Tootfinder

This is an important story for permacultue people. "The notches in the pattern are geographically disparate and murky, but they underscore one point: what oil was to the 20th century, food and water will be to the 21st – precious, geopolitically powerful and contested". https://www.

I am looking for historicist accounts of past ideas that situates those ideas in the lifeworld of the people of those past times: a reading of Ancient Greek philosophy that takes account of their not having a sense of dinosaurs; a reading of Goethe given no understanding of genetics at the time; why Kant would think what Kant thinks given the version of the world's geography at that time. It is hard to frame this question appropriately, but do let me know if anything comes to mind please…

Das Schlimme an dem ganzen Chatkontrolle-Überwachungs-Ding ist: wenn mal das justiziabel verfolgt würde, was heute öffentlich geschrieben wird, bräuchten wir gar keine Überwachung.

@… Hallo und guten Morgen, kann es sein, daß du den Link zu deinem Blog in deiner Profilbeschreibung falsch geschrieben hast? Gefunden habe ich ihn unter https://partlycloudy.blog/

GeoSEE: Regional Socio-Economic Estimation With a Large Language Model
Sungwon Han, Donghyun Ahn, Seungeon Lee, Minhyuk Song, Sungwon Park, Sangyoon Park, Jihee Kim, Meeyoung Cha
https://arxiv.org/abs/2406.09799 https://arxiv.org/pdf/2406.09799
arXiv:2406.09799v1 Announce Type: new
Abstract: Moving beyond traditional surveys, combining heterogeneous data sources with AI-driven inference models brings new opportunities to measure socio-economic conditions, such as poverty and population, over expansive geographic areas. The current research presents GeoSEE, a method that can estimate various socio-economic indicators using a unified pipeline powered by a large language model (LLM). Presented with a diverse set of information modules, including those pre-constructed from satellite imagery, GeoSEE selects which modules to use in estimation, for each indicator and country. This selection is guided by the LLM's prior socio-geographic knowledge, which functions similarly to the insights of a domain expert. The system then computes target indicators via in-context learning after aggregating results from selected modules in the format of natural language-based texts. Comprehensive evaluation across countries at various stages of development reveals that our method outperforms other predictive models in both unsupervised and low-shot contexts. This reliable performance under data-scarce setting in under-developed or developing countries, combined with its cost-effectiveness, underscores its potential to continuously support and monitor the progress of Sustainable Development Goals, such as poverty alleviation and equitable growth, on a global scale.

@… Hallo und guten Morgen, kann es sein, daß du den Link zu deinem Blog in deiner Profilbeschreibung falsch geschrieben hast? Gefunden habe ich ihn unter https://partlycloudy.blog/

🔊 #NowPlaying on #BBC6Music's #GuyGarveysFinestHour
Volcano Choir:
🎵 Island, IS
#VolcanoChoir
https://open.spotify.com/track/0UG45r1E8VGzjzbB5BjnTW
https://volcanochoir.bandcamp.com/track/island-is

GeoSEE: Regional Socio-Economic Estimation With a Large Language Model
Sungwon Han, Donghyun Ahn, Seungeon Lee, Minhyuk Song, Sungwon Park, Sangyoon Park, Jihee Kim, Meeyoung Cha
https://arxiv.org/abs/2406.09799 https://arxiv.org/pdf/2406.09799
arXiv:2406.09799v1 Announce Type: new
Abstract: Moving beyond traditional surveys, combining heterogeneous data sources with AI-driven inference models brings new opportunities to measure socio-economic conditions, such as poverty and population, over expansive geographic areas. The current research presents GeoSEE, a method that can estimate various socio-economic indicators using a unified pipeline powered by a large language model (LLM). Presented with a diverse set of information modules, including those pre-constructed from satellite imagery, GeoSEE selects which modules to use in estimation, for each indicator and country. This selection is guided by the LLM's prior socio-geographic knowledge, which functions similarly to the insights of a domain expert. The system then computes target indicators via in-context learning after aggregating results from selected modules in the format of natural language-based texts. Comprehensive evaluation across countries at various stages of development reveals that our method outperforms other predictive models in both unsupervised and low-shot contexts. This reliable performance under data-scarce setting in under-developed or developing countries, combined with its cost-effectiveness, underscores its potential to continuously support and monitor the progress of Sustainable Development Goals, such as poverty alleviation and equitable growth, on a global scale.

🔊 #NowPlaying on #BBC6Music's #GuyGarveysFinestHour
Françoise Hardy:
🎵 À cache-cache
#FrançoiseHardy
https://open.spotify.com/track/2dMqZm7KS4jUHuVHSQYzck
Please 🔁 BOOST to share what you like
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🔊 #NowPlaying on #BBC6Music's #GuyGarveysFinestHour
Orville Peck:
🎵 The Curse of the Blackened Eye
#OrvillePeck
https://open.spotify.com/track/5WhdOYLmpNgENGhtzyvEXc