Wes Roth (@WesRoth)
요지: Dario Amodei가 'AI 스케일링이 한계에 도달했다'는 루머를 일축하며 업계가 '놀라운 무언가의 직전(precipice)'에 있다고 주장. 체스판 비유(64칸 중 40칸)에 빗대어 향후 급격한 가속을 경고하는 발언을 함.
Amodei completely dismissed the rumor that AI scaling is hitting a wall stating that the industry is "at the precipice of something incredible". He compared the current trajectory to being on square 40 out of 64 on a chessboard, warning that an upcoming radical acceleration
Alibaba’s Aegaeon System Slashes AI Inference Costs by 82% with Smart GPU Scheduling
#AI #Alibaba #CloudComputing #AIInference #AIEfficiency #AIScaling #ChinaAI
Microsoft's venture arm just doubled funding for datacenter cooling software—not because efficiency is trendy, but because AI growth now outpaces grid capacity. When new power takes years, software that frees 40% of cooling energy becomes compute capacity. #DatacenterInfrastructure #AIScaling
https://www.implicator.ai/microsoft-backs-german-ai-firm-etalytics-as-datacenter-power-costs-bite/
"What will happen if AI scaling persists to 2030? We are releasing a report that examines what this scale-up would involve in terms of compute, investment, data, hardware, and energy. We further examine the future AI capabilities this scaling will enable, particularly in scientific R&D, which is a focus for leading AI developers. We argue that AI scaling is likely to continue through 2030, despite requiring unprecedented infrastructure, and will deliver transformative capabilities across science and beyond.
Scaling is likely to continue until 2030: On current trends, frontier AI models in 2030 will require investments of hundreds of billions of dollars, and gigawatts of electrical power. Although these are daunting challenges, they are surmountable. Such investments will be justified if AI can generate corresponding economic returns by increasing productivity. If AI lab revenues keep growing at their current rate, they would generate returns that justify hundred-billion-dollar investments in scaling.
Scaling will lead to valuable AI capabilities: By 2030, AI will be able to implement complex scientific software from natural language, assist mathematicians formalising proof sketches, and answer open-ended questions about biology protocols. All of these examples are taken from existing AI benchmarks showing progress, where simple extrapolation suggests they will be solved by 2030. We expect AI capabilities will be transformative across several scientific fields, although it may take longer than 2030 to see them deployed to full effect."
Leaps, Not Just Steps - Demis Hassabis on Lex Fridman
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China's Tencent Cuts GPU Demand by Turning to DeepSeek's Efficient AI Models
#AI #Tencent #DeepSeek #AIModels #GPUs #AIInfrastructure #ChinaAI #AIEfficiency #AIScaling #AIReasoning #ModelOptimization
This New AI Scaling Method Challenges Scaling Laws — But Can It Deliver?
#AI #AIResearch #AIScaling #MachineLearning #Inference #AIModels #GenAI #TechInnovation #AIPerformance #AIEfficiency #DeepLearning
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