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IEEE:2025年技术趋势预测报告(英文版)(56页).pdf

上传人: Kell****reet 编号:490805 2025-01-24 56页 4.36MB

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1、Technology Predictions 2025Ali Abedi,Mohamed Amin,Cherif Amirat,Jyotika Athavale,Mary Baker,Greg Byrd,Kyle Chard,Tom Coughlin,Izzat El Hajj,Paolo Faraboschi,Rafael Ferreira da Silva,Nicola Ferrier,Eitan Frachtenberg,Jean-Luc Gaudiot,Ada Gavrilovska Habl,Alfredo Goldman,Mike Ignatowski,Lizy K.John,Vi

2、ncent Kaabunga,Mrinal Karvir,Hironori Kasahara,Witold Kinsner,Danny Lange,Phillip A Laplante,Keqiu Li,Avi Mendelson,Cecilia Metra,Dejan Milojicic(chair),Puneet Mishra,Christine Miyachi,Khaled Mokhtar,Chengappa Munjandira,Bob Parro,Sudeep Pasricha,Nita Patel,Alexandra Posoldova,Marina Ruggieri,Tomy S

3、ebastian,Farzin Shadpour,Sohaib Sheikh,Saurabh Sinha,Vesna Sossi,Luka Strezoski,Vladimir Terzija,George Thiruvathukal,Michelle Tubb,Gordana Velikic,John Verboncoeur,Irene Pazos Viana,Jeffrey Voas,Rod Waterhouse,Stefano Zanero,Gerd Zellweger,Ying Zhang.IEEE Computer Society technology experts have un

4、veiled 22 breakthrough technologies set to redefine industries and shape the future of our world for decades to come.Technology Predictions:From Hypothetical Exercise Hypothetical Exercise to Strategic AdvantageStrategic AdvantageThe 53-member 2025 Technology Predictions Team foresees:accelerated gr

5、owth in many AI facets,requiring reskillingof workforceUS-centric reduction in interest in sustainability,due to new economic and socio-political pressures(not globally,though)ever-increasing automation in many dimensions,setting stage for additional AI opportunitiesbiotechnologys rapid development

6、under the radar(e.g.“AI-assisted drug discovery”,“AI-based medical diagnostics”)The 22 technology predictions(see next slide)were:made in 6 categories:verticals(6);applied AI(4);user interfaces(2);non-functional characteristics(4);applied computing(3);and energy-related(3)evaluated for likelihood of

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根据报告的内容,本文主要概括了2025年可能出现的22项技术突破,这些技术可能会重新定义行业并塑造未来几十年的世界。这些技术被分为六大类:垂直领域(6项)、应用AI(4项)、用户界面(2项)、非功能性特征(4项)、应用计算(3项)和能源相关(3项)。其中,最有可能成功并被广泛采用的技术是大型语言模型(LLM)部署,而最有影响力的是AI辅助药物发现。此外,文章还评估了这些技术在2025年的成功可能性、对人类的影响、成熟度、市场采用情况和商业采用的预期时间。
2025年哪些技术最有可能成功? AI在药物发现中能发挥什么作用? 核能数据中心将如何影响未来?
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