Not necesary, it might be an internal system. And no selfie. For example, in Russia it probably would be illegal to send personal and biometric data abroad. But of course in the West the rules might be different and it is ok to send citizens' data to shady foreign companies.
Also I am surprised people do not see the different between isolated internal "old school" systems built on owned servers located at the bank property and modern vibe-coded microservices in kubernetes in a rented cloud with the widest attack surface possible.
I will check what particular settings I have for AdGuard when I get back home this evening. I don't recall anything particularly hairy but it has been a few years.
There‘s a checkbox called “Enhanced Protection” (name might vary, I have the app in another language) that specifically says it’s for stuff like YouTube
We can tell OP is using local or unencrypted DNS because they got DNS-redirected to a message.
If you have DoH configured at either OS or browser level then you will not see a message: the site will either work, or it will error out if the IP is blocked.
If you are using an alternative DNS provider over unencrypted DNS then either outcome is possible.
I'm not sure that roaming still works that way. It used to. I think you get configuration from your home network but everyone involved tries to get your traffic onto the internet at the closest opportunity.
I am a complete legal layman here. What is the actual damage produced by this leak? What can actually be asked for in a trial should it go there? Money? Or is this just a normal hacking case and some cyber law was broken?
I am shocked by the comments in this thread ranging from “yeah but humans also make mistakes” to “yeah but how many mistakes does an AI do compared to a human”. Neither of those is the point here. We have doctors that have employed AI as a software to help them and that software is flawed. I am a software developer and if I write some piece of medical software with bugs that are so blatant in it, I'd see hell up to potentially being sued into oblivion
Somehow the standards we have for every other kind of automation go out the window for AI.
If it turned out an LLM embezzled funds and spent them at an internet casino, we would have folks in the comments explaining that what really matters is the embezzlement rate compared to humans doing the same job.
> the standards we have for every other kind of automation go out the window for AI
I think this sums up what I find most frustrating about all of this from the executive level. It feels like everything that was important just a few years ago is now considered unnecessary baggage, that is merely there to slow everything down. What would've got you sacked is now applauded at times, and this is only really a year or two into it proper.
> Somehow the standards we have for every other kind of automation go out the window for AI.
I dunno, for every new hype automation, the machine is given a lot more leeway then people. At least by some on HN. I remember FSD discussion years ago where FSD was already supposedly better then people and all its problems explained away.
The cargo cult around AI on this website is making me second guess a career choice that has always been obvious to me.
In addition to not enjoying my work as much as what I used to because it's become babysitting an superpowered AI toddler, I now have to deal with this kind of opinion online.
> it's become babysitting an superpowered AI toddler
That's hopefully temporary: you are dealing with provisional architectures - as is obvious by their lackings (transparency; reflection; evolution; one shot learning...).
The very fact that you use the term 'AI' for LLMs when some of us would not ("NNs are used in AI" does not mean that all NNs would be AI), or would be wary of that use signifies a problem that is being tackled and will be worked on until the next stage.
The meta-economy losing touch with reality is a symptom of late stage capitalism. It used to matter whether a technology could solve a problem, then it started to also matter whether this fact could be explained to savvy investors, and now it only matters whether it can be explained to stupid investors because they are the ones with the money. You used to have to sell a product people wanted to get their money, but now customers don't have any money because it's all with billionaires so those are the only people you have to please.
Except a couple of messages, most of the comments questioning our standards of AI integration have been downvoted into oblivion.
Even questioning is starting to feel like a crime here.
This! The problem is not whether AI makes more or less mistakes than a human. But for decades, people have been used to computers either working, or crashing, but never working wrong or misleading. AI changes that, and people really need to understand that. But that goes against the interest of AI provider's and their investor's interests, so the point is not being transmitted to the end users prominently enough.
Yeah, I suspect the problem here is that computer transcription makes mistakes and then a summarizing LLM treats whatever it outputs as gospel.
