At least personally I scanned the article for it and only found the picture at the top, which I was then frustrated to learn that's just a lab photo, and I came here wondering where the actual image is of it in the field so I found OPs comment helpful to indicate that the suggestion there would be a beautiful picture of glowing canopy somewhere is basically a result of editorializing.

The dataset includes US coverage but it's not filtered the same way and FAR more noisy, so I appreciate efforts like this. We haven't got there yet but if you were looking for something deterministic and automatable the Canadian gov's process is potentially worth learning about.

They also produce perimeter estimates based on the hotspots which we can extract and put into a physics-based fire growth model like Prometheus or FARSITE to estimate future fire behaviour based on forecasted weather. This gives very actionable and deterministic estimates of future fire behaviour. We also have worked on a risk model that determines the likelihood of that future fire growth interacting with various assets on the landscape (urban interface areas, power lines, fuel pipelines, forest inventory, etc) and calls out high risk areas. One thing we've been wondering if where LLMs fit into any of this (if at all) so appreciate seeing what others are doing.

1. Department of War broadly uses Anthropic for general purposes

2. Minority interests in the Department of War would like to apply it to mass surveillance and/or autonomous weapons

3. Anthropic disagrees and it escalates

4. Anthropic goes public criticizing the whole Department of War

5. Trump sees a political reason to make an example of Anthropic and bans them

6. The entirety of the Department of War now has no AI for anything

7. Department of War makes agreement with another organization

If there was only a minority interest at the department of war to develop mass surveillance / autonomous weapons or it was seen as an unproven use case / unknown value compared to the more proven value from the rest of their organizational use of it, it would make sense that they'd be 1) in practice willing to agree to compromise on this, 2) now unable to do so with Anthropic in specific because of the political kerfuffle.

I imagine they'd rather not compromise, but if none of the AI companies are going to offer them it then there's only so much you can do as a short term strategy.

This article gave an LLM a bunch of health metrics and then asked it to reduce it to a single score, didn't tell us any of the actual metric values, and then compared that to a doctor's opinion. Why anyone would expect these to align is beyond my understanding.

The most obvious thing that jumps out to me is that I've noticed doctors generally, for better or worse, consider "health" much differently than the fitness community does. It's different toolsets and different goals. If this person's VO2 max estimate was under 30, that's objectively a poor VO2 max by most standards, and an LLM trained on the internet's entire repository of fitness discussion is likely going to give this person a bad score in terms of cardio fitness. But a doctor who sees a person come in who isn't complaining about anything in particular, moves around fine, doesn't have risk factors like age or family history, and has good metrics on a blood test is probably going to say they're in fine cardio health regardless of what their wearable says.

I'd go so far to say this is probably the case for most people. Your average person is in really poor fitness-shape but just fine health-shape.

If you're truly creating such unique and valuable software that it is to be compared to the world's engineering megaprojects in its challenge then perhaps it is beyond being beholden to a budget. Who am I to say?

But 99.9% of this industry isn't doing that and should probably be able to estimate their work.

Estimation is a real problem in a lot of industries, including ours, and I think that's probably common ground here -- I suppose my differing position is that I think the solution is to get better at it, not to refuse to do it.

I've been on projects where I've seen the budget explode and projects where I've seen the budget kept tight and on track. The latter is very hard and requires effort from ALL sides to work, but it's almost always achievable.

I actually empathize a little bit more with megaprojects because generally the larger the budget the harder it will be to keep on track in my experience. Most estimates we're asked to give in our day jobs are not even multi-million dollar estimates.

Also I'm using budget and estimate interchangeably but these are of course different things -- that's one of my nitpicks is that we often treat these as the same thing when we talk about estimating being hard. A lot of individual estimates can be very wrong without affecting the ultimate budget.

I completely agree. That's why I chose that example: They're also awful at it, especially these days in North America in particular. But any contractor that tried to put in a bid claiming "it'll be done when it's done and cost what it costs" would not be considered professionally competent enough to award a multi-million dollar budget.

Estimating in software is very hard, but that's not a good reason to give up on getting better at it

But I agree, I'd rather start solving deficiencies at the diet level than the supplement level and haven't integrated collagen personally so far.

TBH I suspect marketing plays a big role. "Collagen = good, therefore just buy it and eat it" makes logical sense if you don't actually do any research first.

e.g. https://thefeed.com/products/pillar-performance-collagen-1?v...

This is a good point. It'd almost certainly be called something like 'payrate sensitivity factor'