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"My take on generative AI has pretty rapidly gone from a tentative, “it sucks and I hate it, but after the bubble pops, there might be a few very specific use cases for it, like with programming,” to a vehemently zero-tolerance stance. It’s all garbage, burn it down."
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Editors:
Anne Mollen, Fieke Jansen, Sigrid Kannengießer, Julia Velkova
This book is open access, which means that you have free and unlimited access
Provides a much-needed depiction of AI infrastructures as inseparable from ecological, social and economic justice
Makes visible the media and communication perspective on AI infrastructures regarding questions of sustainability
Extends environmental media research to the specificity of AI imaginaries, materialities, economies, and governance
This open access book assembles cutting-edge research in media and communication exploring AI infrastructures and sustainability. It builds upon and expands perspectives on media infrastructure, addressing critical issues such as environmental impacts, economic concentration, and social justice – areas that have not been comprehensively examined under the umbrella term “sustainability” within discussions on AI. The authors explore the complex social, economic and material processes through which AI formations take shape, and offer novel perspectives on the entanglement of digital media, AI and sustainability discourses as they manifest in the shaping of futures with AI.
Anne Mollen, Fieke Jansen, Sigrid Kannengießer, Julia Velkova
This book is open access, which means that you have free and unlimited access
Provides a much-needed depiction of AI infrastructures as inseparable from ecological, social and economic justice
Makes visible the media and communication perspective on AI infrastructures regarding questions of sustainability
Extends environmental media research to the specificity of AI imaginaries, materialities, economies, and governance
This open access book assembles cutting-edge research in media and communication exploring AI infrastructures and sustainability. It builds upon and expands perspectives on media infrastructure, addressing critical issues such as environmental impacts, economic concentration, and social justice – areas that have not been comprehensively examined under the umbrella term “sustainability” within discussions on AI. The authors explore the complex social, economic and material processes through which AI formations take shape, and offer novel perspectives on the entanglement of digital media, AI and sustainability discourses as they manifest in the shaping of futures with AI.
Using 30 months of panel data on 26,811 Chinese students in grades 7-12, we study how generative AI affects homework productivity and learning. The data combine monthly closed-book exams, high-school and college entrance exams, and homework scores and completion time across nine subjects. We exploit staggered AI adoption in a difference-in-differences design. AI adoption raises homework scores by 18% and reduces completion time by 30%, but lowers monthly exam scores by 20% within six months. High-stakes entrance-exam scores fall by 18 and 24%, with the full penalty emerging only after about two years. The losses are largest in social science subjects, followed by STEM and languages, and are especially large for junior students, high-achieving students, and boys. The learning losses are concentrated among roughly 80% of AI users whose behavior is consistent with homework outsourcing, as indicated by exceptionally short homework completion time coupled with high homework scores. AI users who maintain similar homework completion time as non-AI users experience small learning losses.
Sources intéressantes mais malheureusement image IAg.
Abstract
The net climate impacts of artificial intelligence (AI) depend largely on how its applications propagate through competing energy pathways. Predominant analyses examine the relationship between datacenter energy demand, renewables optimization, and demand-side efficiencies, but insufficiently address how AI also reshapes fossil fuel supply economics. We instead model AI as a bidirectional productivity amplifier in a global computable general equilibrium model, quantifying both enabled emissions from fossil fuel productivity gains and avoided emissions from renewables productivity gains. Under parallel adoption scenarios, net annual CO₂ emissions increase by 0.47–1.8 gigatonnes (1.2–4.8% of 2024 global energy-related CO₂ emissions). Enabled emissions exceed avoided emissions whenever fossil-sector gains are nonzero; net emissions reductions require renewables gains 4–5× greater than fossil fuel gains. Absent policy steering, AI’s modeled effects increase the carbon intensity of the global economy and reinforce fossil fuel incumbency—outcomes that current analytical and governance frameworks do not fully capture.
The net climate impacts of artificial intelligence (AI) depend largely on how its applications propagate through competing energy pathways. Predominant analyses examine the relationship between datacenter energy demand, renewables optimization, and demand-side efficiencies, but insufficiently address how AI also reshapes fossil fuel supply economics. We instead model AI as a bidirectional productivity amplifier in a global computable general equilibrium model, quantifying both enabled emissions from fossil fuel productivity gains and avoided emissions from renewables productivity gains. Under parallel adoption scenarios, net annual CO₂ emissions increase by 0.47–1.8 gigatonnes (1.2–4.8% of 2024 global energy-related CO₂ emissions). Enabled emissions exceed avoided emissions whenever fossil-sector gains are nonzero; net emissions reductions require renewables gains 4–5× greater than fossil fuel gains. Absent policy steering, AI’s modeled effects increase the carbon intensity of the global economy and reinforce fossil fuel incumbency—outcomes that current analytical and governance frameworks do not fully capture.