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Show HN: BBC “In Our Time”, categorised by Dewey Decimal, heavy lifting by GPT (genmon.github.io)

688 points by genmon ↗ HN
I'm a big fan of the BBC podcast In Our Time -- and (like most people) I've been playing with the OpenAI APIs.

In Our Time has almost 1,000 episodes on everything from Cleopatra to the evolution of teeth to plasma physics, all still available, so it's my starting point to learn about most topics. But it's not well organised.

So here are the episodes sorted by library code. It's fun to explore.

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.

At the same time I asked for a Dewey classification... and it worked. So I replaced a few days of fiddly work with 3 cents per inference and an overnight data run.

My takeaway is that I'll be using LLMs as function call way more in the future. This isn't "generative" AI, more "programmatic" AI perhaps?

So I'm interested in what temperature=0 LLM usage looks like (you want it to be pretty deterministic), at scale, and what a language that treats that as a first-class concept might look like.

176 comments

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valgaze
This is a really interesting use-case

Applying "transformations" or classifying data in this way without having to setup a lot of detail-work seems like a real labor-saver/multiplier

lerchmo
My company has gone years wanting our product catalog to have structured data around our products but not going through the tedium of extracting it all. about an hour of prompt tweaking and it can pull, normalize, summarize and output valid json from all of our products. Basically pulling it out of a big unstructured html blob.
genmon
I've started thinking about LLMs as a "universal coupling", if that makes sense? It's wild to be able to conceive of APIs to plain text, and natural language queries on structured APIs, but that's what we've got.

My mind was really opened by Nat Friedman's work in GPT for browser automation: https://github.com/nat/natbot

And of course using langchain/ReACT.

So different from ChatGPT and (imo) way more intriguing.

mentioned in this blog post: https://interconnected.org/home/2023/02/07/braggoscope

haolez
Wait. Is temperature=0 "pretty deterministic", or is it deterministic?
LeoPanthera
It’s my understanding that 0 is completely deterministic, except for when the model is updated, which does happen occasionally.
valgaze
@haolez I just tested w/ davinci-003 w/ temperature to 0

Prompt: https://imgur.com/YtQ4fbf --

Reveal the question marks in an interesting way:

The dog goes ????????????? --

With temperature == 0, it consistently ("pretty deterministically"?) generated "woof!"

ex. The dog goes woof!

gwillen
Their docs say (somewhat recently updated, I think) that even 0 is not perfectly deterministic in all cases (though it's very close). Some people had previously observed this and speculated that it was some kind of floating point roundoff issue, when two outputs have almost identical scores.
Jensson
GPU's often do floating point computations in nondeterministic order for performance reasons, so you would get small differences even for exactly the same order. It is probably that.
genmon
In the strict definition, it's deterministic: you get the same response for the same prompt, each time (given the exact same model).

But the prompt is parameterised. The bulk of the prompt is requesting a list of guests and speakers to be extracted, and the episode synopsis is appended as the "parameter". And I've noticed that the variation of the parameter changes what the overall prompt returns... so it might start being less reliable at responding with valid JSON, for example.

So it's instance-deterministic but, across a range of parameters, class-fuzzy, if that makes sense?

acchow
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

messe
> Also, "not", as in "not the fastest" or "not the hottest".

Mid twenties Hiberno-English speaker here and that's always been a fairly common form of hedging, and I've not noticed an increase.

tedsanders
Temperature=0 is not perfectly deterministic for the OpenAI API.

It's mentioned briefly in the OpenAI text completion guide: https://platform.openai.com/docs/guides/completion/introduct...

