It's nice to have good names for things. I'm proud to have named or been involved in naming a bunch of things at Google over the years, including: MapReduce
Bigtable
Spanner
TensorFlow
Tensor Processing Units (TPUs)
Pathways
Protocol Buffers
PaLM
Gemini
COMPUTING
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Naming Major AI and Computing Technologies at Google
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Switching to MacBook Pro: Peer Pressure Influence on Tech Adoption
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I was peer pressured by Twitter people to switch so when I got a new MacBook Pro this year I did
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5G Technology Enhances PGA Championship Broadcasting Operations
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Catch more highlights!from the #PGAChamp !
An absolute pleasure to speak with Mo Katibeh CMO at @TMobileBusiness sharing personal highlights and discussing how #5g tech is enhancing #broadcasting #operations and #experience – from the fairway to home viewing alike! -
Generational Divide: How Tech Upgrades Define Modern Childhood
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Children today upgrade phones and iPads so matter-of-factly. We got more thrills when we were finally allowed to switch from fountain pen to ball pen.
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Blended AI Systems: Reduced Computational Requirements for Efficient Inference
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3. Efficient Inference: Blended systems require significantly less computational power. Each response is generated by a single model, maintaining the speed and efficiency of smaller models.
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Blending Smaller AI Models for Cost-Effective Performance
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Blending smaller AI model enables: 1. Cost-Effective Performance: Combining three mid-sized models (6B/13B parameters) can rival or surpass the capabilities of a single large model without the hefty computational demands.
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NVIDIA’s Vision AI Pipelines: DeepStream SDK Development Impact
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Discover NVIDIAN Aparna Chhajed’s impact on vision AI pipelines as she manages software development kit releases on the NVIDIA DeepStream team. #NVIDIAlife https://
nvda.ws/3UKoP4j -
AI Hardware for Accelerated Video Analysis Showcased at EmbVision Summit
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We're excited to showcase our purpose-built hardware at the @EmbVisionSummit from May 21-23! Visit us in booth 622 to see our accelerated AI video analysis demo and let's discuss how our solutions can unlock your #AI #compute capabilities. Meet with us: https://
tinyurl.com/3ajd83ya -
Brain Computing Power Compared to NVIDIA A100 GPU
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The naive napkin math would go something like 1 brain ~= 1e11 neurons * 1e4 synapses * 1e1 fires/s = 1e16 FLOPS (i.e. 10 petaflops) NVIDIA A100 = 312e12 peak FLOPS, in-practice achievable utilization may be let’s say 50%, i.e. 156e12. Dividing you get 1 brain ~= 1e16 / 156e12 =