This is so cool. I used to work on urban heat island analysis and now work in natural catastrophe modelling, and in both cases knowing the average heights/volumes of buildings is a very handy thing to have but is surprisingly difficult information to retrieve. Even a coarse estimate available at annual resolution has some really awesome use cases, very excited to see this.
Readit Newsthicknavyrain commented on Skyfall-GS – Synthesizing Immersive 3D Urban Scenes from Satellite Imagery skyfall-gs.jayinnn.dev/... · Posted by u/ChrisArchitect
thicknavyrain commented on CauseNet: Towards a causality graph extracted from the web causenet.org/... · Posted by u/geetee
I know it's a reductive take to point to a single mistake and act like the whole project might be a bit futile (maybe it's a rarity) but this example in their sample is really quite awful if the idea is to give AI better epistemics:
{
"causal_relation": {
"cause": {
"concept": "vaccines"
},
"effect": {
"concept": "autism"
}
}
},
thicknavyrain commented on HN Slop: AI startup ideas generated from Hacker News josh.ing/hn-slop... · Posted by u/coloneltcb
That's super cool, I love the SciShow videos.
I think you're right about the editorial thinking + what do people find interesting parts. But that doesn't have to be solved by directly by AI, it's easy enough to sidestep the problem and provide a nice interface for the human-in-the-loop part. I'd imagine that would save you a ton of time by having a nice starting point depending on how much you have to rewrite for tone.
That's true, it could just turn the writer's role into more of an editorial role. The main time-saving I have so far is being able to upload papers and get it to fact check for me. The editorial guidelines at SciShow are stricter than any academic journal I've published in: any non-trivial statement has to be supported by a direct, findable quote in (most-of-the-time) peer-reviewed scientific literature. I once had to find a citation for the idea that heat + fuel + oxygen generates a fire! (for this video: https://www.youtube.com/watch?v=BEcaE0e0CZg)
LLMs make that much easier. As I collect primary sources during my drafting/writing phrase, I can type up any non-trivial claims I'm making in my script in a separate document, share that with the LLM and say "Quoting directly from the set of attached PDFs, identifying which document, and on which page the quote comes from, find content which directly supports each of these assertions" and it generally goes a great job. At any rate, I have to check each of those quotes for accuracy but the help in _finding_ those quotes in order to pass a stringent fact checking procedure is a huge help if I didn't scribble down the supporting quotes during my research phase. This is also, by the way, stricter than the fact checking process for most non-fiction publishing.
thicknavyrain commented on HN Slop: AI startup ideas generated from Hacker News josh.ing/hn-slop... · Posted by u/coloneltcb
>SciDigest: An AI-powered platform that transforms complex scientific research into engaging, digestible content for the general public, making scientific knowledge accessible and actionable.
This would actually be great. So many researchers have a marketing problem with explaining and getting people excited for their work.
I'm a popular science writer with eight year's experience doing exactly this (SciShow, Crash Course, Veritasium and recent winner of the Wellcome Collection Non Fiction Awards) without AI. Done right, the right coverage of even a pre-print reached hundreds of thousands/millions of people. But I've experimented with every SOTA model since 2022 with the most detailed and specific prompting I can think of (including multiple examples of transcripts of work already in the public domain) to see if it can replicate good quality science communication.
The content is usually reasonably strong but the tone is always off and it never quite understands what it is a reader/viewer needs to really get to grips with the topic if they don't already have a prior foundational understanding (though I notice this about a lot of other media outlets with professional science communicators too). It also has poor editorial thinking around what bits are most likely to be interesting and cohesive when considered as part of the whole piece.
But I'm still reasonably convinced as AI improves it ought to be able to replace me with the right workflow/context/prompting. I think there will always be a demand for my (and many other writers') talents as they are so it doesn't really bother me, but it'd be great to extend the work to all the many scientific discoveries that don't get the same attention. If anyone is serious about developing something like this, I'd be interested in partnering with them as someone with domain expertise on science communication and familiar with prompt engineering (email in bio).
thicknavyrain commented on Satellites Spotting Depth tech.marksblogg.com/depth... · Posted by u/marklit
There must be some cool application for this but I can't think of what. I guess computing shadows and things like that but we often already have 3d buildings (though maybe not for rural areas like this does).
Urban heat island analysis. The physical volumes of buildings is an essential input parameter into calculating the estimated impact of the built environment and possible interventions (e.g. greening, reducing traffic) against local temperature rises. It is notoriously difficult to obtain that data at fine spatial resolution. This would be a game changer. True to a lesser degree for air pollution modelling as well, building volume is a significant input for land use regression models.
I have an Erdös number of 2[] via a paper, co-authored with Ron Graham, on the topic of the maths of juggling.
AMA.
[
] I also have an Erdös number of the second type of 3, but that doesn't involve Ron Graham or juggling.Just want to say, I recognised your name immediately when I saw it. Saw a talk you gave at the Institute of Education in Bloomsbury back in 2010 when I was a teenager and it is still, to this day, one of the best popular mathematics talks I've ever witnessed (obviously helped by the immense juggling talent).
I've gone on to do a PhD in Physics and write lots of popular science for some big YouTube channels (SciShow, Veritasium) and among some of the more long term influences on my career I definitely count your talk as one of them!
> Why are people surprised
I see this type of question a lot when something is considered common knowledge in whatever online bubble someone is part of.
But the only way to go from “everybody knows” to documented fact is through investigative journalism and reporting. The point of these stories is not to say “wow we are so surprised”, the point is to say “this company is in fact lying and we have the documentation to prove it.”
Well said, and not to mention the importance of common knowledge as a driving impetus for enacting a change. "Everyone knows Hollywood is full of abuse" was true for decades but when the Weinstein allegations finally came out into the open, some (if not enough) action finally started happening against it. Saying the obvious thing loudly and openly is a coordinating mechanism.
> The two most impactful thinkers/writers in my life
I am not familiar with the author, but after reading this article I am interested in learning more him and his other thoughts. Can you provide a good starting place for reading material? Specifically something that you feel affected your life.
Land is a Big Deal is a great place to start (and his three articles summarising Georgism for SSC, also on his substack named after Henry George's book "Progress and Poverty", which contain much of the same content).
thicknavyrain commented on Ask HN: Books you read in 2023 and recommend for 2024? · Posted by u/vanschelven
The Identity Trap by Yascha Mounk, a mostly even handed, sensible and necessary read for right now.