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GSMA:2026规模化AI影响力:中低收入国家创新者实践案例研究报告(英文版)(126页).pdf

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1、AI for Impact at ScaleCase studies from innovators in low-and middle-income countries AI for Impact at Scale2The GSMA is a global organisation unifying the mobile ecosystem to discover,develop and deliver innovation foundational to positive business environments and societal change.Our vision is to

2、unlock the full power of connectivity so that people,industry and society thrive.Representing mobile operators and organisations across the mobile ecosystem and adjacent industries,the GSMA delivers for its members across three broad pillars:Connectivity for Good,Industry Services and Solutions,and

3、Outreach.This activity includes advancing policy,tackling todays biggest societal challenges,underpinning the technology and interoperability that make mobile work,and providing the worlds largest platform to convene the mobile ecosystem at the MWC and M360 series of events.We invite you to find out

4、 more at GSMA EmergingTech ProgrammeThe GSMA EmergingTech programme accelerates impact and climate action by fostering the adoption of AI and emerging technologies in low-and middle-income countries(LMICs)by working with public,private and third sector innovators to develop scalable and sustainable

5、solutions that have inclusive and responsible AI at the core.The Emerging Tech programme works closely with the GSMA AI for Impact initiative to drive real-world,impact-focused implementation with telcos in LMICs.To get in touch with the Emerging Tech team,please email:Author:Ibrahim Sajid(GSMA Mobi

6、le for Development)Contributors:Eugnie Humeau(GSMA Mobile for Development)Zarah Udwadia(GSMA Mobile for Development)Daniele Tricarico(GSMA Mobile for Development)Acknowledgements:We would like to thank the many individuals and organisations that contributed to this research.This includes EY,World Fo

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1. **AI在低收入和中等收入国家(LMICs)的潜力**:AI在农业、医疗、气候等领域展现变革潜力,如农业AI工具可提高产量、减少虫害,医疗AI可缓解医护人员短缺。 2. **规模化与本地化**:解决方案通过本地化语言、多渠道交互(如SMS、IVR)和轻量模型适配低资源环境,如Digital Green的FarmerChat覆盖820万农民,60%用户采纳新农技。 3. **技术挑战**:依赖数字基础设施和可靠数据,但LMICs常面临数据碎片化、算力成本高(如GPT-3.5推理成本两年降280倍)及低资源语言模型优化难题。 4. **商业模式**:以非营利为主(如Digital Green获捐助),探索SaaS、MNO合作等可持续模式,目标服务低收入群体。 5. **核心数据**:LMICs 84%成人拥有手机,AI或使非洲2035年GDP增速翻倍;FarmerChat单用户农技采纳成本从$35降至$0.35。
AI如何赋能农业? AI如何改善医疗? AI如何助力气候?
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