If you have two possible tokens with probability 40% and 30%, you'll always get the 40% token at T=0. But if you have two possible tokens at 40% and 39.99%, you may get the 39.99% token on occasion, even if at T=0. (Numbers illustrative.)

minkzilla
Where does the inherent randomness come from?
ChatGTP
It's injected into the model, you can deviate from no randomness to get "creative".
minkzilla
Isn't the temperature the "injected" randomness? The way I imagine it would be somewhere in the model you would do

input += (Math.random - 0.5) * Coefficient * temperature

so setting temperature to 0 would mean no randomness. On thinking further about why there is inherent randomness I believe it is from a lack of associativity in floating point operations. They obviously do A LOT of parallel floating point operations.

maccaw
Well technically everything is deterministic.
PaulHoule
What's the prompt?
valgaze
From what I understand:

- Typescript definition (turn raw html markup >> well-formed JSON)

- Dewey Decimal classification/score

So probably combination of those two

genmon
One of the prompts is

  Extract the description and a list of guests from the supplied episode notes from a podcast.

  Also provide a Dewey Decimal Classification code and label for the description

  Return valid JSON conforming to the following Typescript type definition:

  {
    "description": string,
    "guests": {"name": string, "affiliation": string | null}[]
    "dewey_decimal": {"code": string, "label": string},
  }

  Episode synopsis (Markdown):

  {notes}

  Valid JSON:
(And the completion tends to be JSON, but not always.)
lerchmo
I like the typescript definition, rather than example json that I normally use.
genmon
Credit where it's due: I was working with structured data as JSON for the completion, and the Typescript definition hugely increased reliability. I took that from helpful advice (on Twitter) from Noah Brier who afiak came up with the approach:

https://brxnd.substack.com/p/the-prompt-to-rule-all-prompts-...

dvt
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).
genmon
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.)

malborodog
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.
specproc
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.

genmon
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!

taberiand
I haven't tried, but the Open AI docs mention priming on the whisper model being available

    prompt
    string
    Optional

    An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language.
genmon
I hadn't noticed that, very much appreciated.
coder543
In my experience, Whisper does a great job even with specialized terminology. It won't catch everything, but I think it will exceed your expectations. One of the hardest things about Whisper is choosing which model to use; they offer a variety of sizes, and sometimes the smaller ones do better than the larger ones. It's worth trying a few different models and deciding what is best for each particular application.

I will also say that I've personally been unimpressed with the new "large-v2" model, even though it supposedly scores better. The original "large-v1" model seems to work better than the "large-v2" model in the audio clips I've been testing Whisper against, but results will vary. In general, I find I'm really happy with what "small.en" and "medium.en" will emit, and they're much faster than the large models. (The ".en" models are specialized to English, and usually perform better for strictly English input, whereas the non-".en" models are trained on multiple languages.)

simonw
I'd still like the ability to prime Whisper. I used it to transcribe a podcast episode I appeared on recently and one of the fixes I had to make was that ChatGPT came out as "chat GPT" every time it was mentioned: https://simonwillison.net/2023/Mar/7/kqed-forum/#kqed-forum

Update: turns out this exists already: https://platform.openai.com/docs/guides/speech-to-text/promp...

coder543
I recorded myself saying a few sentences from that transcript, then fed it through different Whisper models. "small.en" and "large-v1" both generated "chat GPT", "large-v2" generated "chat-gpt", but somehow "medium.en" correctly generated "ChatGPT".

This was the same audio sample fed through each of those four models, with no "prompting" as you're discussing.

If I add "--initial_prompt ChatGPT", then all four models are able to get the spelling correct.

Regardless, I don't think "chat GPT" versus "ChatGPT" is a huge deal. There will always be some level of uncertainty and ambiguity in the transcript, and even books written by humans always have a few typos get past multiple stages of copy editing. Perfection is virtually unachievable, but you can always scroll through the transcript and make some edits after the fact, if desired. Maybe some future model will magically eliminate all typos.

simonw
Yeah it wasn't a big problem for me - I had to do a bunch of other tidy-ups on the transcript anyway to add things like the name of the person who was speaking.

I cleaned that bit up with a bulk replace of "chat GPT" with "ChatGPT" in VS Code.

aardvark179
I see several very dubious classifications. Shouldn’t the great stink be under civil engineering rather than agriculture, and why is Plato’s Atlantis under computer science?
genmon
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

theodorewiles
FYI you can get dewey decimals programmatically by hacking the OCLC API: http://classify.oclc.org/classify2/api_docs/classify.html

this says you need an API key but I think I found some way to call this without one… You might be able to improve classification by either incorporating the dewey decimals of the books mentioned on a podcast or fine-tuning a model based on known book titles (or maybe there are book summaries somewhere) to known dewey decimals from OCLC.

once_inc
There's also an episode about aliens under 000.
genmon
Some unlinked features...

