#waterfootprint — Public Fediverse posts

SPRUCE 0.9 is out

Here's what's new:

💧 Water is now a first-class metric. SPRUCE can now estimate water consumption — factoring in regional hydric stress.

⚡ Operational emissions got more accurate, with power supply and transmission overheads now included.

📊 A Python-based report generator makes it easier to surface insights from your data.

🚀 and many more improvements under the hood

https://opensourcegreenops.cloud/

#GreenOps #CloudSustainability #OpenSource #WaterFootprint

‘Based on a previous reality’

Artificial intelligence tools are becoming a part of everyday life as folks and companies have started using them…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Artificialintelligence #AI #AlexdeVries-Gao #ArtificialIntelligence #Bottledwater #cleanerenergysystems #DataCenters #electricitydemand #JonathanKoomey #Technology #waterfootprint
https://www.newsbeep.com/us/415245/

‘Based on a previous reality’

Artificial intelligence tools are becoming a part of everyday life as folks and companies have started using them…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Artificialintelligence #AI #AlexdeVries-Gao #ArtificialIntelligence #Bottledwater #cleanerenergysystems #DataCenters #electricitydemand #JonathanKoomey #Technology #waterfootprint
https://www.newsbeep.com/us/415245/

Me he encontrado con este letrero sobre el #consumohidrico. Me enoja el tono polarizante en lugar de hacer #conciencia. Nos toca ser responsables con nuestras #waterfootprint y #CO2footprint no solamente tirar mierda. Ademas las imágenes #AI de #ghiblistyle atacab muchas otras éticas

AI’s excessive water consumption threatens to drown out its environmental contributions
#Technology #AI #ArtificialIntelligence #Water #Google #Taiwan #Semiconductors #RareMinerals #AIWaterDilemma #EnvironmentalImpact #WaterFootprint
https://the-14.com/ais-excessive-water-consumption-threatens-to-drown-out-its-environmental-contributions/

.> ... large-scale AI models are indeed big water consumers. For example, training GPT‑3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater (enough to produce 370 BMW cars or 320 Tesla electric vehicles), and the water consumption would have been tripled if training were done in Microsoft’s data centers in Asia. These numbers do not include the off-site water footprint associated with electricity generation.

.> ChatGPT needs a 500-ml bottle of water for a short conversation of roughly 20 to 50 questions and answers, depending on when and where the model is deployed. Given ChatGPT’s huge user base, the total water footprint for inference can be enormous.

.> ... if we only consider carbon footprint reduction (say, by scheduling more AI training around noon), we’ll likely end up with higher water consumption, which is not truly sustainable for AI.

.> ... the vast majority of data centers still use potable water and cooling towers. For example, even tech giants such as Google heavily rely on cooling towers and consume billions of liters of potable water each year. Such huge water consumption has produced a stress on the local water infrastructure; Google’s data center used more than a quarter of all the water in The Dalles, Ore.

.> ... some AI conferences have requested that authors declare their AI models’ carbon footprint in their papers; we believe that with transparency and awareness, authors can also declare their AI models’ water footprint as part of the environmental impact.

.> ... large-scale AI models are indeed big water consumers. For example, training GPT‑3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater (enough to produce 370 BMW cars or 320 Tesla electric vehicles), and the water consumption would have been tripled if training were done in Microsoft’s data centers in Asia. These numbers do not include the off-site water footprint associated with electricity generation.

.> ChatGPT needs a 500-ml bottle of water for a short conversation of roughly 20 to 50 questions and answers, depending on when and where the model is deployed. Given ChatGPT’s huge user base, the total water footprint for inference can be enormous.

.> ... if we only consider carbon footprint reduction (say, by scheduling more AI training around noon), we’ll likely end up with higher water consumption, which is not truly sustainable for AI.

.> ... the vast majority of data centers still use potable water and cooling towers. For example, even tech giants such as Google heavily rely on cooling towers and consume billions of liters of potable water each year. Such huge water consumption has produced a stress on the local water infrastructure; Google’s data center used more than a quarter of all the water in The Dalles, Ore.

.> ... some AI conferences have requested that authors declare their AI models’ carbon footprint in their papers; we believe that with transparency and awareness, authors can also declare their AI models’ water footprint as part of the environmental impact.

.> ... large-scale AI models are indeed big water consumers. For example, training GPT‑3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater (enough to produce 370 BMW cars or 320 Tesla electric vehicles), and the water consumption would have been tripled if training were done in Microsoft’s data centers in Asia. These numbers do not include the off-site water footprint associated with electricity generation.

.> ChatGPT needs a 500-ml bottle of water for a short conversation of roughly 20 to 50 questions and answers, depending on when and where the model is deployed. Given ChatGPT’s huge user base, the total water footprint for inference can be enormous.

.> ... if we only consider carbon footprint reduction (say, by scheduling more AI training around noon), we’ll likely end up with higher water consumption, which is not truly sustainable for AI.

