This dude most certainly does not abide.

Like, most of the time I do.

Just not in this particular instance.

Depending on your threat model you’re almost certainly fine.

I don’t really have an opinion on this case (in terms of Syed’s guilt or innocence), but it all just seems like such a shit show. If this guy is innocent he’s been repeatedly screwed by the system.

Thanks for providing this update. You added some sources and data that I didn’t know, and your last point clearly articulates the set of likely causes of this misstep.

When I first became aware of this story my gut-reaction was “I fucking hate unforced errors like this!”; I’m now very curious why this happened the way it did. Mind you, in the grand scheme of things I suspect this is nothing more than a fleeting political blip.

I got 2.7k on a post, but I just got lucky.

That isn’t true though. Most convicted felons can’t vote, but they can run for office.

I can try to explain, but there are people who know much more about this stuff than I do, so hopefully someone more knowledgeable steps in to check my work.

What does ‘random’ or ‘noise’ mean? In this context, random means that any given bit of information is equally as likely to be a 1 or a 0. Noise means a collection of information that is either random or unimportant/non-useful.

So, you say “Compression saves on redundant data”. Well, if we think that through, and consider the definitions I’ve given above, we will reason that ‘random noise’ either doesn’t have redundant information (due to the randomness), or that much of the information is not useful (due to its characteristic as noise).

I think that’s what the person is describing. Does that help?

I’m not an Information Theory guy, but I am aware that, regardless of how clever one might hope to be, there is a theoretical limit on how compressed any given set of information could possibly be; and this is particularly true for the lossless compression demanded by this challenge.

Quote from the article:

The skepticism is well-founded, said Karl Martin, chief technology officer of data science company Integrate.ai. Martin’s PhD thesis at the University of Toronto focused on data compression and security.

Neuralink’s brainwave signals are compressible at ratios of around 2 to 1 and up to 7 to 1, he said in an email. But 200 to 1 “is far beyond what we expect to be the fundamental limit of possibility.”

Michael Lewis wrote an interesting book on this, published as an audio-book in 2018, called The Coming Storm (https://www.goodreads.com/book/show/41016100-the-coming-storm). It’s well worth the listen:

In his first Audible Original feature, New York Times best-selling author and journalist Michael Lewis delivers hard-hitting research on not-so-random weather data — and how Washington plans to release it. He also digs deep into the lives of two scientists who revolutionized climate predictions, bringing warning systems to previously unimaginable levels of accuracy. One is Kathy Sullivan, a gifted scientist among the first women in space; the other, D.J. Patil, is a trickster-turned-mathematician and a political adviser.

Most urgently, Lewis’s narrative reveals the potential cost of putting a price tag on information with the potential to save lives, raising questions about balancing public service with profits in an ethically-ambiguous atmosphere.

https://www.youtube.com/@TsodingDaily

If you’re a programmer, or think you might want to be one, I highly recommend this channel. He’s a savant at all sorts of low level things, quite funny and entertaining, and does a fantastic job of explaining what’s going on.