If you put the Dewey division in the URL, the directory auto-opens. e.g. here are episodes about prehistoric life (my current jumping-off point)

https://genmon.github.io/braggoscope/directory#560

There's a visual map of episodes. After principal component analysis of the episode embedding vectors, these are the most significant two components as the x,y

https://genmon.github.io/braggoscope/map.html

(it's not super useful tbh -- e.g. the Manhattan Project and the Cambrian Explosion have the same x,y... presumably because they are both about explosions?)

Many episodes have a reading list, and these are all linked to Google Books (so you can purchase/check out from a library), e.g. this episode page

https://genmon.github.io/braggoscope/2022/10/20/the-fishtetr...

There are ~4,600 books, and I have ~88% coverage on getting a Google Books page from the original data. Any ideas about what to do with this big list of academic-recommended books v welcome!

CrypticShift
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...)
genmon
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.

gwern
I wouldn't necessarily reach for PCA. No reason to think that the first two principal components necessarily encode anything particularly interesting. If you want to lay out each point in 2D in a way which keeps similar points nearby, something like t-SNE is worth a try - visualizing embeddings is what it was invented for.
genmon
Excellent, new to me and I'll give it a go, thanks!

I gravitate to PCA for terrible reasons (undergrad so it's what I think of first) and like you say, it's beguiling yet disappointing, the components rarely have any human meaning.

nl
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.
PeterisP
The word game of 'semantle' (https://semantle.com/) is a nice way to get an intuitive grasp of how unintuitive the closeness in a highdimensional space is, as you're required to guess a word based on it's semantic similarity (according to classic word2vec metric) to your previous guesses.
p0pcult
Love the visual map. What does color mean? Any way to do a 3rd PC, and put the visualization in a cube one can toy around with?
sacrosancty
Your PCA is awesome. The horizontal axis seem to go from people (high level nature?) on the left to physics (low level nature?) on the right, while the vertical axis seems to go from individuals and particles (small things?) at the bottom, up to civilizations and the universe (big things?) at the top. Maybe the Manhattan Project and Cambrian Period (not explosion) are together because they're both big things in their fields and physics and evolution are near each other horizontally?
flir
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?

fnordpiglet
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 must live with for where the moderation happens. I’m sure it’ll get things wrong, but humans do too.
chezelenkoooo
I'm sure I remember Zuckerberg talking about this very thing in one of the Congress interviews he had.

I don't disagree with you, the content producers are always going to outnumber the moderators by a massive margin. It makes reasonable moderation very difficult.

nl
OpenAI have a content moderation endpoint for this too.
amiga386
Automating your moderation policies is a cop-out. It isolates you even further from the peons whose lives you're controlling.

European humans have the right for any automated decision affecting them to be reviewed by a human. We do not want a world where a tiny handful of people, getting fewer by the day if it's Twitter, have untrammeled control over what billions of people can say, see or do. We can't let businesses do what they like, change their policies on a whim and have it instantaneously applied by automatons, if it's bringing misery to millions of people.

Remember those old-fashioned things called laws? Courts? Judges? Legislators? The things we used to use for deciding what people could/couldn't do, before tiny companies with billions of users applied their own de-facto laws with no oversight, governance or accountability. Move fast and break things!

fnordpiglet
1) you can have human reviewed appeal and have automated moderation 2) a provider of a service is not a provider of a public commons, nor is the service being confined to a specific type of discourse preventing anyone from doing anything in their life. You don’t have the right to go to a movie theatre and use it to practice your singing recital during the movie - the theatre owner can and will kick you out. This doesn’t trample on your rights - you can still practicing singing, just not there. Likewise you can still spew racism and talk about child sexual abuse in your home all you want. But no one is obliged to provide you a platform for that. 3) I see nothing superior to a capricious and biased human judging my content vs a capricious and biased model judging my content. The human, however, will be traumatized by the “millions of people” made miserable by their inability to spew hate and share child porn on Facebook. 4) we still do depend on judges and laws. Today the judges as laws say you not only have the right but the obligation to create a space relatively safe. In your European utopia speech and thought is even more limited by law than in the US, but in both locales forum providers are held to a standard. Right now in the US there’s a major case in front of the Supreme Court which will determine whether these companies can be held liable directly for failing to sufficiently moderate - if it passes, they’ll be even more obliged to “purify” their platforms of anything that anyone might feel compelled to sue them over. But even now all providers have to conform to the laws and judgements of the lands they operate in, and those lands say it’s not only ok to moderate, but it’s mandatory. So, your final point really, truly, makes no sense.
Tarq0n
Love the plot, for high dimensional embeddings try UMAP for a better result, maybe T-sne too if you're interested in clusters.
boringg
Also a big fan of the show -- cool project!
astroalex
I love this project!!

Ever since my partner and I discovered In Our Time a few years back, it’s been our go-to podcast to listen to together. Part of the allure is that the archive is so vast, but that makes it hard to browse.

My partner made her own archive of In Our Time here, if you’re interested: https://shelby.cool/melvyn/

She used Wikipedia to find and categorize each episode. I also really like that she indexed episodes by guest, too. Certain guests are REALLY good and have been on many episodes.

Super excited to see someone else make an archive; we’ll definitely be exploring yours!

zeristor
That's really impressive.

I've had a mild idea for ages to have a sort of annotated In Our Time. Listening to the podcast on a webpage as the text rolls by links could appear to explain or give background to the item or person being discussed.

SMIL is the multi-media mark up language. Generally if one thinks of something there's someone on the Internet who has already had that idea.

Additional: I think the BBC is very careful about transciptions. They've sold a book of the transcripts of several episodes, but it would be a great way to go through a subject.

bscphil
> She used Wikipedia to find and categorize each episode.

This is a really clever use of an existing dataset. I clicked through before reading this and was stunned by how thorough the tag set was. Even more obscure things like "Alumni of Magdalen College, Oxford" have multiple episodes. I'm going to keep this in mind on future projects for sure.

theodorewiles
I emailed you but did something similar and posted to Show HN a while back -

https://weekend-collection.s3.amazonaws.com/Catalog+-+Feb+17...

jccalhoun
It is an interesting approach and I appreciate it. However, an alphabetical list would be more useful for me when I am interested in topics and might not know where they are classified.
jack_riminton
In Our Time has been a life-long companion, one of things that makes me proud of the BBC
genmon
Have you read the New Yorker articles about it?

On _In Our Time_ -> https://www.newyorker.com/culture/podcast-dept/escape-the-ne...

Profile of Melvyn Bragg -> https://www.newyorker.com/culture/the-new-yorker-interview/t...

Both well worth your time.

jack_riminton
That NewYorker summarises it nicely. I can’t stand the over-produced stuff on NPR for example. In Our Time has no politics, no hook to get you listening for the next episode. It’s just a lovely small window into academia. Above all it makes things simple without dumbing it down
gerdesj
"It’s just four intelligent people in a studio, discussing complex topics that are, as a friend of mine once said of Bragg’s openers, aggressively uncommercial."

Nicely put by the New Yorker.

EamonnMR
In Our Time is one of the best podcasts out there. Are there any similar ones?
maccaw
Love it - also a huge fan of In our Time.

V2 would be:

- Transcribe through Whisper - Semantic search

DC-3
Nice project OP. I also love In Our Time.