Automated transcription for anything official is scary to begin with, because some noise in the background is all it takes to turn "I've never taken mushrooms" to "I take mushrooms," or whatever. And then the LLM will simply report "Patient reported using mushrooms."
Speech recognition is a notoriously difficult problem to solve. It can work great as a first pass that someone can fix up, but not on its own. This is why having sentence/clause and word-level transcription markers along side a recording help.
One of the main issues is around homophones in an accent (Adam/Atom in American English, Bath/Barf in London English, etc.). Not to mention pronunciation variations due to fast speech, speech impedements, or parts of words side-by-side that sound like a different word.
Another big issue is around misaligned training data. For example, Whisper is known to hallucinate on silence [1].
> The problem is not whether AI makes more or less mistakes than a human
My problem is who is accountable when the AI is given autonomy and messes up
It seems like AI is being deployed so it can take the blame for some individuals decisions that will have negative impacts. Then they can shrug and say "wasn't me, it was the AI"
> Then they can shrug and say "wasn't me, it was the AI"
Or even worse they hold a fall person accountable. For example, a company pushing its employees to give more autonomy to LLMs for automating tasks and then blaming “human error” when the next token predictor inevitably fucks up something important.
“But humans make mistakes” is probably the worst argument in the AI booster’s toolbox. We use computers because “they” don’t probabilistically make mistakes. They are deterministic. If a computer does make a mistake, it is a bug/defect that can be root-caused and fixed.
It should be completely and utterly intolerable that a computer produces a different output given the same input. We shouldn’t couch that behavior in soft terms like “hallucination”. A computer system that non-deterministically makes mistakes is a defective computer system.
Audio transcription is a hard problem that is inherently non-deterministic and probabilistic due to ambiguities in the speech due to:
1. accents -- Especially around mergers (cot-caught [AmE], trap-bath [BrE] vs palm-bath [LondonE], pin-pen [Some AmE]). These can even be hard for native speakers -- try transcribing a broad Scottish, London, Brooklyn, or Indian accent and see how well you do.
2. sound/phoneme variation based on surrounding phonemes -- It is common for the 'n' sound to be realised as an 'ng' sound before a 'k' or 'g' sound due to velarization ('ng' is the velar variant of 'n' and 'k' and 'g' are velar sounds). It is common for vowels to be nasalized before nasal sounds ('n', 'm', 'ng'). It is also common in non-rhotic (don't pronounce the 'r's next to vowels like in 'start' and 'north') to pronounce an 'r' between two adjacent vowels in words ending/beginning with vowels (the "intrusive r", e.g. in "there and back").
3. sound changes due to fast speech ("I'm gonna see 'bout it t'day.", etc.)
4. ambiguity about where words start/end (e.g. "to Damon" vs "today mon" where the "mon" is the variant of "man" in Caribbean English).
5. word play, puns, etc. due to accent and other speech.
6. technical words in a given domain, specific place names, etc.
7. other things that can affect speech such as mumbling, stuttering, or slurred speech.
Computers ruining lives with wrong or misleading output has always been a thing, consider e.g. the Post Office scandal in the UK or Robodebt in Australia.
It seems to me that most people regard computers as some kind of infallible truth machine. If told its spewing garbage they're more likely to double down and shoot the messenger than try and get it sorted out.
If concepts can be that sloppy, then the party that believes it an argument that NNs surpass humans get a point.
Edit: in fact, there is a point: we compare AI (proper AI) to optimal professionals, but that is not the real scene. And this is why in computing we bet on deterministic algorithms: they do not guess a solution, they compute it. There is no comparison with the possibility of failure from a biology based system - in deterministic computing the failure is restricted to exceptions.
The worst part is that this will be connected to another AI. The insurance company's AI chatbot responsible for verifying claims. 'You took illegal drugs hence your claim is denied" and "there are no humans available to review your case. write to us and someone will get back to you". This will be followed by a "deep search AI agent" responding multiple times before it is sent for a human review. And even then the risk of AI misclassifying it as a routine question and not an urgent payments related question.
