Seedance 2.0 Shows How Far AI Has Come https://
youtu.be/6n9fjS0of5o?si
=OAas0qmRSQUYbmxr
… via @YouTube #seedance #artificialintelligence #AI #AIart️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️ #AIartist #AIvideo #video @SpirosMargaris
MACHINE LEARNING
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Seedance 2.0 Shows How Far AI Video Has Come
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GLM 5.2 numbers imply quicker Fable 5 arrival than predicted
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GLM 5.2 numbers make me believe I was too conservative in my own prediction 2 months tops and we'll have Fable 5 at home
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DeepSeek rewires residual connections that power AI
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DeepSeek Is Rewiring the Residual Connections That Still Power AI! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming
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SkyPilot: Run and Scale LLM Workloads Across Any AI Infrastructure
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SkyPilot! SkyPilot: Run and Scale Large Language Model Workloads Across Any AI Infrastructure! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless
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AI benchmarks correlations: value in disentangling them
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Everything is correlated in AI benchmarks. The value would be picking apart the correlation
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Artificial Analysis useful but index lacks real-world validity
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I think artificial analysis fills a useful spot in the ecosystem for independent assessment, but the index has very little validity compared to real world tasks. It is just lucky that basically every measure is correlated so you can pick any set of benchmarks and they kinda work
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Critique of AI benchmark using AI evaluation on public questions
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This was not a good benchmark before it was updated and it is not a good benchmark now. Having AIs evaluate the work of other AIs on publicly available questions from a different closed benchmark doesn’t tell you very much. And it is unclear how they establish the human ELO.
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SambaNova ready for RaiseSummit 2026: inference 2.0 with GPU and RDU
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We're ready for @RaiseSummit 2026 🙌
— SambaNova (@SambaNovaAI) 16 juin 2026
Training built the models. Inference put them to work. Inference 2.0 is about disaggregating the workload.
GPUs for prefill. RDUs for decode. The right chip for the right job.
Join us at RAISE Summit in Paris to see what's next:… pic.twitter.com/FNThE5nXi8We are ready for @RaiseSummit 2026 Training built the models. Inference put them to work. Inference 2.0 is about disaggregating the workload. GPU for prefilling. RDU for decoding. The right chip for the right task.
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Combining hardware architectures to split shards and run models simultaneously
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It means combining different hardware architectures to split shards / run models across all of them at the same time
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New article by LeCun: visual learning via temporal differences
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Another interesting article supervised by Yann LeCun! "You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences" This article proposes Temporal Difference in Vision (TDV), which is a simple idea for learning vision from videos.