Some favourite episodes off the top of my head:

* Wilfred Owen - https://www.bbc.co.uk/programmes/m001df48

* The Evolution of Crocodiles - https://www.bbc.co.uk/programmes/m000zmhf

* The May Forth Movement - https://www.bbc.co.uk/programmes/m001282c

* The Valladolid Debate - https://www.bbc.co.uk/programmes/m000fgmw

* Gerard Manley Hopkins - https://www.bbc.co.uk/programmes/m0003clk

* Henrik Ibsen - https://www.bbc.co.uk/programmes/b0b42q58

* Wuthering Heights - https://www.bbc.co.uk/programmes/b095ptt5

And finally, in which three mathematicians heroically attempt to explain asymptotic analysis to (septuagenarian novelist and cultural broadcaster) Melvyn:

* P v NP - https://www.bbc.co.uk/programmes/b06mtms8

genmon
Great list! Some personal faves in return, in no particular order:

- The evolution of teeth https://www.bbc.co.uk/programmes/m0003zbg

- The fish-tetrapod transition https://www.bbc.co.uk/programmes/m001d56q

- The late Devonian extinction https://www.bbc.co.uk/programmes/m000sz7x

- The American West https://www.bbc.co.uk/programmes/p00548gg

- Metamorphosis (Ovid) https://www.bbc.co.uk/programmes/p00546p6

- Politeness https://www.bbc.co.uk/programmes/p004y29m

- The Bronze Age collapse https://www.bbc.co.uk/programmes/b07fl5bh

- Doggerland https://en.wikipedia.org/wiki/Doggerland

dendrite9
I really enjoyed the recent Superconductivity episode. Especially hearing Melvyn say 'Good god' half way through.
martinpw
I like the way they keep the very end of where the producer comes into the studio and asks the guests politely if they want coffee or tea. Something very satisfyingly British about that.
rosetremiere
One thing I am slightly wary about after listening to quite a few episodes is Melvyn Braggs seems to be a bit too patriotic oftentimes. It's nothing really obvious, but you hear a lot of "we" when talking about positive aspects of english history, and a lot of focus on all english innovations compared to other countries. Maybe I'm wrong though.
petesergeant
Seems pretty reasonable for a national broadcaster, no? Also British encompasses more than just the English, unless you specifically mean he’s excluding the other Home Nations…
rosetremiere
Sorry, I didn't mean English more than British (the distinction being a bit too subtle for me to make). It's "par for the course" I guess for a national broadcaster, but:

* That doesn't mean it's not less than ideal.

* It's something one might want to bear in mind, when listening, since it might introduce some kind of a bias to the whole thing (maybe the other comment about number of episodes about european vs african history is a good argument in that direction)

mellosouls
Wrt bias it's actually a refreshing change for some of us to hear the voice of a traditional (old-school) leftist, as they tend to be more interested in actual social problems rather than the made-up ones that infest modern discourse, and - perhaps - shift the centre so much that plain common-sense is now perceived as "bias".
DC-3
I am also an english patriot so that doesn't bother me.
gadders
He's a Labour peer in the House of Lords. They're not known as a group for rampant patriotism.

My criticism of him his how much he struggles with anything of a maths or scientific nature.

rosetremiere
Good to know, it might be me putting such thought in his mouth then!
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mattlondon
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 LLMs as a function call (the technology is kinda-there and mostly-works, and people are trying out a lot of ideas ... some work, some don't).

My mind is racing. Thanks for the epiphany moment.

ketzo
It’s awesome for formatting and structuring.

Copy and paste a bunch of styled JS components -> get back out a single CSS sheet

Paste in a markdown document -> get out the same thing in HTML

Fun stuff like that.

chaxor
Ah yes, you now too can have the wonders of pandoc - now on sale! Retail pandoc price of 20MB, now selling for only 3TB with the exclusive offer of ChatGPT!
seagullriffic
I don't want to be mean, but this seems like the famous Dropbox/rsync comment.

The value here is how easy it is, and the fact that a generalised model can take the place of a (well-engineered) specialised tool.

v7n
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.

