thicknavyrain

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.

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"
            }
        }
    },

... seriously? Then again, they do say these are just "causal beliefs" expressed on the internet, but seems like some stronger filtering of which beliefs to adopt ought to be exercised for an downstream usecase.

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).

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.

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!

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.

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).

The Identity Trap by Yascha Mounk, a mostly even handed, sensible and necessary read for right now.

It's remarkable how many problems seemingly come back to Land Value Taxation.

Everyone always misses the corollary to this "groupthink is why we have dark matter" theory which is that academics are very heavily incentivized to try and overturn conventional dark matter because it would be an absolute career-maker to have a credible alternative (and get credit for upturning a widely accepted theory). And in fact lots of people have tried various MOND theories etc but the reason Lambda-CDM persists for so long is because it still fits the data the best.

Certainly useful for thinking, but its lack of falsifiability, predictive power or validation is indeed why there's so much controversy around it and why it remains a hypothesised model rather than widely accepted scientific fact. The best you can do is test the assumptions of the model itself, or its constituent terms.

I find this kind of sentiment a little vacuous. Charitably, we might say "Much of the non-human life on Earth will be just fine without humans" but the kinds of ecological disaster that would wipe us would would take a significant chunk of other life along with it. Beyond that, what value is a floating chunk of rock except for the very fact it sustains conscious life, the only thing capable of valuing it? Humans, and other forms of life, are pretty much entirely what makes Earth worth caring about at all.