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.
A few years later, the gravitational deflection of the Himalayas on a plumb line by Airy proved less than expected, which suggested that mountains have 'roots' that extend below them, displacing more dense rock--like icebergs more or less.
I used the gravitational force of the Longmenshan range to calculate the perturbations in the elastic stress field of the Earth's crust in Sichuan province, China, to estimate the tectonic forces in the region, which caused the 2008 Wenchuan earthquake: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/201...
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.
Nice! I learned Matlab to do nonlinear optimization of GPS data to solve for fault locking depths and slip rates, and then a year or two later learned Python to do run FEMs of thermochronology data (also to get fault slip rates) on Amazon's servers in parallel. (Matlab was not really up to that task.) None of that was to impress my advisor exactly, but it worked...
For people interested in science (finance would also work) there is an infinite number of things to be done to be useful (or simply interesting) at various levels.
Doing calculations by writing functions, reading files, etc. is a great way to progress. One can take some equations from an intro physics/chem/bio book and get started. Or doing some budgeting/personal finance/investing simulations using personal banking CSVs or online stock data.
It's a good alternative to game programming in that it may be more obviously pragmatic and appeals to people (like myself) who are thoroughly uninterested in games. I imagine it's a very different style of programming as well; less interactive and more geared towards producing results rather than an experience.
For more experienced programmers looking for interesting and useful side projects (maybe to show off skills for a job search), I think there is a lot of opportunity to work with scientists to help them with their research. It's actually pretty easy to reach out to scientists by going to public talks and seeing what they do, then chatting them up and seeing how to make yourself useful. The hard part is of course sticking with it...
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.