TaylorAlexander
I did not realize it could do that. I asked it to generate a cube with a hole through the center and it really struggled to succeed. Interesting concept though!
martyvis
ChatGPT has a pretty good handle on G-code (the machine code to drive CNC and 3D printers). A machinist friend of mine and myself got it to write G-code to create a shape from a block of material, adding holes and arrises and so on. To top it off it wrote some decent poetry about 2 mates discussing G-code over a few beers.
fnordpiglet
Fusion 360 has a generative AI design extension already. Never used it, I’m too much a poor to use something like that.
ewalk153
There is a free hobby plan for Fusion 360 that I use for my personal projects.
fnordpiglet
I thought it doesn’t have the generative AI extension? When I looked at the plan comparison it was a part of the commercial.
samplatt
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.

brutusborn
I'm about to start work in this space. Some companies are getting close to full building automation, Augmenta seems to me to be closest / have the best approach. [0]

Although the complexity of building standards is a challenge, the speed and quality of various generative algorithms are still major limitations.

[0] https://www.augmenta.ai/

yarbas89
Thanks for linking augmenta. I was wondering if you know of other similar companies? I'm an experienced structural engineer who's just completed an MSc in Comp.Sci. I want to combine the two disciplines and so far I've only been looking for consultants who utilise parametric design (Rhino + Grasshopper).
brutusborn
No problem!

I don’t know of any other multidiscipline generative design companies, but for process plant design there is OptiPlant and PipeStream. There are a lot in the architecture space but these are mostly for high level conceptual design.

In my experience many structural engineers code and use simple parametric models. Finding a company using actual generative design algorithms is still rare but there are definitely engineering teams out there who value comp.sci skills.

nl
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

chrisweekly
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

EZ-Cheeze
That is the truly mind-blowing thing that's coming.

I am currently creating reference images for a game which I expect within 6-12 months I'll be able to feed into a multimodal ChatGPT to create 3D assets out of the 2D pics.

We'll be able to conjure up worlds at a whim - so start imagining them already!

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basch
Mark gets a lot of flack for metaverse, but can imagine world design where you start in a blank room and describe what you want around you, and it appears. Like the Matrix loading white room. And with voice recognition and eye tracking (and brain scans), how close are we to “you have to use your hands? It’s like a baby’s toy.”
EZ-Cheeze
YES!!! That's exactly what's coming. I'm getting a head start by envisioning a rich world and creating pictorial references for it along with text descriptions. But it's a tiny head start: once the "holodeck" technology comes along, we'll all be able to create anything with just natural language and correcting anything the model got wrong. ITERATIVE CHISELING YO!!!

By the way, I am still using SD 1.5 inpainting.ckpt - thank you Runway for releasing it, it's perfect for my needs and abilities. I never even tried SD 2 and later ones - heard they worked completely differently and I'm too busy creating to be relearning.

sho_hn
This was always the coolest capability of Star Trek's holodecks, not the projector technology. Super cool that we might just get there within the lifetime of someone who watched the show in the 80s.

My favorite Star Trek episode has always been "Identity Crisis". Not because it's one of the good ones, it's pretty clunky. But it contains a fantastic 5-7 minute long montage featuring Geordi La Forge interacting with computers (by touch, voice and on the holodeck) to solve a murder mystery, analyzing and live-manipulating 3D "holo footage" to discover a vital clue. Whoever imagined that sequence is the hero of my childhood and perhaps the reason I became a software engineer, doing an oddball mix of HMI and systems engineering.

There's so much in that sequence. The free mixing of different input modes, the complementary collaboration between a human and an AI system, carrying state with you from room to room. Analyzing and generating. Following instructions and making suggestions. Powerful inference, precision of control.

We're getting close now!

basch
I remember having that epiphany in 2017, reading the original google transformer press release. I remember thinking “they taught computers to understand!, and someday soon i’ll be able to translate anything into anything.” And not like translating “languages” but translating contexts and concepts.

LLMs really shine when you try and combine two previously untouched things or map one bit of knowledge it a common pattern. Eg “summarize Mary Shelly’s Frankenstein as if it was an episode synopsis of Magic School Bus.” “Now do the same for Dracula.”

