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Thrillhouse ,

I don’t understand how Israel can sit there and justify being evil by claiming everyone has ties to Hamas. Like yeah, they’re the terrorist group ruling Gaza, I’m sure everyone knows someone involved with them.

By that logic, since the IDF has been terrorizing Palestine for decades, and since military service is mandatory in Israel, are there no true civilians and only terrorists in Israel too?

Of course that’s silly, but it’s a good way to point out how flawed Israel’s logic is when they always claim casualties in Palestine have ties to Hamas.

postmateDumbass ,

Leaked AI code:

10 if israeli goto 30

20 kill

30 end

phoneymouse ,

AI doesn’t know who is an aid worker, so chooses targets like WCK volunteers in armored vehicles transporting food to starving people.

neuropean ,

That’s strange, I could have sworn they were using a magic 8-ball.

tal ,
@tal@lemmy.today avatar

The officials, quoted in an extensive investigation by the online publication jointly run by Palestinians and Israelis, said that the AI-based tool was called “Lavender” and was known to have a 10% error rate.

So, there’s pretty much no information to decipher what it’s actually doing. But I think that one could at least use a human baseline. For a human in a similar role, assuming that a human can approximate whatever it’s doing, what’s the error rate?

alcoholicorn ,

The error rate for inventing reasons you can bomb someone? Zero, whether it’s done by human or computer.

FuglyDuck ,
@FuglyDuck@lemmy.world avatar

So, there’s pretty much no information to decipher what it’s actually doing. But I think that one could at least use a human baseline. For a human in a similar role, assuming that a human can approximate whatever it’s doing, what’s the error rate?

the verge had a piece on it.

Lavender was trained to identify “features” associated with Hamas operatives, including being in a WhatsApp group with a known militant, changing cellphones every few months, or changing addresses frequently. That data was then used to rank other Palestinians in Gaza on a 1–100 scale based on how similar they were to the known Hamas operatives in the initial dataset.

Basically, they’re looking at habits and social connections and the AI matches people.

part of the problem?

To build the Lavender system, information on known Hamas and Palestinian Islamic Jihad operatives was fed into a dataset — but, according to one source who worked with the data science team that trained Lavender, so was data on people loosely affiliated with Hamas, such as employees of Gaza’s Internal Security Ministry. “I was bothered by the fact that when Lavender was trained, they used the term ‘Hamas operative’ loosely, and included people who were civil defense workers in the training dataset,” the source told +972.

shit data in, shit data out.

tal ,
@tal@lemmy.today avatar

Hmm.

I believe that law enforcement has done that sort of thing for a long time, built databases to look for correlating factors and among relationships. And it sounds like they’re explicitly writing up the criteria, else they probably wouldn’t be able to rattle them off. So I kind of doubt that they’re using machine learning to find new criteria.

If I had to guess from your text, what they did is had people come up with all the criteria that they could think of that’s likely to indicate that someone is Hamas. Then they had some database of known Hamas figures, and ran their classifiers against it, let the system figure how weightings for each of those criteria. I don’t know if that last bit is standard practice for law enforcement software, to identify likely suspects, but I can believe that it might be.

“AI” might be a slightly ambitious term to use for that. I have used SpamAssassin, which uses Bayesian classifiers to identify spam, for decades. It does something comparable, but I don’t think that people have generally called SpamAssassin “AI”.

FuglyDuck ,
@FuglyDuck@lemmy.world avatar

So it’s machine learning.

They have lots of data -social media foot print, addresses, names of friends, coworkers, etc. who they call, where they go for coffee; or happy hour after work, quite literally everything they can get on these guys.

They the. Give it a known list and tell the machine to look for patterns (like switching burner cell phones every so often.) consistent between all of them.

It even weights lower strength correlations as softer evidence.

They then take that and run it against everyone they have in their database. And it spits out people that match the same things.

As for it being artificial intelligence- it is, just not general AI (Like Data in Star Trek, R2-D2 in Star Wars or Kryzen in Red Dwarf). They’re more like idiot savants that are very good at this one task and suck and literally anything else.

The problem is mostly in the shit data it was programmed with; and an assumption that it would always be right. It can recognize patterns, but there’s always some natural variation in the pattern.

Minotaur ,

Seemingly the AI from Goldeneye 64

Immersive_Matthew ,

This just underscores that it is not AI we should fear, it is how some choose to use it.

tal ,
@tal@lemmy.today avatar

I mean, I’d assume that people are going to have a go at using it for pretty much everything. If not LLMs now, then more-sophisticated systems down the road.

Car ,

Insider look of the AI assistance:

“Here’s a 100 square meter zone of unmolested real estate. Would you like me to generate a press statement for why this is an important and legitimate target?”

some_guy ,

AI: All of them.

Israel: OK!

Fuck Israel.

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