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IEEE:2026年技术趋势预测报告(中译版)(77页).pdf

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1、TECHNOLOGYPREDICTIONS2026Surprises in 2026 Predictions The speed,depth,and breadth of AI adoption over the course of last year biased the prediction team to being more near-term focused Literally all predictions were AI-related,-influenced,or-driven;six were directly related(slide 7)Many more predic

2、tions have higher impact on humanity than likelihood of success,requiring more funding(slide 16)This forced the team to introduce riskreward categories from remaining honorable mentions(slide 11)Aggressive AI adoption enables or fosters:new megatrends(health,energy,space,and robotics,slide 19)new ve

3、rticals(future of coding/work),and new types of computing(in-memory,rack-scale,slide 7)It seems like AI-driven technologies are advancing at a much higher rate than any other previous revolutions(digital transformation,industrial revolution)Being a group of acknowledged technology experts,we have a

4、bias toward believing in the“success”of technology.Therefore,a dedicated perspective on risks and rewards is introduced.The optimism about relatively immature technologies(e.g.,Social AI,AI Personalities)is also reflected in the anticipated adoption in 202622026 TECHNOLOGY PREDICTIONSAli Abedi,Aleja

5、ndro Acero,Sheikh Iqbal Ahamed,Metin Akay,Karen Alexander,Mohamed Amin,Cherif Amirat,Jyotika Athavale,Mary Baker,Marc Beebe,Elisa Bertino,Jose Roberto Boisson de Marca,Greg Byrd,Tracy Camp,Lesleigh Campanale,Solimar Crdenas,Jose Ignacio Castillo,Sri Chandra,Carl K.Chang,Rong N.Chang,Kyle Chard,Demin

6、g Chen,Ernestina Cianca,Tom Coughlin,Ernesto Damiani,Sanja Damjanovic,Marko Delimar,Ksenija Draskovic,Christof Ebert,Mohamed Essaaidi,Paolo Faraboschi,Rafael Ferreira da Silva,Nicola Ferrier,Eitan Frachtenberg,Jean-Luc Gaudiot,Ada Gavrilovska Habl,Glenn Ge,Alfredo Goldman,Izzat El Hajj,Sumi Helal,Sa

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1. **AI主导技术预测**:2026年26项技术预测中,所有预测均与AI相关或受AI驱动,其中6项直接相关(如AI生成内容、具身物理AI)。 2. **高影响与高风险领域**:未来医学对人类影响最大(A级),但部分技术(如融合能源、量子计算)成功概率低、回报高,需政府/企业投资。 3. **核心瓶颈**:采用瓶颈集中在“信任+能源”,包括身份验证、AI管道保障及数据中心能耗管理(如Nvidia芯片单 rack 功耗达180-1000千瓦)。 4. **劳动力转型**:AI代理将成为知识工作者“团队成员”,竞争优势从“人力规模”转向“智能杠杆”,需重新定义技能(如验证素养、人机协作)。 5. **长期机遇**:空间技术(卫星直连终端)、新型处理器(性能提升1000倍)及碳负计算等高回报领域值得关注。
AI如何重塑工作? 2026科技风口? AI风险与收益?
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