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Safe Crime Prediction: Encrypted Deep Learning for Less Intrusive Surveillance

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Re: Safe Crime Prediction: Encrypted Deep Learning for Less Intrusive Surveillance

#51
post #32

Earlier quoted context omitted.

Counterpoint: As Taleb points out, car accident deaths are 'thin-tailed'; that is, the rate of car fatalities is essentially fixed (and predictable by looking at past data), while terrorism is 'fat-tailed', and the number of deaths that would be caused by a dirty-bomb in Manhattan does not appear in historical data, and so it can't be 'priced' in the same way.

I have made this exact argument (quoting Taleb) but HN users are not receptive to anything other than a do-nothing approach. https://news.ycombinator.com/item?id=14262011

Unfalsifiable arguments are not especially strong.

Re: Safe Crime Prediction: Encrypted Deep Learning for Less Intrusive Surveillance

#52
post #50
post #49

Earlier quoted context omitted.

That's interesting. While studying the Reichstag Fire in grade-school history class, I ran across a quote from someone in the Nazi propaganda machine, who asserted something to the effect that exerting control over a democracy was simply a matter of manufacturing a large enough exterior threat for them to fear. Sadly, I cannot remember the quote nor the author.

It was a well-discussed idea at the time, so I'm not sure who you're referring to. But Hermann Goering famously discussed it with Gustave Gilbert: "[...] the people can always be brought to the bidding of the leaders. That is easy. All you have to do is tell them they are being attacked and denounce the pacifists for lack of patriotism and exposing the country to danger. It works the same way in any country."

That was the one! Clearly I didn't remember the details of the wording well enough to use the right search term.

Re: Safe Crime Prediction: Encrypted Deep Learning for Less Intrusive Surveillance

#53
post #44
post #20

Earlier quoted context omitted.

If the media wouldn't cover terrorism it would probably go away by itself. Israel is probably the only country dealing with it effectively.

That's like telling a bullied kid to ignore a bully (or stop giving him/her attention) and that it will make the bully go away, eventually, sometime in the future (maybe). It may work in the long-term, but it doesn't fix the problem of people hurting and being scared in the now. I'm a simple person. We have government, and we pay taxes so we can get services and have safety (that is what they tell us). So, terrorism…

The bully won't go away, but terrorism's purpose is to terrorize as many people as possible. By spreading the word about it, the media is essentially doing it a service - popularizing terror.

Did it ever occur to you that terrorism could be aided by your or other government, using the taxes you and others pay? The US is arming rebel factions in Syria now. We'll see how that turns out. They armed the Taliban with Stinger missiles during the Russian war in Afghanistan. The Taliban took over the country and then harboured Osama Bin Laden and Al Queda. US ally Saudi Arabia is sponsoring ISIS and sunni rebel groups in Yemen. Turkey also bought oil from ISIS. There's a huge scandal in the Middle East now due to Quatar allegedly supporting the Muslim Brotherhood. Have tou ever heard the quote: state sponsored terrorism? All of this is done with taxpayer money.

Re: Safe Crime Prediction: Encrypted Deep Learning for Less Intrusive Surveillance

#54
post #32

Earlier quoted context omitted.

Counterpoint: As Taleb points out, car accident deaths are 'thin-tailed'; that is, the rate of car fatalities is essentially fixed (and predictable by looking at past data), while terrorism is 'fat-tailed', and the number of deaths that would be caused by a dirty-bomb in Manhattan does not appear in historical data, and so it can't be 'priced' in the same way.

I have made this exact argument (quoting Taleb) but HN users are not receptive to anything other than a do-nothing approach. https://news.ycombinator.com/item?id=14262011

This was reply to the grand-parent comment, mistakenly posted to parent comment.
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