The secret of Cerebras’ architecture isn’t just our giant wafer – it’s our highly scalable wafer-scale cluster design. This means that whether you program 1 or 2048 nodes, the entire cluster appears as a single chip. No Megatron, no DeepSpeed, no sharding – it’s the speed of a
COMPUTING
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Python Integer Singleton Implementation Detail Explained
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(Technically I think every call does share the same 3, but that's due to a Python implementation detail where integers below 255 are all allocated as singletons)
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Cerebras CS-3 Achieves 256 Exaflops for AI Supercomputing
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The Cerebras CS-3 redefines scalability in AI supercomputing. A 2048 CS-3 cluster can deliver an astounding 256 exaflops of AI compute. This makes it possible to train Llama2-70B in less than one day—a task that would take at least one month on gigantic GPU clusters. The entire
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Neuralangelo: NVIDIA’s AI Model for High-Fidelity 3D Surface Reconstruction
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Neuralangelo is an #AI model for high-fidelity neural surface reconstruction
— Ronald van Loon (@Ronald_vanLoon) 1 avril 2024
via @NVIDIAAIDev#ArtificialIntelligence #Innovation #FutureOfWork #Tech #MachineLearning #3D
cc: @pascal_bornet @patrickgunz_ch @chr1sa pic.twitter.com/C5rp4KER3dNeuralangelo is an #AI model for high-fidelity neural surface reconstruction via @NVIDIAAIDev #ArtificialIntelligence #Innovation #FutureOfWork #Tech #MachineLearning #3D cc: @pascal_bornet @patrickgunz_ch @chr1sa
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Stanford Launches Embedded Ethics Initiative for Computer Science Students
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October 2020: Together with @stanfordethics and @stanfordeng
, we launched the Embedded Ethics initiative to ensure that computer science students understand the importance of ethics in a technological context. #MondayMilestones 8/n https://
hai.stanford.edu/news/building-
ethical-computational-mindset
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3D Athlete Tracking: Boost Performance with AI and Edge Computing
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#3D Athlete Tracking: Boost Performance
— Ronald van Loon (@Ronald_vanLoon) 1 avril 2024
by @Ronald_vanLoon & Chi Tran |#IntelAmbassador @Intel @IntelBusiness @IntelEdge #MWC24 #5G #AI #Edge #Sports #DataAnalytics #IoT
Cc: @KirkDBorne | @jamesvgingerich | @Chels_LA | @BevEve | pic.twitter.com/H03n0hIKyg#3D Athlete Tracking: Boost Performance
by @Ronald_vanLoon & Chi Tran | #IntelAmbassador @Intel @IntelBusiness @IntelEdge #MWC24 #5G #AI #Edge #Sports #DataAnalytics #IoT Cc: @KirkDBorne | @jamesvgingerich | @Chels_LA | @BevEve | -
10 Emerging Innovations That Could Redefine IT
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10 emerging #Innovations that could redefine #IT
by @peterwayner @CIOonline Read more: https://
buff.ly/3JRhiKW #AI #BigData #MachineLearning #ArtificialIntelligence #Cloud #ML #Blockchain cc: @terenceleungsf @yvesmulkers @pascal_bornet -
Decentralized Web of Trust: Personal Reputation Systems Explained
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I like the idea of the web of trust. How would trust be established/signed? Would that be like page rank but for trust? Would trust be personal: e.g. I might not necessarily trust the same website my mom does?
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New Method to Visualize Living Cell Activity at MIT
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A new way to see the activity inside a living cell
by Anne Trafton @MIT Read more: https://
buff.ly/3NahTL7 #Healthcare #HealthTech #Technology #Innovation #FutureOfWork #AI cc: @siddhartha265 @nathealings @guzmannutrition -
Reproducing Benchmarks: Torch Implementations and Performance Comparisons
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The code to reproduce the benchmarks yourself is in the link. To note, the native torch implementations used here are well optimized implementations that are widely used. If you compare "plain" implementations (e.g. research code) it's not unusual to see a 5x speedup. I get the