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RESEARCH
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Continuous learning from observations and mistakes boosts robustness and adaptability
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Congrats! The potential of systems that learn continuously from observations and their own mistakes (and successes!) is high, and these systems are likely to be more robust and adaptable than systems that don't learn continuously.
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Google’s Omni multimodal model and blending potential
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Google has the only true Omni model, but the elements aren't hooked up. It appears it can take in & output audio, images. video, songs, text, code, etc. But right now each type of output is separate. When you can access the model directly, blending modes, a lot becomes possible.
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Biohub’s Protein World Model: ESMC-6B and Scaling Laws in Biology
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Biohub’s Protein World Model: ESMC-6B, ESMFold2, 6.8B proteins, 1.1B structures, antibody design, SAEs, & the bitter lesson for biology https://
latent.space/p/esmfold2 @biohub Head of Science @alexrives explains why biology may scale like language modeling, how metagenomics unlocked -
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 -
AI Hiring Tools Show Algorithmic Discrimination Against Minority Applicants
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New research reveals alarming patterns in AI hiring tools.
— Stanford HAI (@StanfordHAI) 27 mai 2026
A large-scale study of 4 million job applications found that 26% of Black applicants and 15% of Asian applicants faced algorithmic discrimination.
When one AI vendor screens for multiple employers, qualified candidates… pic.twitter.com/rbpsO5S1yENew research reveals alarming patterns in AI hiring tools. A large-scale study of 4 million job applications found that 26% of Black applicants and 15% of Asian applicants faced algorithmic discrimination. When one AI vendor screens for multiple employers, qualified candidates
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WordPress categories covering AI topics
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Web designers after reading this: https://t.co/yONuEtjT8L pic.twitter.com/p3y16ldruL
— Charly Wargnier (@DataChaz) 27 mai 2026Web designers after reading this:
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No filter beats filtered data in large LLM training
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“A Bitter Lesson for Data Filtering” A common consensus is that LLMs need carefully filtered web data, because noisy data can easily hurts in small compute regimes. But this paper shows that with a large enough model size and training, the best filter is no filter. Raw Common
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DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
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"DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation" End to end backprop stores activations through every layer, which is why deep Transformers is expensive to train. This paper reinterprets residual blocks as diffusion denoising steps, so each
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5-second video generation in 4.2s on single Blackwell GPU open-sourced
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You should read this thread.
— NVIDIA AI (@NVIDIAAI) 27 mai 2026
It used to take about 25 seconds to generate a 5-second video on 8 Blackwell GPUs. The legends at @haoailab brought that down to just 4.2 seconds on a single Blackwell GPU… and then open sourced the tech behind it. https://t.co/egQnhx0N1eYou should read this thread. It used to take about 25 seconds to generate a 5-second video on 8 Blackwell GPUs. The legends at @haoailab brought that down to just 4.2 seconds on a single Blackwell GPU… and then open sourced the tech behind it.