When the same function produces different data sets based on device age, how do you maintain consistency across decades?
COMPUTING
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New TPU Generation Shows 2-3x Performance Improvement Over Ironwood
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Huge improvements over Ironwood TPU’s (2-3x), can’t wait to see Gemini in action with these : )
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Google Launches 8th Generation TPU AI Hardware
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TPU’s are a core part of the Google secret sauce, excited to see our 8th generation TPU see the light of day : )
— Logan Kilpatrick (@OfficialLoganK) 22 avril 2026
pic.twitter.com/R4fiT5YvFiTPU’s are a core part of the Google secret sauce, excited to see our 8th generation TPU see the light of day : )
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Edge Platforms Must Support Future Requirements on Legacy Hardware
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The edge platform cannot be purpose-built for today's rules alone. It must accommodate new requirements running on hardware installed years earlier.
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DOE Under Secretary Leads AI Science Discovery Conference at Stanford
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Join U.S. Department of @ENERGY Under Secretary for Science @dariogila
, together with leading scientists, engineers, and researchers, at the upcoming @StanfordHAI and Stanford Data Science conference on AI+Science: Accelerating Discovery. Register here: https://
hai.stanford.edu/events/ai-scie
nce-accelerating-discovery
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Edge Systems Navigate Firmware Diversity in IoT Hardware
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Battery edge systems need context for old and new firmware. Same function on 10-year-old hardware vs brand new device produces different data sets. The edge processes fast locally while summarizing for cloud. Partner content with @IOTechSystems. #iotechsys_iiot pic.twitter.com/XH4zrm3aus
— Lucian Fogoros (@fogoros) 22 avril 2026Battery edge systems need context for old and new firmware. Same function on 10-year-old hardware vs brand new device produces different data sets. The edge processes fast locally while summarizing for cloud. Partner content with @IOTechSystems
. #iotechsys_iiot -
Trillion-fold compute growth: AI training scales toward 2028
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Since I began work on AI in 2010, training compute for frontier models has grown by one trillion times. Now we're looking at something like another thousand-fold growth in effective compute by the end of 2028. 1000x the existing 1,000,000,000,000x. Extraordinary stuff.
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MIT CSAIL explores efficient models for complex reasoning at ICLR
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This sampling of MIT CSAIL papers at ICLR shows a common need for efficient models that can reason about complex, real-world problems. More compute helps, but the ways these machines "think" also need refinement. You can find more info about these projects on our website:
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Snapchat Achieves 76% Cost Savings GPU-Accelerated Data Pipelines
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@Snapchat realized 76% daily cost savings on production A/B testing by shifting data pipelines to GPU‑accelerated Apache Spark with NVIDIA cuDF on Google Cloud. -
Schrodinger Accelerates Drug Discovery with NVIDIA GPU Computing
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@Schrodinger is shrinking weekslong drug discovery simulations into hours with NVIDIA accelerated computing on Google Cloud.