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新美国安全中心:2024面向未来的前沿人工智能监管研究报告(中译版)(58页).pdf

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1、MARCH 2024Future-Proofing Frontier AI RegulationProjecting Future Compute for Frontier AI ModelsPaul ScharreAbout the AuthorPaul Scharre is the executive vice president and director of Studies at the Center for a New American Security(CNAS).He is the award-winning author of Four Battlegrounds:Power

2、in the Age of Artificial Intelligence.His first book,Army of None:Autonomous Weapons and the Future of War,won the 2019 Colby Award,was named one of Bill Gatess top five books of 2018,and was named by The Economist as one of the top five books to read to understand modern warfare.Scharre worked in t

3、he Office of the Secretary of Defense in the Bush and Obama administrations,where he played a leading role in establishing policies on unmanned and autonomous systems and emerging weapons technologies.He led the Department of Defense(DoD)working group that drafted DoD Directive 3000.09,establishing

4、the departments policies on autonomy in weapon systems.He holds a PhD in war studies from Kings College London,an MA in political economy and public policy,and a BS in physics,cum laude,from Washington University in St.Louis.Prior to working in the Office of the Secretary of Defense,Scharre served a

5、s an infantryman,sniper,and reconnaissance team leader in the Armys 3rd Ranger Battalion and completed multiple tours in Iraq and Afghanistan.He is a graduate of the Armys Airborne,Ranger,and Sniper Schools and honor graduate of the 75th Ranger Regiments Ranger Indoctrination Program.About the Techn

6、ology&National Security ProgramThe CNAS Technology&National Security program explores the policy challenges associated with emerging technologies.A key focus of the program is bringing together the technology and policy communities to better understand these challenges and together develop solutions

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本文由CNAS的Paul Scharre撰写,探讨了未来AI计算能力的发展趋势及其对政策制定的影响。主要观点如下: 1. 未来十年内,无需根本性的AI科学突破,仅通过扩大现有技术在更大模型和更多数据上的应用,AI计算能力将显著提升。 2. 预计到2020年代末或2030年代初,训练前沿AI模型的计算量将是GPT-4的1000倍,考虑算法进步,有效计算量将是GPT-4的1000万倍。 3. 尽管计算能力的增长可能受到成本和硬件限制,但私人公司如大型科技公司完全有能力资助这一增长。 4. 算法效率的提高和硬件改进将降低训练成本,使AI能力迅速普及。 5. 限制硬件获取(如出口管制)将导致某些行动者落后于前沿研究,但不会完全阻止AI能力的普及。 6. 计算能力是训练前沿AI模型的关键,但仅限制计算能力可能不足以有效监管AI。 7. 政策制定者应预见AI计算能力的发展,建立前瞻性的监管框架。
未来AI计算能力如何增长? 硬件限制如何影响AI发展? 计算能力能否成为AI监管的有效手段?
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