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Author: willitcode

content/_index.md

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 title = 'Hello!'
 description = "We're Citizens for Clean Artificial Intelligence, and we're an advocacy group working to decrease the environmental impact of artificial intelligence."
-featured_image = '/img/dominik-dancs-unsplash.jpg'
+featured_image = '/img/dan-cristian-padure-unsplash-1.jpg'
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-###### Photo by Dominik Dancs on Unsplash
+###### Photo by Dan Cristian Pădureț on Unsplash
 
 We believe in the future of AI, and we're pushing to make it more eco-friendly. Currently, AI has huge environmental effects through its water consumption, greenhouse gas emissions, and the mining of silicon and other raw materials. Our work revolves around trying to reduce those emissions while still allowing for the incredible potential of AI's contributions to global environmental preservation efforts.
 

content/causes/_index.md

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 title = 'The Causes' # alt title: 'Why It's Happening'
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+###### Photo by Kevin Olson on Unsplash
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 As climate change has worsened, colonialism has been digitized into data colonialism. Data colonialism is a new, digitalized version of colonialism that has created new ways forms of colonial exploitation, which have expanded those same impacts. Data colonialism is the major cause behind this massive problem we have with AI and the environment, which explains why several corporations like Google and Microsoft have begun developing new regional hubs at profitable locations. While establishing regional hubs for various businesses doesn’t directly harm first-world countries, it takes away from and competes against the natives of second and third-world countries that rely on those same resources. Since AI and its data centers require millions of gallons of potable water per day, this creates a question in morality behind the government as to who should be prioritized: Their people or the advancement of technology. 
 
 Unfortunately, government officials in countries such as Uruguay and Chile have prioritized their economy's future rather than their nations' citizens. Corporations attempt to exploit regions that are profitable to them and have less strict environmental regulations. In other words, vulnerable regions such as Latin America, Africa, and Asia are “cash cow” locations for the IT industry. Government officials hope that introducing and developing IT infrastructure will bring prosperity to the economy. However, as Steven Gonzalez Monserrate states, “This is not a permanent industry. It is extractive like, like mines”.[^1] For context, Montevideo was the first capital to declare day zero—when a city’s water supply is nearly depleted. However, the Uruguayan government and Google disregarded this issue and decided to begin constructing a new Google regional hub in Canelones—roughly a 50-minute drive from Montevideo—which has begun in September of this year. This raises several concerns for the natives of Uruguay. How will taxes be affected now with this plan in place? How can government officials prioritize the future of IT rather than the safety of their people? As living conditions continue to worsen for natives, conflict will arise between those dealing with the aftermaths versus government officials responsible for these decisions. 

content/impacts/_index.md

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 title = 'The Impacts'
+featured_image = "/img/documerica-unsplash-2.jpg"
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+###### Photo by Documerica on Unsplash
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 As big tech companies across the world turn their focus to the power of AI, the number of resource-intensive data centers needed to support the growing industry only increases.[^1] Because data centers require so much electricity, companies constructing data centers often choose areas with cheap, instead of renewable, energy sources meaning that they rely on coal or oil burning power plants which have massive carbon footprints.[^2] New data centers can often delay the closing of these carbon-producing power plants, or even prompt the construction of new ones to provide the necessary electricity, pumping out more carbon into the atmosphere, continuing to warm the planet and provoke extreme weather events. 
 
 With the increase in focus on renewable energy and carbon emissions, one of the biggest problems with artificial intelligence slips under the radar: the water required to cool the servers in the data centers. A cycle of high consumption and low recycling creates a deficit that is usually addressed by simply pulling in more water from other sources, draining the surroundings of a much-needed resource.[^3] Data centers are often built in locations with cheap real estate and energy, leading to a worsening of the area’s water stress as water is redirected into cooling the massive data centers.[^4] 

content/problem/_index.md

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 title: The Problem
+featured_image: "/img/t-l-unsplash-1.jpg"
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+###### Photo be T L on Unsplash
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 AI is beginning to pose a massive threat to the environment by impacting global consumption of water and various raw materials. AI servers are stored in data centers that produce massive amounts of electronic waste, which contains mercury and lead, both extremely toxic and dangerous substances. 
 
 To function and maintain optimal temperature, these data centers use water to help. However, these data centers are consuming far more water than is available, especially when roughly 2 billion people in the world do not have access to clean drinking water.[^1] In 2022, Microsoft’s environmental report showed that its water consumption had jumped to 34% since 2021, using nearly 1.7 billion gallons of water.[^2] Google reported using 5.5 billion gallons of water during that same time.[^2]

content/solutions/_index.md

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 title: 'Solutions'
 description: 'Our work towards a brighter future with cleaner AI'
+featured_image: "/img/vitalii-khodzinskyi-unsplash-1.jpg"
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+###### Photo by Vitalii Khodzinskyi on Unsplash
+
 # Spiking Neural Networks
 Spiking Neural Networks, or SNNs, are a new kind of neural network. In Artificial Neural Networks, or ANNs, the current standard, the artificial "neurons" are basically active all the time. SNNs, on the other hand, behave more like a human brain—the neurons only activate when they are actively communicating with each other. This leads SNNs to be up to 280 times more efficient than ANNs.[^1] This increase in efficiency means a decrease in both greenhouse gas emissions *and* water consumption, since it's an increase in overall computing efficiency.