If you're interested in finding out more about AlphaGo and move 37, see here: https://t.co/sV5gHepsNX
— Demis Hassabis (@demishassabis) 12 mars 2026
If you're interested in finding out more about AlphaGo and move 37, see here:
RESEARCH
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AlphaGo Move 37: Exploring AI’s Revolutionary Chess Moment
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GENIUS Suite Evaluates Multimodal AI Reasoning Capabilities
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Can your favorite AI truly reason on the fly, or just remember what it's learned? A team from Peking University, CUHK, StepFun, PolyU, and MSRA introduces GENIUS, a groundbreaking evaluation suite. GENIUS challenges Unified Multimodal Models (UMMs) on Generative Fluid
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How Not to Be Fooled by Viral Charts
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How not to be fooled by viral charts https://
buff.ly/NwsCUYR
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Google Researcher: AI Crisis Lies in Inference, Not Training
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BREAKING: A Google researcher and a Turing Award winner just published a paper arguing that the real AI crisis isn’t training. It’s inference. And the hardware stack we rely on today was never built for it. The paper, by Xiaoyu Ma and David Patterson, was accepted by IEEE
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10 GitHub Repos to Master AI Engineering in 2026
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If you want to become an AI engineer in 2026, spend less time collecting tutorials and more time reading real code, real notebooks, and real systems. Here are 10 GitHub repositories that can teach you more practical AI engineering than many expensive courses: 1) AI Agents for
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Debunking LLM Consciousness and Supply Risk Claims
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Wild.
— Gary Marcus (@GaryMarcus) 12 mars 2026
If any of this is true (and most if it isn’t*) it’s true of all LLMs and not just Anthropic’s models.
There is no technical difference between models that would make one more of a supply risk than another.
*mimicking text doesn’t make an AI conscious or anxious etc. but… https://t.co/SzI5FULryYWild. If any of this is true (and most if it isn’t*) it’s true of all LLMs and not just Anthropic’s models. There is no technical difference between models that would make one more of a supply risk than another. *mimicking text doesn’t make an AI conscious or anxious etc. but
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Scalable MoE Training Efficiency with Megatron Core
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“Scalable Training of Mixture-of-Experts Models with Megatron Core” This NVIDIA MoE report walks through the hard part of MoE training. The key is not to add more parameters, but keeping sparse models efficient when only a small part of the model runs for each token. For
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PHAN seeks proposals to reduce airborne disease transmission
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In Public Health Action Network we’re about to go through an internal expert review process to choose how to focus our efforts over the next few years. We’ll be deciding, how can we, as a small non-profit, most effectively reduce the spread of airborne disease. If you have creative, high leverage ideas for us to pursue, please submit a proposal for us to evaluate alongside our own ideas. Public Health Action Network (@pubhealthaction) PHAN is now accepting requests for proposals for projects that will have measurable results in reducing transmission of indoor airborne pathogens. These could be projects like breath-based multi-pathogen detection systems or continuous HVAC compliance verification systems. — https://nitter.net/pubhealthaction/status/2028878294813397321#m
→ View original post on X — @goodfellow_ian, 2026-03-12 17:05 UTC
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CHMv2 Open Source for Global Carbon and Land Management
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CHMv2 is already supporting public sector efforts in the United States, Europe, and beyond. By making these advances open source, we aim to accelerate research and inform carbon offsetting, reforestation, and land management decisions globally. Read the paper:
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Canopy Height Maps v2: Open Source Global Forest Mapping Model
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We’re announcing Canopy Height Maps v2 (CHMv2), an open source model for high-resolution global forest canopy mapping, developed in partnership with the @WorldResources.
— AI at Meta (@AIatMeta) 12 mars 2026
CHMv2 leverages our DINOv3 Sat-L vision model, specifically optimized for satellite imagery, to deliver… pic.twitter.com/atTCZgkEsxWe’re announcing Canopy Height Maps v2 (CHMv2), an open source model for high-resolution global forest canopy mapping, developed in partnership with the @WorldResources
. CHMv2 leverages our DINOv3 Sat-L vision model, specifically optimized for satellite imagery, to deliver