I remember my mind racing so much that day. With all the ideas of things it would unlock. And I still managed to under imagine just how many different types of knowledge, and different layers of knowledge and language, they would be able to encode into one compact dataset.

novaRom
LLMs will be used to discover "not so obvious" connections in science. I think we will see first scientific discoveries done by LLMs very soon (in fact, such works are probably already happening). Expect major breakthroughs in physics, math, cosmology, biology, etc. published in Nature and authored by LLMs.
OOPMan
Cranking that hype level to the next stage eh?
JRagone
This is where I see the Semantic Web, semantic interoperability, and ontology alignment spaces coming to life. Who needs to standardize data when you can stick an AI in the middle of two services to do the translation for you? And then suddenly the interoperability of the metaverse doesn't seem so insurmountable. I made a post last summer that speculated on this, but I didn't think this future was so near. Or even possible really.

https://en.wikipedia.org/wiki/Semantic_Web

https://en.wikipedia.org/wiki/Semantic_interoperability

https://en.wikipedia.org/wiki/Ontology_alignment

https://metaverse-research-590.web.app/posts/communication/

simonw
I wanted to see which speakers had been on the most episodes.

I went to https://genmon.github.io/braggoscope/guests and opened the Firefox DevTools console and ran this:

    guests = Array.from(document.querySelectorAll('ul a.text-blue-500.underline')).map(el => ({
      name: el.innerText, count: parseInt(el.nextElementSibling.textContent.slice(1, -1), 10)
    }))
Then this:

    guests.sort((a, b) => a.count < b.count)
    console.log(JSON.stringify(guests, null, 2))
The top few were:

    [
      {
        "name": "Simon Schaffer",
        "count": 24
      },
      {
        "name": "Angie Hobbs",
        "count": 23
      },
      {
        "name": "Martin Palmer",
        "count": 22
      },
      {
        "name": "Steve Jones",
        "count": 21
      },
      {
        "name": "Paul Cartledge",
        "count": 20
      }
genmon
I think Angie Hobbs will take it! Looks like she is referred to with a few different names in the episode notes and I’ll need to merge them manually (will be 25 appearances)

(btw thanks for datasette. I use SQLite as an intermediary db and datasette was invaluable for exploring and refining queries.)

I_complete_me
Lots of great contributors to the show over the years. My favourite is Steve Jones for his huge intellect, humility, knowledge and listenabilty - yeah it's the right word for someone like him. I've listened to a lot of episodes (some many times over). I'll just mention The Migration of Birds as a sound listen.

However, Melvyn is an "interesting" catalyst but still great to fall asleep to. Long may it continue.

I believe there is sister program with a younger female presenter. Name escapes me...

FearNotDaniel
Ah, now that's interesting, having attended Schaffer's lectures at uni many years ago, I wasn't sure if it was my imagination that I seemed to hear his mellifluent drone coming out of the wireless more and more often these days... at last a stat to back up my hunch!
donut
This is so cool! Thanks for sharing.

Taxonomies are inherently limited -- I love this portion of the talk "Everything is Miscellaneous" about Melvil Dewey: https://www.youtube.com/watch?v=x3wOhXsjPYM&t=1206s

Surely some things fit equally well in more than one category.

Have you considered asking for best two or three categories, and placing the episodes in multiple locations? Or would that be too noisy?

genmon
I found that Dewey was borderline acceptable — more categories seemed to degrade it, though admittedly I didn’t spend much time prompt tuning. I also tried tags and these weren’t reliable (not at a consistent “scale” from episode to episode).

I suspect I’ll need a more mechanical approach, long term.

vidro3
I always assumed the podcast was current with the show but it seems like it's about 4 weeks behind.

e.g. I have Tycho Brahe on March 2 in my podcast app, rss shows it released Feb 2.

genmon
The BBC is pushing its Sounds app by lagging the podcast. Disappointing but understandable I guess given they want the opportunity to suggest other programmes.
flir
That's recent. Everybody wants a walled garden.
cheschire
No longer does Tony the Pony need to parse XHTML with regex. Who knew GPTs would be the actual solution?
haunter
Something like this for Essential Mix episodes with tracklists and genre would be mindblowing
viburnum
I love it!

"In Our Time" is the first podcast I listened to, way back in 2004, and I still listen to every episode.

arsome
Tea under Economics and coffee under Technology > Home and family management... some... interesting decisions in here.