHING. They take it as a failure that they have to tell the LLM explicitly to do something a couple times. Or they take it as a problem that the LLM didn't "one shot" something.
I'd like it to take less time to correct than it takes me to type out the code I want and as of yet I haven't had that experience. Now, I don't do Python or JS, which I understand the LLMs are better at, but there's a whole lot of programming that isn't in Python or JS...
I've had success across quite a few languages, more than just python and js. I find it insanely hard to believe you can write code faster than the LLM, even if the LLM has to iterate a couple times.
But I'm thankful for you devs that are giving me job security.
And that tells me you're on the dev end of the devops spectrum while I'm fully on the ops side. I write very small pieces of software (the time it takes to type them is never the bottleneck) that integrates in-house software with whatever services they have to actually interact with, which every LLM I've used does wrong the first fifteen or so times it tries (for some reason rtkit in particular absolutely flummoxes every single LLM I've ever given it to).
I pretty well span the devops spectrum from building/maintaining services to running/integrating/monitoring them in prod. LLMs are definitely better at the dev side than the ops side, no doubt about that. And when it comes to firewalld and many other sysadmin tools I agree it can often be faster to just hand type than to have the LLM do it. Even just writing Dockerfiles it's often faster to do it by hand than the LLM because the LLM will screw it up 6 to 12 times before getting it right, and usually "getting it right" is because I told it something like, "dude you can't mount, you need to copy." It's especially insanely stupid when it comes to rootless podman.
But that said, there are still plenty of ops-y situations where AI can be very helpful. Even just "here's a 125k lines of prod logs. Can you tell me what is going wrong?" has saved me lots of time in the past, especially for apps that I'm not super familiar with. It's (sometimes) pretty good at finding the needle in the haystack. The most common workflow I have now is to point an agent at it and while it's griding on it I'll do some hand greps and things. I've gotten to the bottom of some really tricky things much faster because of it. Sometimes it points me in the wrong direction (for example, one time it noticed that we were being rate-limited by the Cloudflare API, and instead of adding a single flag to the library calls it wrote it's own very convoluted queue system. But it was still helpful because at least it pinpointed the problem).
The other "small pieces of software" I find it very helpful for are bash functions or small scripts to do things. The handwritten solution is usually quick, but rarely as resilient/informative as it could be because writing a bunch of error handling can 5x or 10x the handwritten time. I will usually write the quick version, then point AI at it and have it add arg passing/handling, error handling, and usage info/documentation. It's been great for that.
> When I go buy a beer at the gas station, all I do is show my ID to the cashier. They look at it to verify DOB and then that's it. No information is stored permanently in some database that's going to get hacked and leaked.
That's how it should be, but it's not how it is. Many places now scan your ID into their computer (the computer which, btw, tracks everything you buy). It may not go to a government database (yet) but it's most certainly being stored.
Yep, it absolutely will I expect. All the pieces are being or have been laid to build the new world where only a "trusted" device will be able to use the internet. Us nerds can still have our Linux, but it won't work with much of the internet because we won't be able to pass attestation.
Building to that future is exactly what I would expect from Apple, but Google doing so has surprised me. Google doing so is also the thing that will bring it to pass, so there's a special seed of hatred for them germinating in my heart right now. Hopefully I'm just being alarmist and paranoid, but I really don't think I am.
Some Refs:
Web Environment Integrity: https://en.wikipedia.org/wiki/Web_Environment_Integrity
Private Access Token: https://developer.apple.com/news/?id=huqjyh7k
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