Show HN: BBC “In Our Time”, categorised by Dewey Decimal, heavy lifting by GPT

On a related note, has anyone noticed the growing use of "pretty" as a hedge against ever being wrong? Also, "not", as in "not the fastest" or "not the hottest".

It could be 46C/115F outside and you say "wow, today is unbearably hot", to which someone retorts "nah, it's fine. It's not the hottest day". That's pretty good hedging. You can make infinite technically correct statements this way without ever saying anything meaningful

There's more on the About page but in summary: extracts the synopsis, guests (name, affiliation), and reading list (title, author, publisher, year) as structured data. Not massively hard with a bit of web scraping, but tedious and results in brittle code -- this took 20 minutes to write the prompt plus 3 cents per episode.

https://genmon.github.io/braggoscope/about

(GPT-3 not ChatGPT for the model.)

I've been considering this, but my assumption is that it would be tripped up by the specialist words.

I wonder... is there a way to "prime" Whisper (e.g. with the embedding of the episode synopsis) so that it "listens out" for words related to a particular topic? I haven't looking but this would be neat!

What a beautiful thing.

Now that's an interesting regression! I don't remember seeing it there before.

(The worst I've noticed before has been Lawrence of Arabia under History of the Ancient World. Very much 20th century really.)

Several other classifications are arguable -- which I think shows one of the limitations of this technique: it's not possible to iterate + improve.

So instead I've been wondering about using the embeddings of each episode synopsis, and comparing to the embeddings of Dewey subdivisions. I should be able to tune the results better that way.

There's also a technique from Google called CAVs (Concept Activation Vectors) that I'm intrigued about trying -- would love to hear if anybody has experience using this

https://arxiv.org/abs/1711.11279