Yup. U is the most common letter to come after Q in general, not just in names. Because interestingly there isn't really another vowel nor letter that works after Q (Qa Qe Qi Qo Qr Ql?)

Some of the more popular Q names include Quinn, Quincy, Queen/Queenie, and Quintessa.

U is the most common letter to come after Q in general, not just in names. Because interestingly there isn't really another vowel nor letter that works after Q (Qa Qe Qi Qo Qr Ql?)

Some of the more popular Q names include Quinn, Quincy, Queen/Queenie, and Quintessa.

Bro… ok got it

It does if you include the placeholder "characters" for the start and end of each name! The most probable "name" A represents three tokens: [name start], A, [name end]. And if you generate many names using the transition matrix, you will indeed observe that the frequency of [name start] -> A and A -> [name end] matches the corresponding frequencies in the source data.

EDIT: on reflection, I agree with you. I should introduce the heatmap as a description of transition probabilities, but should avoid walking the reader through using the transition matrix to generate new "names." I should separate the topic of generating new names using the transition matrix under the (invalid) Markov assumption as a diversion. Thanks for pointing out the flaw in my explanation. I'll edit my top level comment when I have a chance!

The Y Axis is number of books. I agree with you though, That was an oversight on our part. I also don't like when graphs don't have a labelled Y-Axis. Next time we'll add them.

Watch the demo. Youtube doesn't give matches this precise.

You can try it here: https://atlas.atila.ca/

Exactly! This is how it works.

I agree it's not perfect, but remember, Youtube itself is not a library so any comparisons to real libraries will require some degree of approximation. You can think of it as an approximate estimate or my preferred term, a Fermi Estimate.

The fact that names are not memoryless is exactly why treating them as such produces such entertaining results :)

If one follows your steps, the most common outcome is one letter and there has no between-letter patterns which clearly doesn’t match the between-letter patterns of the source data.

Names are not memory-less, transition probability does not make sense.

The reason that happens is because unless someone has previously submitted a youtube video with "I gotta have more cowbell" we won't have it in our index.

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>The transcripts get added on-demand when users request to search for a video. It wouldn't make sense to index the entire database given it's large size. We're also able to get the transcripts pretty quickly, so there's no need to pre-cache the transcripts if a user has never asked for it before.A more detailed overview of how it works can be found here:

1. https://www.reddit.com/r/OpenAI/comments/10j3gzy/comment/j5jh0wo/?utm_source=share&utm_medium=web2x&context=3
2. https://atila.ca/blog/tomiwa/atlas

See: earlier comment

Oh? What part? I specifically qualified my interpretation with "want to reflect typical between-letter patterns of US girl names."

That's the point of using this viz to generate new names: generating character strings with totally realistic letter-to-letter transition probabilities is not enough to yield plausible names, or names which already exist. The generated names are often bizarre or excessively long, yet their character transition probabilities exactly reflect that of the real names in the input dataset.

Yup, here! https://atlas.atila.ca/

Thanks! The transcripts get added on-demand when users request to search for a video. It wouldn't make sense to index the entire database given it's large size. We're also able to get the transcripts pretty quickly, so there's no need to pre-cache the transcripts if a user has never asked for it before.

​

A more detailed overview of how it works can be found here:

1. https://www.reddit.com/r/OpenAI/comments/10j3gzy/comment/j5jh0wo/?utm_source=share&utm_medium=web2x&context=3
2. https://atila.ca/blog/tomiwa/atlas

1. The other interesting piece is that Library of Congress was founded in 1800 (though a fire caused it to restart it's collection in 1815).

Youtube was founded in 2005.

So in just 17 years, Youtube has amassed a collection of information that is 57% the size of the world's largest library which has been accumulating it's collection for over 200 years.

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2. I'm also Canadian. Hadn't heard of it either until we did this report. We probably haven't heard it because we likely won't need to use any of it's resources. Public libraries already do a really good job for most of our day to day needs.

3. Wikipedia's small size makes sense given that contributions are heavily restricted and have such a high bar. Imagine if every Youtube video had to be approved by a editors before or every author had to have their books approved by editors before publishing.

Yeah, I think the display of the data is interesting, I just think what you wrote about it is misleading.

That 95 means that 95% of the time, a Q was followed by a U in a name.

Oh, absolutely: the fact that this Markov assumption yields nonsensical names shows that the sequence of letters in given names are not generated by a Markov process. (The next character depends very much on previous characters, not just the current one.)

But this visualization does accurately present the relative frequencies of character transitions in actual names. Using these frequencies to generate Markov chains of characters and calling the results names is a fun diversion whose results I found entertaining.

Can you please clarify? what do you mean by it isn't clear how books on Youtube is calculated?

If you check this range you can see how we arrived at our numbers:

1. We calculated the number of hours of video uploaded to Youtube every minute from 2007-2022 source: statista
2. We found how many words are spoken per hour of human conversation source: virtualspeech
3. We calculated the number of words in the average book source: jericho writers

Then we did some calcualations with those numbers to arrive at 99,338,400 books on Youtube

Y Axis is the number of books. You're right though, the Y Axis should definitely have been there.

You can see the details of those calculations here: https://docs.google.com/spreadsheets/d/1UbekWhTLJKQj6ZLipg1R269CQ8g0ACDbzPRDFN14inc/edit#gid=52223737

Context for the Y-Axis

95 names with Qu? I can’t think of one

I don't think it's fair to say that comparing Youtube to a Library is like comparing Mt. Everest to a Cow. For one thing, there is actually a pretty clever way to estimate the amount of text on Youtube and compare it to the amount of text in a library.

Maybe, if I explain how we made the graph you'll see that it's more apples to apples than mountains to cows:

1. We calculated the number of hours of video uploaded to Youtube every minute from 2007-2022 source: statista
2. We found how many words are spoken per hour of human conversation source: virtualspeech
3. We calculated the number of words in the average book source: jericho writers

Then we did some calcualations with those numbers to arrive at 99,338,400 books on Youtube

You can see the details of those calculations here: https://docs.google.com/spreadsheets/d/1UbekWhTLJKQj6ZLipg1R269CQ8g0ACDbzPRDFN14inc/edit#gid=52223737

Can't argue with that, good take.

My point is your interpretation is flawed, because the most likely outcome of it is very far from the actual most likely name.