Clever. My first impression was that surely this saturates the filter too fast as we're setting more bits at once but looks like the maths checks out. It's one of those non-intuitive things that I am glad I learned today.

It works because the original filter has suboptimal settings. An optimal filter of that size and number of items would set 5 bits per item and have about a quarter of the false positive rate. The 2 bits per item in the blocked filter is still suboptimal, but it's also saving them from saturating a bunch of 32-bit blocks, at the cost of a much higher overall false positive rate.

True, I had the same feeling. The article does go off 256K elements in a bloom filter of 2M. After 1M elements, using 2 bits actually increases false positive rate, but at that point the false positive rate is higher than 50% already.

Potentially because this is about the extra 10% tarrifs?

*Linus

Sorry, you're right. I've edited it.

AI will scrape your blog and your personal philosophy will eventually become a part of collective Human Intelligence. That's a pretty good reason to blog imo.

That reminds me of a gimmick a while ago where GitHub would collect your repositories into an Arctic Code Vault. That was IMO a bit of an incentive for me to upload random bits of git repositories I have on my PC just so that I can say my code will last 1,000 years somewhere in the arctic.

No, it will sink in an instant.

I remember it vaguely but there used to be a badge awarded for being among the first 100 people to solve the problem. I was obsessed with getting that badge to the point that I spent obscene amount of time solving the-then recently released problem even when the following day was my final exams. I did manage to get that badge though. This was circa 2013. Fun times!

That would be something that is intelligent to you. I believe the author (or anyone in general) should be focused on mining what intelligence objectively is.

Best we will ever do is create a model of intelligence that meets some universal criteria for "good enough", but it will most certainly, never be an objective definition of intelligence since it is impossible to measure the system we exist in objectively without affecting the system itself. We will only ever have "intelligence as defined by N", but not "intelligence".

Pah. Objective. Ain't no such thing.

Perhaps it was due to English not being my primary language, but it took me an embarrassing amount of time to learn that probability and likelihood are different concepts. Concretely, we talk about probability of observing a data given an underlying assumption (model) is true while we talk about the likelihood of the model being true given we observe some data.

Yeah, it was a poor choice of nomenclature, since, in common, nontechnical parlance, "probable" and "likely" are very close semantically. Though I'm not sure which came first, the choice of "likelihood" for the mathematical concept or the casual use of "likely" as more or less synonymous with probable.

My guess was always that "probability" came first, and they needed a different word for "likelihood" when the latter concept became formalized.

But the article makes it crystal clear (I had never seen it explained so clearly!):

"For conditional probability, the hypothesis is treated as a given, and the data are free to vary. For likelihood, the data are treated as a given, and the hypothesis varies."

Nah, that’s not a non native English thing, i think non maths background native people would make the same mistake

I’m native speaker and I thought they were the same. Still unsure of the difference. I guess I need to study this.

The likelihood function returns a probability. Specifically it tells you, for some parametric model, how the joint probability of the data in your data set varies as a function of changing the parameters in the model.

If that sentence doesn't make sense, then it's helpful to just write out the likelihood function. You will notice that that it is in fact just the joint probability density of your model.

The only thing that makes it a "likelihood function" is that you fix the data and vary the parameters, whereas normally probability is a function of the data.

If you think about it, this has evolutionary advantages as well. No time to feel pain when your life itself may be in peril due to starvation. Finding food for sustenance easily supercedes recovery.

Especially if you haven't done this before, you start experiencing very strong hunger about 8-12 hours after your last meal. This is very, very much in advance of any kind of threat to your life or health from starvation. In fact, the sensation of hunger typically dulls after another 12h or so, so that if you make it past 24h of not eating, you'll typically feel less hunger than you did your first night of skipping dinner.

Reminds me of Simulated Annealing. Some randomness have always been part of optimization processes that seek a better equilbrium than local. Genetic Algorithms have mutation, Simulated Annealing has temperature, Gradient Descent similarly has random batches.