So, part of it is, that if one attractive person finds you attractive, other people are more likely to find you attractive.
Humans are an odd bunch, like that.
The other side of it, is that a truly astonishing number of people go through life being told- and believing- that they need to have a “serious” relationship to be happy/successful/not a total waste, and then there’s the biological hardwiring that more or less confirms this is true.
Sometimes, to the point that they jump from one shitty relationship straight into the next.
Even though most people don’t agree with the stats, I think it makes sense because Arch users are never satisfied with their setup. It could cause many of them to choose an average number.
Restic. You just need a s3-compatible object store in k8s to make it work. All else is handled by the client. That’s what I used (not with k8s), with resticprofile.
I also heard Borg is a great alternative, but never try personally, nor how it works. Both are CLI only I believe.
I second restic and i use it with wasabi. Haven’t touched it in years. Do a fire still once a year and it’s worked perfectly. I even basically cloned my proxmox setup the other day.
They said something similar with detecting cancer from MRIs and it turned out the AI was just making the judgement based on how old the MRI was to rule cancer or not, and got it right in more cases because of it.
Therefore I am a bit skeptical about this one too.
Using AI for anomaly detection is nothing new though. Haven’t read any article about this specific ‘discovery’ but usually this uses a completely different technique than the AI that comes to mind when people think of AI these days.
The problem is that it refers to so many and constantly changing things that it doesn’t refer to anything specific in the end. You can replace the word “AI” in any sentence with the word “magic” and it basically says the same thing…
According to the paper cited by the article OP posted, there is no LLM in the model. If I read it correctly, the paper says that it uses PyTorch’s implementation of ResNet18, a deep convolutional neural network that isn’t specifically designed to work on text. So this term would be inaccurate.
or a pattern recognition model.
Much better term IMO, especially since it uses a convolutional network. But since the article is a news publication, not a serious academic paper, the author knows the term “AI” gets clicks and positive impressions (which is what their job actually is) and we wouldn’t be here talking about it.
Well, this is very much an application of AI… Having more examples of recent AI development that aren’t ‘chatgpt’(/transformers-based) is probably a good thing.
Op is not saying this isn’t using the techniques associated with the term AI. They’re saying that the term AI is misleading, broad, and generally not desirable in a technical publication.
Op is not saying this isn’t using the techniques associated with the term AI.
Correct, also not what I was replying about. I said that using AI in the headline here is very much correct. It is after all a paper using AI to detect stuff.
Haven’t read any article about this specific ‘discovery’ but usually this uses a completely different technique than the AI that comes to mind when people think of AI these days.
For the image-only DL model, we implemented a deep convolutional neural network (ResNet18 [13]) with PyTorch (version 0.31; pytorch.org). Given a 1664 × 2048 pixel view of a breast, the DL model was trained to predict whether or not that breast would develop breast cancer within 5 years.
The only “innovation” here is feeding full view mammograms to a ResNet18(2016 model). The traditional risk factors regression is nothing special (barely machine learning). They don’t go in depth about how they combine the two for the hybrid model, so it’s probably safe to assume it is something simple (merely combining the results, so nothing special in the training step). edit: I stand corrected, commenter below pointed out the appendix, and the regression does in fact come into play in the training step
As a different commenter mentioned, the data collection is largely the interesting part here.
I’ll admit I was wrong about my first guess as to the network topology used though, I was thinking they used something like auto encoders (but that is mostly used in cases where examples of bad samples are rare)
They don’t go in depth about how they combine the two for the hybrid model
Actually they did, it’s in Appendix E (PDF warning) . A GitHub repo would have been nice, but I think there would be enough info to replicate this if we had the data.
Yeah it’s not the most interesting paper in the world. But it’s still a cool use IMO even if it might not be novel enough to deserve a news article.
It’s really difficult to clean those data. Another case was, when they kept the markings on the training data and the result was, those who had cancer, had a doctors signature on it, so the AI could always tell the cancer from the not cancer images, going by the lack of signature. However, these people also get smarter in picking their training data, so it’s not impossible to work properly at some point.
That’s the nice thing about machine learning, as it sees nothing but something that correlates. That’s why data science is such a complex topic, as you do not see errors this easily. Testing a model is still very underrated and usually there is no time to properly test a model.
Any S3-compatible object storage solution would do, plus it’s immensely used in enterprise so a lot of software supports backing up to S3 objects. Operates entirely over HTTPS.
I watch a stupid amount of YouTube so I pay for YouTube Premium. I wish that meant I could have premium features like disabling shorts, disabling those annoying themed sections that keep popping up (right now it’s the olympic games), a search function that actually searches for what I want instead of shoving more suggested videos in my face, and changing every “not now” button into “don’t ever ask again”.
