211–220 of 222 posts
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Come on, be serious. No one is going to cut any transatlantic cables just to prevent scams. VPNs make a huge difference: as long as you can get a route out somewhere then you can use a VPN (possibly to a compromised host) to make your traffic appear to be coming from another source.
You can't VPN on a non existent path. Or bomb them. All it takes is an actual desire to stop scammers.
I was thinking about it, because wife was telling me story from work, where a woman was scammed with AI generated stuff and her colleague was a little too nonchalant about it ( 'it is on her to do her due diligence' ). And it made me annoyed. How can you possibly make due diligence when everyone around you is incentivized to lie? We do have a concept of fraud, but advertising seems to be able to move around its edges…
I'm sure that some (few) of these NGOs do good work. However, sooner or later, they all seem to succumb to two problems: (1) excessive staff costs, and (2) a failure of incentives. The second one is more insidious: If they solved the problem they address, they would no longer need to exist. They have no incentive to succeed. So they go around addressing individual problems, taking sad pictures, and avoid addressing s…
NGOs must constantly raise money to fund their operations. The money that an NGO spends on fund-raising & administration is called "overhead". The percentage of annual revenue spent on overhead is the overhead percentage. Most NGOs publish this metric.
When a big donor stops contributing, the NGO must cut pay or lay off people and cut projects. I've never heard of an NGO "succumbing to excessive staff costs" like a startup running out of money. Financial mismanagement does occasionally happen and boards do replace CEOs. Board members are mostly donors, so they tend to donate more to help the NGO recover from mismanagement, instead of walking away.
NGOs pay less than other organizations, so they mostly attract workers who care about the NGO's mission. These are people with intrinsic motivation to make the NGO succeed in its mission. Financial incentives are a small part of their motivations. For example, my supervisor at Opportunity International refused several raises.
> So they go around addressing individual problems, taking sad pictures, and avoid addressing systemic problems.
Work on individual problems is valuable. For example, the Carter Center has prevented many millions of people from going blind from onchocerciasis and trachoma [0].
The Carter Center is not directly addressing the systemic problems of poverty and ineffective government health programs. That would take different expertise and different kinds of donors.
The world is extremely complicated and interconnected. The Carter Center's work preventing blindness directly supports worker productivity in many poor countries. Productivity helps economic growth and reduces poverty. And with more resources, government health programs run better.
Being effective in charity work requires humility and diligence to understand what can be done now, with the available resources. And then it requires tenacity to work in dangerous and backward places. It's an extremely hard job. People burn out. And we are all better off because of the work they do.
When we ignore the value of work on individual problems, because it doesn't address systemic problems, we practice binary thinking [1]. It's good to avoid binary thinking.
[0] https://en.wikipedia.org/wiki/Carter_Center#Implementing_dis...
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Not sure where you’re seeing that. There’s criticism of people using fake AI content all over the place, for all sorts of reasons. Charity seems one of the more distasteful applications.
I don't understand what possible relevance AI could have in the purported context. Is it less of a problem if Nike or McDonald's hands a ton of cash to an ad agency who do fakery the expensive, old fashioned way?
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That picture of the skeletal kid that went around shows they can spread some pretty wrogn ideas with real photos too. Context is everything.
If we're thinking of the same picture: That kid is real, it has a severe condition that needs medical attention. It's not the general case, but an extreme one, but that doesn't make the problems any less urgent.
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> her colleague was a little too nonchalant about it ( 'it is on her to do her due diligence' ). I’m always fascinated by victim blaming culture, which has been pervasive long before generative AI. You see it most frequently in cases where the victim is thought to be a safe target: Someone wealthier, an office rival, a corporation. On HN it appears in every thread about someone being scammed, but it was most obvious…
> You see it most frequently in cases where the victim is thought to be a safe target: Someone wealthier, an office rival, a corporation. That must be a representation of your own social circle because I can assure you that poor people are commonly blamed for all the bad things that happen to them.
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I don't know what this means.
Citing that the average poor person is, say, African, is not useful when discussing a specific poor area in, say, Asia.
What I was saying is if you ask an LLM to generate an image of a poor person, it makes sense they'd be brown or black because if you were to randomly pick actual poor people from Earth, the chances are very high it'd be a brown or black person. In this case, it's just accurate representation.
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Citing that the average poor person is, say, African, is not useful when discussing a specific poor area in, say, Asia.
I still don't understand what you're trying to say. What I was saying is if you ask an LLM to generate an image of a poor person, it makes sense they'd be brown or black because if you were to randomly pick actual poor people from Earth, the chances are very high it'd be a brown or black person. In this case, it's just accurate representation.
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I still don't understand what you're trying to say. What I was saying is if you ask an LLM to generate an image of a poor person, it makes sense they'd be brown or black because if you were to randomly pick actual poor people from Earth, the chances are very high it'd be a brown or black person. In this case, it's just accurate representation.
And the problem is that often times we are not talking about random poor people, but rather, specific populations of poor people whose demographics and other traits do not match the highest level average. So the LLM is entirely wrong.
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And the problem is that often times we are not talking about random poor people, but rather, specific populations of poor people whose demographics and other traits do not match the highest level average. So the LLM is entirely wrong.
I don't think the LLM can be wrong. It's just giving you a random reflection of the world. Keep generating pictures until it matches your use case. The people using inappropriate pictures are the only ones here who are wrong, or can even be.
You mean until it’s not wrong?