[thewopr]

Having been in technology, antarctic research, and remote monitoring of environmental systems down there, my take: don't read into this this too much.

This is mostly a PR piece, probably pushed by the university or non-profit researchers involved. They are trying to use some sort of partnership with NVIDIA (as loose as that partnership might be) to draw attention and show they are having "Broader Impacts" for their impact statement.

Most eco research is based on historical comparisons of months/years/decades of data So the use of real-time/streaming data down there is pretty limited. You can just as easily shove the data into storage and have a researcher pick it up next time they go down (often *much* easier as you don't have to worry about powering comms systems).

Climate/weather data may be different, if only because some of the data might go into current/real-time weather models. But even there, it's probably a stretch (I know of very little work being done with anything near real-time as far as data goes down there).

[earthscienceman]

Hey @thewopr.

I'm actually working on real-time measurement transmission of the climate parameters, though at the other pole in the Arctic. I always imagined people working on such things were here on HN.

This article is absolutely a PR/puff piece. This type of transmission isn't novel, unique, or "AIoT"... using that acronym for this is beyond hilarious, I'd challenge anyone on the project to describe how AI is in any way relevant to the project beyond NVIDIA PR blurbs. I've looked through the research and the only thing I've found is a nebulous "we will analyze the data with AI at some point". But! People have been doing these sorts of measurements and transmitting them from these sorts of places for... ever... basically. Also entirely based on open source. The autonomous station I(we)'ve designed uses a custom Linux box/SoC for low power data processing and amalgamation and the results are transmitted in realtime back here to the USA to be ingested by models, without a single chip designed by NVIDIA. From places much more remote than a few kms from a base in Antarctica, we had a station running in the Arctic ice pack during winter. Not to add more criticism, but I also always love how these puff pieces don't actually link to the research of the people who put in the work on the project[1].

As an aside, AI could actually very relevant/important for these types of measurements. One idea I'm working to spin up:

Vertical profile retrievals of important atmospheric measurements (cloud properties, and more) are extremely power intensive and nearly impossible to do autonomously via lidar/radar. However, there are many ways one could imagine designing a low power implementation of those retrievals using a combination of different sensors and a cleverly trained algorithm to get at the parameters of interest.

Anyway, link/tell me about some of your remote monitoring. I'm pretty disconnected from the south side of these things.

[1] https://www.uow.edu.au/media/2023/world-first-mosscam-and-sm...

[thewopr]

My experiences were with the McMurdo Dry Valleys LTER program [1]. They have (things may have changed) a number of sites sending telemetry back to the states via the Iridium network. Nothing too fancy. It worked. Biggest challenge really was that iridium was slow and relatively high power (and somewhat flaky deep in the valley where we were). I also had some involvement with the NTL LTER program[4], but that type of work has even easier telemetry constraints (these days, just use a cell modem).

I totally agree with you on the "using a combination of different sensors and a cleverly trained algorithm to get at the parameters of interest". This is something not too far from, in a way, how many sensors work already. They are *proxies* of the actual thing being measured. From my world, the s-can DOC sensor was always a good example, using in-situ spectroscopy to estimate DOC concentration.

Crux of the challenge is "what is the parameter of interest" and "can you come up with a way to estimate it with something easily measured?

Because this is HN, I'll say there is another interesting route possible. If you can change the economics of a situation and decrease the cost of a basic sensor, then you can often increase the volume of applicable uses. I was tangentially involved with the development of the miniDOT [3], which ended up being one of the first "inexpensive" (as in less than $5k) dissolved oxygen sensor. It really changed how people used them and increased the amount of DO sensing by probably an order of magnitude.

[1]: https://mcm.lternet.edu/ [2]: https://www.s-can.at/en/product/carbolyser-v3/ [3]: https://www.pme.com/new-products/minidot-usb-oxygen-logger [4]: https://lter.limnology.wisc.edu/