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

genmon.github.io

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

#101

Post by samplatt »

v7n wrote: post #81
mattlondon wrote: post #55

Finally, my interest in LLMs is piqued! Seems like everyone has been getting excited around the search or code-generation use cases ... or simply trying to make it say naughty things (boring, not interested, wake up in a few more years), but this is eye opening. The idea of this as a "universal coupler" is fascinating, and I think I agree with the author that we are probably standing at an early-90s-web moment with L…

Bit of a tangent but I also predict an explosive demand for parametric AI-first design and simulation software. OpenSCAD works right now in the sense that the produced source is valid, but asking for even something well documented e.g. an AR-15 lower receiver does not impress.

My previous boss (we were in the civil construction industry) has been saying for years that we are on the cusp of an industry-shattering breakthrough with generative engineering.

We should be able to, by now, input terrain maps, account for local water ways & rainfall, and Autodesk's Brobdingnagian suite of tools should be able to spit out a whole new suburban package, engineered to local country/city engineering standards.

The main thing that's holding is back is the complexity of engineering standards, and its implementation in the software. But all that'll take is time & money.

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

#102

Post by malborodog »

nextaccountic wrote: post #92

> Web scraping is usually pretty tedious, but I found that I could send the minimised HTML to GPT-3 and get (almost) perfect JSON back: the prompt includes the Typescript definition. Could you share the prompt? Or, if OP can't share, does anyone have ideas for a prompt to do something like this?

The prompt is probably simple, but the bigger challenge is that even a minified html of a typical web page would be more than the 4k gpt token limit

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

#103

Post by malborodog »

genmon wrote: post #13
dvt wrote: post #8

The BBC offers pretty exhaustive RSS feeds ( https://podcasts.files.bbci.co.uk/b006qykl.rss ) so I'm not exactly sure what ChatGPT even did here (Maybe the Dewey classification? Which is of dubious usefulness).

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.)

Have you considered making the repo public so other folks (well, certainly me!) could extend and adapt the idea to other use cases? It's just very exciting work and it would be fun to see the code.

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

#104

Post by lisasays »

At the same time I asked for a Dewey classification... and it worked.

May I ask - how do you know exactly?

Specifically - with what level of accuracy? And how is it assessed?

At first glance it does seem to be highly accurate - except where it isn't:

    650 Management and public relations (1)
    - Caxton and the Printing Press 18 Oct, 2012
Which is not to knock what you're doing - because overall the performance does seem to be quite impressive (especially considering the effort invested).

Still - there's that "last mile".

So it would be nice to now how the performance compares to that other topic modeling approaches. And to what extent LLMs actually provide a boost.

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

#106

Post by chrisweekly »

nl wrote: post #97
mattlondon wrote: post #55

Finally, my interest in LLMs is piqued! Seems like everyone has been getting excited around the search or code-generation use cases ... or simply trying to make it say naughty things (boring, not interested, wake up in a few more years), but this is eye opening. The idea of this as a "universal coupler" is fascinating, and I think I agree with the author that we are probably standing at an early-90s-web moment with L…

We are months away from being able to do this with images too. All the pieces are there, and multi-modal (smallish) large language + image models are already being used in research labs; eg MS Kosmos-1[1]. Check out the visual IQ test results in the paper. Kosmos-1 is only 1.6B parameters. When that or similar models scale out to 50B_ params they will be pretty amazing. [1] https://arxiv.org/abs/2302.14045

Linked article / brief mentions interleaving of text and images as essential for certain kinds of learning; that particular intersection reminds me of the profoundly compelling multimodal book by Nick Sousanis, "Unflattening". Highest possible recommendation.

https://en.m.wikipedia.org/wiki/Unflattening

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

#107

Post by akras14 »

specproc wrote: post #14

I love In Our Time, a real BBC gem. I've been meaning to pull all the audio for a while and this has inspired me. A fun thing to do would be to pass through Whisper, a great corpus to play with.

Whisper worked great on an old speech by Winston Churchill. Haven’t looked into the time limit.

API used and results can be found here: https://techtldr.com/transcribing-speech-to-text-with-python...

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

#108

Post by nl »

fnordpiglet wrote: post #93
flir wrote: post #58

Earlier quoted context omitted.

This is great. I'll certainly be thinking of "classification" uses for ChatGPT in the future. Thinking out loud: Add the experts, not just the reading lists. They're a jumping-off point into academic-paper-space. What have they published? In what journals? Who have they collaborated with?

Here’s a powerful use - content moderation. Today we literally traumatize content moderators with the dregs of the human mind. Chatgpt is fairly good at identifying the classification of content on many dimensions, including the ones it’s actively screens for. Regardless of how you personally feel about content moderation, I would be happy to see humans not have to be actively involved in it and face the traumas they…

OpenAI have a content moderation endpoint for this too.

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

#109

Post by nl »

genmon wrote: post #32
CrypticShift wrote: post #29

Earlier quoted context omitted.

Excellent! I'd love to see a script that sends a list of "descriptions" (1-100 words) to ChatGPT and directly gives you back a ready-made (embedding vectors closeness) map in a (textual) graph/chart format (like your above map or your plot https://interconnected.org/more/2023/02/in_our_time-PCA-plot... )

It turns out that "closeness" is usually hard to visualise/explore when you're dealing with a 1,000-dimensional space... and PCA has the failures mentioned above. It's weird -- it's locally useful to navigate, and at a high level kinda useful, but only if you squint and don't look at the problems. So I feel like a fisheye visualisation would be appropriate? That's something that I'm exploring in other projects.

TSNE or UMAP (as others have mentioned) is good but also take a look at the tensorflow projector. You can host your own and/or pass custom data to it.
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