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新美国安全中心:2024面向未来的前沿AI监管:前沿AI模型未来算力预测报告(英文版)(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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本文主要探讨了未来AI模型计算能力和训练成本的发展趋势。主要观点如下: 1. 训练前沿AI模型所需的计算能力在未来十年内可能增长1000倍,甚至达到GPT-4的100万倍。 2. 计算能力的增长主要受硬件改进和计算投入增加的推动,同时算法效率的提高也起到了关键作用。 3. 训练成本的增长速度可能放缓,预计在私人公司可承受的数十亿美元范围内达到极限。 4. 硬件限制可能导致某些国家在训练AI模型方面落后于其他国家,但算法效率的提高将有助于缩小这一差距。 5. 计算能力可能成为未来AI模型监管的一个有效途径,但需要与模型本身的规定相结合。 6. 政策制定者应考虑AI进步的速度,提前建立适应未来发展的监管框架。
未来AI计算能力如何增长? 硬件限制如何影响AI发展? 算法进步如何影响AI普及?
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