Despite the fact that I’m a voracious consumer of YoutTube videos and a long-time paying customer, I have to accept that I am not the target audience. They want passive users who endlessly watch whatever gets put in front of them so that they never leave the app. If there was a respectful alternative that worked well with iOS and AppleTV I’d gladly pay for that instead.
Reporting scams to Google is a waste of your time. If the company has any sort of manual review of the reports (I have my doubts), odds are that the review happens only if an item is reported by multiple people, and that the reviewers don’t spend more than five whole seconds checking it.
I work in manufacturing. The engineers at my plant think everything works like it does on their computer screens. I had one of them tell me the mix needs exactly 248.73kg of a product and they were shocked when I told them we just add five 50kg bags and don’t actually weigh out 248.73kg.
I just pointed out that our scales are only accurate to 0.5kg. How did he think we were measuring out 0.73kg when our scales don’t have that amount of accuracy? If anything I thought an engineer would know about significant digits!
The funny thing is, the very first thing engineers learn in almost any class is significant figures and to make sure an answer makes sense in a real life scenario. Obviously not everyone is the same in terms of how they apply things, but engineers are definitely taught not to do stuff like that
This is one of those correlation != causation things, hm?
It might be more a case of the “average” Arch user being more sensitive to small quirks/bugs or certain defaults. Arch is at least comparatively unbiased, which might be why these users pick Arch in the first place.
I would personally agree with where Arch is because I prefer a distribution that mostly works out of the box and already made a lot of the decisions for me that I don’t want to be bothered with. I do still customize quite a few elements to my (sometimes very specific) liking, but I also like that I don’t have to do anything when it comes to configuring my disk layout, or configuring zram, or install and configure fwupd or other packages that kind of just make sense to have.
But I don’t really see why Arch users can’t be as happy with their choice as I am with mine, unless the only reason they “use Arch btw” is that they think that’s unironically something to brag about (or peer pressure, but that shouldn’t be a thing I hope).
This is just fun with statistics. I don’t think your Linux distro has a big impact on your overall happiness in life, but of course you can order the results by any parameter you like.
Often, it’s a third factor that influences both, in this case probably age, which influences happiness and distro choice.
Arch: for the young'uns with some fire left in them that just discovered open source and want to stick it to M$ and show off in front of friends.
Debian: When those people grow up and start having to do actual work on their computers...
I went through that cycle over the last 25 years. Thought I was hot shit running Slackware on a ThinkPad 380 when all my friends were on Windows 98. Then I got better things to do than running configure scripts all day and tweaking the UI yet again.
Went to a wedding and the pastor’s pre-ceremony sermon was fire and brimstone followed by a rant about how it was God who gave him the right to marry, not the state. Lots of stuff about the wife being subservient to the husband and acting as his servant. The deep state government was being controlled by a satanists who call themselves secular humanists. Marriage can only happen between a man and a woman and the state was defiling marriage by allowing gays to marry, but it wasn’t real marriage according to God. Some really wacky stuff to talk about at a celebration. Killed the mood.
Turns out my friends had joined one of those extreme, right-wing cults and this was their normal pastor. This group was worse than any of the usual bad actors and interacting with any of their congregation was weird. We fell out of touch for some reason.
So I come from a muslim(ish?) background, but no one in my family or extended family goes to mosque or anything, or says “selam aleykum” everytime we meet (we just say “merhabalar” (i.e. ‘hello’)). It’s just a cultural thing. Most cultural christians want a priest at a funeral, and most cultural muslims want an imam.
Anyway, back to my great aunt’s funeral. The imam was there, doing the prayer in arabic because that’s what you do, even though no one could understand what he was saying. At one point however, he switched to a language that we could understand, and it was very clear he was telling us that we were bad people and bad muslims for not attending mosque, and that our aunt will pay the consequences of our failings.
Needless to say, at the next few funerals we went with a different imam. A nicer one. One who understands that religion is not a key aspect of many people’s lives, but that spirituality in times of distress can be a great comfort.
People - everyone, including you and me - don’t think before most of their acts. And a lot of bad habits boils down to conditioning or lack of.
That’s likely the case for littering: they do it without thinking, justification, or reasoning. “I got some trash, I don’t want it, so I throw it on the ground.”
Everyone overlooking the rigorous maintenance required on the trolley. There will need to be several seamless swap outs each day with cleaning and engineering crews to keep the trolley, tracks, and grounds around running in a fulfilling order. And probably some ovens.
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