[BS Fuji Prime News]
AI Analysis of SNS Criticism Against Japan Aguko Tomoko × Minemura Kenji × Ishii Junya Broadcast on 2026/6/3 Front Part: https://
youtu.be/oN543zF6mVU
Back Part: https://
youtu.be/EEx7gkZx0ac
@sakanaailabs
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BS Fuji Prime News: SNS Criticism AI Analysis – Aguko, Minemura, Ishii Discussion
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Official DiffusionBlocks Implementation for ViT Image Classification
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Our official implementation of DiffusionBlocks on image classification using Vision Transformers (ViT)
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Introducing DiffusionBlocks for independent block-wise neural network training
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DiffusionBlocks: Training Neural Networks One Block at a Time https://
pub.sakana.ai/diffusionblock
s/
… tl;dr We introduce DiffusionBlocks, a principled framework that partitions a residual network into blocks and trains each one independently by reinterpreting block-wise updates as the reverse -
Sakana AI Partners with DEEP DIVE for AI-Powered Analysis
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Sakana AI has entered into a partnership with the non-profit organization DEEP DIVE to leverage AI for information analysis. https://
sakana.ai/deep-dive-part
nership/
… ■Expertise and open data × Proprietary AI technology
■Achieving analysis at scales, speeds, and resolutions that are difficult -
Block-by-Block Neural Network Training Framework
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ニューラルネットワークをブロックごとに学習する枠組みを開発
— Sakana AI (@SakanaAILabs) 27 mai 2026
ブログ: https://t.co/45Xvzl1T1k
ニューラルネットワークの学習は通常、ネットワーク全体を一度に扱う必要があり、深いモデルほど多くのメモリを必要とします。このメモリ消費は、近年のAIモデルの大規模化を支える上で大きな制約と… https://t.co/DhnviMIgPdDeveloping a Framework for Learning Neural Networks Block by Block Blog: http://
pub.sakana.ai/diffusionblocks Neural network training typically requires handling the entire network at once, and deeper models demand even more memory. This memory consumption has become a major constraint in -
DiffusionBlocks: Block-wise Neural Network Training via Diffusion
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Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretationhttps://t.co/45Xvzl2qQS
— Sakana AI (@SakanaAILabs) 27 mai 2026
What if we didn’t have to hold an entire neural network in memory to train it?
Standard neural net training optimizes all parameters jointly. As a result, the… pic.twitter.com/jJtFg61nJhIntroducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation http://
pub.sakana.ai/diffusionblocks What if we didn’t have to hold an entire neural network in memory to train it? Standard neural net training optimizes all parameters jointly. As a result, the -
AI Predicting Scientific Progress: Oxford Stanford Study
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How Far Can the Progress of Science Be Predicted by AI? A paper verifying the ability of cutting-edge AI to predict future scientific achievements has been published as a collaboration with researchers from the University of Oxford, Stanford University, @Allen_AI
, and others. -
Transformer research symposium with original authors
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先日サンフランシスコで開催された討論会「Transformers vs Post-Transformers」に、Sakana AIの共同創業者兼CTOであるLlion Jones @YesThisIsLion が登壇しました。
— Sakana AI (@SakanaAILabs) 21 mai 2026
本イベントは、現在のAI界を牽引するアーキテクチャ「トランスフォーマー」について、論文共著者を含む4人が、トランスフォーマー… https://t.co/KjkRgKKauc pic.twitter.com/ql0GpZQN1nLlion Jones @YesThisIsLion
, co-founder and CTO of Sakana AI, took the stage at the recent symposium "Transformers vs Post-Transformers" held in San Francisco. The event featured four panelists—including co-authors of the original Transformer paper—divided into supporters of the -
KAME AI model uses parallel LLM processing for real-time speech
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In KAME, a fast speech model starts replying instantly, while a backend LLM runs in parallel to inject deep knowledge on the fly. It’s a completely different way to approach conversational AI, making it feel remarkably more alive. Try the KAME model here
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Open-Sourced Sparse Training Code for H100 GPUs
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For those interested in the implementation details, we’ve open-sourced the reference code for this paper. The repository includes our sparse training code and the custom CUDA kernels designed for H100 GPUs leveraging the TwELL packing format. GitHub: