cossatot

Well, the ones on the summits of Hood and Rainier are just so readily accessible that it'd be a shame not to dip your toesies in.

Forecasting earthquakes via ML should be possible but is very strongly limited by data. We have ~50 years of reasonably good seismological catalogs for most of the world. The seismic cycle (the sequence of major earthquake, reloading, major earthquake on a single section of fault) is generally thousands of years except at the fastest-slipping faults. There are very few sections of faults where we have seismological observations of multiple events, and for >90% of faults, we don't even know when the last earthquake was. There are geologic methods to help with this, but they are labor intensive and often yield error bars of hundreds to thousands of years, because the earthquakes don't produce radiocarbon signatures directly; the geologists use e.g. charcoal older and younger layers as available to bracket the timing, and many faults do not have suitable geologic sites to preserve the earthquake deformation and bracket the timing.

I do think it's possible that thorough exploration of the data that do exist can yield broader patterns that apply to many regions, but earthquake behavior has a lot of complexities and different fault systems may behave differently.

A lot of the hope is for coupling physical simulators to ML and the existing datasets to better understand the physics and then work from there, but this is typically cutting-edge HPC work, which limits the pace of research and the number of researchers.

>The system is what geologists call a fault-controlled non-volcanic geothermal feature.

These are quite common. Hot Springs National Park in Arkansas is a notable example. Most of the hot springs in Idaho are on fault lines, as are the hot springs along Highway 395 on the east side of the Sierras (though there is Quaternary volcanism in the region, it's not the source of heat), and through much of the rest of the Intermountain West.

This site seems to be unique for both the volume and the temperature of water, though. I'd be curious to know how far away the water originated, and how deep it goes before being heated. The crust in the area is not known to be particularly hot, as far as I know, unlike the Basin and Range of the western US where tectonic stretching and thinning of the crust has brought the hot mantle much closer to the surface and increased the geothermal gradient.

We're a century into it at least, even in nominal dollar terms, starting with Rockefeller as the first billionaire.

I don't know whether John Arnold is spread too thin or not, but he's certainly top caliber and does a lot to measure progress before/during investment in various causes (including education). He also seems to be more agnostic on what the most appropriate solution may be at the beginning of the process.

Maybe, just maybe, this is of obvious utility to the many people who have needs that are not yours?

I very regularly need to interact with my work through a python interpreter. My work is scientific programming. So the variables might be arrays with millions of elements. In order to debug, optimize, verify, or improve in any way my work, I cannot rely on any other methods than interacting with the code as it's being run, or while everything is still in memory. So if I want to really leverage LLMs, especially to allow them to work semi-autonomously, they must be able to do the same.

I'm not going to dump tens of GB of stuff to a log file or send it around via pipes or whatever. Why is there a nan in an array that is the product of many earlier steps in a code that took an hour to run? Why are certain data in a 200k-variable system of equations much harder to fit than others, and which equations are in tension with each other to prevent better convergence?

Are interpreters and pdb not great, previously-existing tools for this kind of work? Does a new tool that lets LLMs/agents use them actually represent some sort of hack job because better solutions have existed for years?

I use both gvim on linux and macvim on mac for a lot of things--not 'real' coding, typically, but opening and editing scripts and config files, writing in markdown, etc; I'm usually opening these from dolphin or finder. In the terminal, working on real code bases and not scripts, I use neovim. My configs for these have diverged a bit over the years but since the use cases are different, it doesn't bother me.

Thanks. I've got an OpenAI subscription and tried this in the past, and got a handful of results, but nothing comprehensive. Perhaps it is better now, or I could change the way I ask.

And wine glass stains are the only way to know your paper has been graded.

It does on my fedora KDE machine at least. It is probably in the top 5 usability improvements of linux over mac in my world.

Do you think desalinating 10% of the world's ocean water is feasible? What are the energy resources necessary to do that?

There were no alphabets in the Americas before European contact. Mayan had written mathematics and hieroglyphics, and some Quechuan speaking peoples had string that had symbolic knots that had some mathematical representation (I don't know if it allowed arithmetic or was just record keeping).

Sequoia developed the Cherokee syllabary (where symbols represent syllables instead of vowels/consonants) in the 1800s after seeing white men reading, and figuring out what they were doing (he spoke little English and could not read it). This is the first real written indigenous language in the Americas.

The Skeena characters shown here are obviously derived from European characters, as was the Cherokee syllabary. I think most written forms of native languages in the Americas are similar.

The Cree have a script which is far from European characters but was nonetheless developed for the Cree by a missionary in the 1800s. The Inuit have modified it for their language.

I don't know much about indigenous languages in the rest of the world.

They are so frequently intertwined