And I am sure people will still defend that dystopia with "companies send canned response all the time".
It's really more about stakes. A human taking notes (and being familiar with the protocols) will of course pay attention to elements with significant or critical consequences. AIs are just text generators, they don't have a notion of consequences or risks...
though this is exactly the thing that can be handled during RL deep learning
the problem is that introduction of any new technology usually happens with so much emotional baggage, that when there's an error (human or otherwise) some humans will understandably see their biases confirmed in them, and will signal boost everything to the Moon.
Yes, buggy software is the right model. In the popular imagination AI currently sits in a weird zone of unaccountability, it's not a person so cannot be held responsible, and developers get a free pass when their golem goes off the rails.
LLM's will never be reliable enough to let loose on tasks that require 100% accuracy, therefore a human will have to review their work. So will any time actually be saved, or at least enough time to justify the cost and extra complexity of the new system?
Another important differentiator is determinism. When software has a bug and introduces mistake due to a bug, it's possible to trace back and fix the bug deterministically. With LLMs it's "works as designed".
Also, the types of mistakes are completely different. A person may mishear something and ask to verify; LLM is always certain that what it transcribes is a fact. A person might omit something but won't make up the facts like that.
So, a mistake is not the same thing as a hallucination.
> I am a software developer and if I write some piece of medical software with bugs that are so blatant in it, I'd see hell up to potentially being sued into oblivion
Based on watching the medical software field as a consumer (patient) and friends who are doctors, this is a fantasy. The quality of software in this field is abysmal and there seems to be almost no repercussions to those who develop or sell it.
Which is precisely why this sort of thing can be rolled out without much fear by those pushing it.
That's besides point, the point is that classical software bugs are contained and we have process and understanding allowing us to discover them, triqge and fix. For AI based sw you can guess and try another prompt.
Why is that "the point"? You are the second person in this thread to insist on owning it.
I get why it's problematic, obviously, but if it produces statistically better results (which I have no idea of), I don't think it's right to just write it off because of this.
Because you cannot hold people accountable, since it isn't "people".
We humans are emotional creatures, and we will always prefer systems where someone can be held accountable for mistakes. The developer, the doctor. Whoever.
I do not oppose AI integration; I'm not a Luddite. But having a "move fast, who cares if a couple die" isn't the way to go with sensitive fields, like the medical field.
I suppose we will come up with proper responsibility-hierarchies and guardrails around AI, but until then, people have a right to complain about the lack of them.
The case of Therac-25 is required reading for a computer science ethics course. (How requisite a CS ethics course is a different story entirely though.) It's not a fantasy for more regulated medical devices eg pacemakers.
The software is heavily regulated for medical devices. Saying an MRI machine has bad software seems highly unlikely to me. This is of course different from Epic, but even then as a patient MyChart is really not that bad
The reasoning seems to be "Bad thing X existed for a long time with no solution. That means it's okay to make it worse, because if it was actually a problem it would've been solved by now. Plus it's not my job to solve X."
My whole family are doctors and If you have ever worked in the medical profession, the amount of human medical document related errors you see is far, far greater than AI scribes..AI scribes are a revolution in note taking.. so get off your druthers.. we live in a real world that optimizes.. and we are stuck in bad doc handwriting, old docs and faxes for pete sake.. and Epic electronic systems with empty text fields becuase of the headaches in taking proper medical notes
I can say for a fact that reliable medical transcrption and dictation is worth handling hallicinations..
its an order of magnitude worse in real life.. or else its just ommitted info since most docs and nurses dont have time for details..
By all means lets be accurate but we must remember all these complex workflows are filled with human error..
It’s the same in Romania where they use the system for every little thing from it’s raining heavily (often the alert comes during said heavy rain) or a nearby fire, or one infamous message saying something on the lines of “here comes the bear” when a bear was spotted within city limits. And it’s loud so a lot of people simply disabled the alerts altogether. But now that the real alerts are coming - see Russian drones entering the country, many people have them disabled
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