.> ... the vast majority of data centers still use potable water and cooling towers. For example, even tech giants such as Google heavily rely on cooling towers and consume billions of liters of potable water each year. Such huge water consumption has produced a stress on the local water infrastructure; Google’s data center used more than a quarter of all the water in The Dalles, Ore.

.> ... some AI conferences have requested that authors declare their AI models’ carbon footprint in their papers; we believe that with transparency and awareness, authors can also declare their AI models’ water footprint as part of the environmental impact.

.> ... large-scale AI models are indeed big water consumers. For example, training GPT‑3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater (enough to produce 370 BMW cars or 320 Tesla electric vehicles), and the water consumption would have been tripled if training were done in Microsoft’s data centers in Asia. These numbers do not include the off-site water footprint associated with electricity generation.

.> ChatGPT needs a 500-ml bottle of water for a short conversation of roughly 20 to 50 questions and answers, depending on when and where the model is deployed. Given ChatGPT’s huge user base, the total water footprint for inference can be enormous.

.> ... if we only consider carbon footprint reduction (say, by scheduling more AI training around noon), we’ll likely end up with higher water consumption, which is not truly sustainable for AI.

.> ... the vast majority of data centers still use potable water and cooling towers. For example, even tech giants such as Google heavily rely on cooling towers and consume billions of liters of potable water each year. Such huge water consumption has produced a stress on the local water infrastructure; Google’s data center used more than a quarter of all the water in The Dalles, Ore.

.> ... some AI conferences have requested that authors declare their AI models’ carbon footprint in their papers; we believe that with transparency and awareness, authors can also declare their AI models’ water footprint as part of the environmental impact.

.> ... large-scale AI models are indeed big water consumers. For example, training GPT‑3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater (enough to produce 370 BMW cars or 320 Tesla electric vehicles), and the water consumption would have been tripled if training were done in Microsoft’s data centers in Asia. These numbers do not include the off-site water footprint associated with electricity generation.

.> ChatGPT needs a 500-ml bottle of water for a short conversation of roughly 20 to 50 questions and answers, depending on when and where the model is deployed. Given ChatGPT’s huge user base, the total water footprint for inference can be enormous.

.> ... if we only consider carbon footprint reduction (say, by scheduling more AI training around noon), we’ll likely end up with higher water consumption, which is not truly sustainable for AI.

.> ... the vast majority of data centers still use potable water and cooling towers. For example, even tech giants such as Google heavily rely on cooling towers and consume billions of liters of potable water each year. Such huge water consumption has produced a stress on the local water infrastructure; Google’s data center used more than a quarter of all the water in The Dalles, Ore.

.> ... some AI conferences have requested that authors declare their AI models’ carbon footprint in their papers; we believe that with transparency and awareness, authors can also declare their AI models’ water footprint as part of the environmental impact.

@SkepticalEye Reverse osmosis produces several gallons of wastewater for every gallon of "pure" water it produces. It's incredibly wasteful.

The bottled water companies and those who sell RO sysyems know this and don't care. They end up getting their money.

#ReverseOsmosis #WaterPurifier #WaterFootprint #WasteWater #Conservation

“the Colorado River system is in a state of collapse. It is a collapse hastened by climate change but also a crisis of management.”

https://talkingpointsmemo.com/news/a-water-war-is-brewing-over-the-dwindling-colorado-river

#climatechange #WaterWars #WesternUS #JohnWesleyPowell #agriculture #aquifer #drainagebasin #watermanagement #coloradoriver #WaterFootprint

“the Colorado River system is in a state of collapse. It is a collapse hastened by climate change but also a crisis of management.”

https://talkingpointsmemo.com/news/a-water-war-is-brewing-over-the-dwindling-colorado-river

#climatechange #WaterWars #WesternUS #JohnWesleyPowell #agriculture #aquifer #drainagebasin #watermanagement #coloradoriver #WaterFootprint

“the Colorado River system is in a state of collapse. It is a collapse hastened by climate change but also a crisis of management.”

https://talkingpointsmemo.com/news/a-water-war-is-brewing-over-the-dwindling-colorado-river

#climatechange #WaterWars #WesternUS #JohnWesleyPowell #agriculture #aquifer #drainagebasin #watermanagement #coloradoriver #WaterFootprint

“the Colorado River system is in a state of collapse. It is a collapse hastened by climate change but also a crisis of management.”

https://talkingpointsmemo.com/news/a-water-war-is-brewing-over-the-dwindling-colorado-river

#climatechange #WaterWars #WesternUS #JohnWesleyPowell #agriculture #aquifer #drainagebasin #watermanagement #coloradoriver #WaterFootprint

“the Colorado River system is in a state of collapse. It is a collapse hastened by climate change but also a crisis of management.”

https://talkingpointsmemo.com/news/a-water-war-is-brewing-over-the-dwindling-colorado-river

#climatechange #WaterWars #WesternUS #JohnWesleyPowell #agriculture #aquifer #drainagebasin #watermanagement #coloradoriver #WaterFootprint