Supercomputing for AI — Foundations, Architectures, and Scaling Deep Learning. [804-page masterpiece] Read it online: https://
jorditorresbcn.github.io/supercomputing
-for-ai-book/
… Buy it: https://
amzn.to/4qS4pFz GitHub repo: https://
github.com/jorditorresBCN
/supercomputing-for-ai
…
RESEARCH
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Supercomputing for AI — Foundations, Architectures, and Scaling
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SciSpace expands AI-powered research platform with new integrations
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Great news today in the AI-powered research world!@SciSpace has released new capabilities +plus+ two major upgrades in its all-in-one AI-powered research platform:
— Kirk Borne (@KirkDBorne) 25 février 2026
1️⃣Extended Apps Integration in the SciSpace Agent — GitHub, Notion, OneDrive (added to existing integrations with… pic.twitter.com/C6YRpH3IE7Great news today in the AI-powered research world! @SciSpace has released new capabilities +plus+ two major upgrades in its all-in-one AI-powered research platform:
Extended Apps Integration in the SciSpace Agent — GitHub, Notion, OneDrive (added to existing integrations with -
News anchors now explain language model distillation better than experts
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Even news anchors now know what language model distillation is. In fact, they explain it better than I do Thanks to Anthropic 😀
→ View original post on X — @skathirmani, 2026-02-25 13:08 UTC
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Why ‘Be Creative’ Fails in AI
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First, understand WHY "be creative" fails. AI creativity is probabilistic. It defaults to the most statistically common answer. "Be creative" has no constraints.
No constraints = no creative pressure.
No pressure = average output. The fix isn't less structure. It's MORE of the -
The Hidden Truth Behind 2026’s Massive Context Windows
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Everyone's bragging about 200k, 1M, even 10M token context windows in 2026. Nobody's talking about what actually happens inside them. I just fell down this rabbit hole and I can't stop thinking about it: A new paper published January 2026 tested hundreds of thousands of data
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Open Source Data: Essential Missing Component for Large-Scale Training
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More Open Source Data. The main missing ingredient for large scale training.
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Industrial audio-video generation models reach stereo quality standards
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Recent industrial audio-video gen model(s) is already stereo (more than good), what else to do in this area hmm
→ View original post on X — @shiqi_yang_147, 2026-02-25 03:31 UTC
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Overview of Loss Functions in Machine Learning
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Machine Learning loss functions #infographic Loss Functions in ML Explained: https://
datacamp.com/tutorial/loss-
function-in-machine-learning
… Comprehensive Review of Loss Functions in Deep Learning: https://
arxiv.org/pdf/2504.04242
v1
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#AI #DataScience #DataScientist #Mathematics -
Full motion transformer trained in 3 days on 128GPUs at 10000x speed
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This full motion transformer was trained in 3 days on 128GPU at 10.000x faster than wall clock speed.
— Linus ✦ Ekenstam (@LinusEkenstam) 25 février 2026
in english: this AI model controls the motion of the robot.
It supports, text to command, remote teleportation and much more.
Amazing work by Jim Fan et al. 🫰 https://t.co/ynXhrNwu5iThis full motion transformer was trained in 3 days on 128GPU at 10.000x faster than wall clock speed. in english: this AI model controls the motion of the robot. It supports, text to command, remote teleportation and much more. Amazing work by Jim Fan et al.
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Mercury 2: Diffusion LLM Achieves 5x Faster Inference Speed
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Impressive inference speed from Inception Labs’ diffusion LLMs. Diffusion LLMs are a fascinating alternative to conventional autoregressive LLMs. Well done @StefanoErmon and team! https://t.co/w4w5QZpyp6
— Andrew Ng (@AndrewYNg) 25 février 2026Impressive inference speed from Inception Labs’ diffusion LLMs. Diffusion LLMs are a fascinating alternative to conventional autoregressive LLMs. Well done @StefanoErmon and team! Stefano Ermon (@StefanoErmon) Mercury 2 is live 🚀🚀 The world’s first reasoning diffusion LLM, delivering 5x faster performance than leading speed-optimized LLMs. Watching the team turn years of research into a real product never gets old, and I’m incredibly proud of what we’ve built. We’re just getting started on what diffusion can do for language. — https://nitter.net/StefanoErmon/status/2026340720064520670#m
→ View original post on X — @andrewyng, 2026-02-25 02:04 UTC