Without a trace of irony, at all: one of the most important strategic decisions for every publication, think tank, and etc is “What decisions are we taking today to influence future training runs and inference sessions?”
RESEARCH
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ERNIE 5.1 achieves near SOTA with only 6% pre-training cost
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Hold on, Chinas ERNIE 5.1 is almost SOTA but using only around 6% of the pre-training cost of comparable models?? ERNIE 5.0’s pre-training foundation: Baidu says ERNIE 5.1 achieves stronger search, reasoning, knowledge Q&A, creative writing, and agentic capabilities while using
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13B Language Model Mimicking 1930
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Researchers trained a 13B language model that thinks it's 1930.
— AlphaSignal AI (@AlphaSignalAI) 9 mai 2026
It speaks like someone from a century ago, with no awareness of computers, World War II, or anything after.
The team behind it, talkie, gathered 260B tokens from public-domain books, newspapers, patents, and… pic.twitter.com/JDbjmh1E3bResearchers trained a 13-billion-parameter language model that believes it is 1930. It communicates like someone from a century ago, unaware of computers, World War II, or any events that followed. The team behind it, Talkie, collected 260 billion tokens from public-domain books, newspapers, and patents.
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New Research Paper Examines Capabilities of Autonomous AI Agents
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Harvard, MIT, Stanford & Carnegie Mellon just released one of the most disturbing AI papers of 2026. “Agents of Chaos” http://
arxiv.org/pdf/2602.20021 Autonomous AI agents with real tools:
→ Email
→ File systems
→ Persistent memory
→ Shell access http://
drdebashisdutta.com -
Context Awareness Unlocks Smarter Research
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The bigger unlock is context awareness. You can bring:
→ Multiple tabs
→ Documents
→ Images Into one query. That means the system understands your entire research flow, not isolated searches. This is what Chrome’s AI Mode is aiming for, and it changes how we learn and -
Lighter and Faster Language Models
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Sparser, Faster, Lighter Transformer-Based Language Models Paper:
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New RAG Approach ‘Blockify’ Improves Search Efficiency
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Naive RAG vs. Blockify!
— Akshay 🚀 (@akshay_pachaar) 9 mai 2026
There's a new RAG approach that:
– cuts corpus size by 40x.
– reduces tokens per query by 3x.
– improves vector search relevance by 2.3x.
Blockify GitHub: https://t.co/R0LSWC1V8a https://t.co/xNuoXSBj7E pic.twitter.com/ibwgORIHIbNaive RAG vs. Blockify! There's a new RAG approach that: – cuts corpus size by 40x.
– reduces tokens per query by 3x.
– improves vector search relevance by 2.3x. Blockify GitHub: https://
github.com/iternal-techno
logies-partners/blockify-agentic-data-optimization
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3D Deep Learning with Python — PyTorch3D for Computer Vision
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3D Deep Learning with Python — Design and develop Computer Vision models with 3D data using PyTorch3D: http://
amzn.to/491yDwh v/ @PacktDataML ————
#AI #MachineLearning #ML #DataScience #DataScientist #PyTorch -
Deep Learning with TensorFlow and Keras 3rd Ed. Book Announcement
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Deep Learning with TensorFlow and Keras — Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models (3rd Ed., 667 pages): http://
amzn.to/3gitVEJ v/ @PacktDataML —————
#DataScientist #DataScience #AI #MachineLearning #ML -
Practical Guide to Reinforcement Learning from Human Feedback released
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New release from @PacktDataML available at http://
amzn.to/3PMn1ZL A Practical Guide to Reinforcement Learning from Human Feedback (RLHF). Amazon Summary: RLHF is a powerful approach to AI alignment and human-centered machine learning. By combining reinforcement learning