xAI is training 7 different models on Colossus 2 in different sizes from 1T to 15T, including Imagine V2. Not soon
RESEARCH
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HappyHorse-1.0 Alibaba Model Tops Artificial Analysis Leaderboard
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It turns out that HappyHorse-1.0—a new model that suddenly topped the Artificial Analysis leaderboard—comes from Alibaba's Taotian Group, developed by a team led by Zhang Di, formerly the head of Kuaishou's Kling project. pic.twitter.com/3QA8FqG0SS
— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026It turns out that HappyHorse-1.0—a new model that suddenly topped the Artificial Analysis leaderboard—comes from Alibaba's Taotian Group, developed by a team led by Zhang Di, formerly the head of Kuaishou's Kling project.
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Anthropic’s Security Capability: Open Source vs Centralized Control
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On Mythos: I know it feels utterly wrong and bad to say it's better/safer if this capability was widely available and e.g. open sourced, but it actually might be. Anthropic could now be sitting on so many zero-days for countless companies, institutions, etc… It's noble of them to not deploy and build this defensive coalition first, but historically these kinds of power overhangs are very risky In the short term, it's positive that this capability is not open; IMO in the long term, we're all better off if it's diffused so that power is not centralized with one actor (no matter how noble). So kudos to Anthropic for taking the step; now the open source frontier will and should catch up; it'll be a bloodbath at first but it'll lead to better security mindset and infra
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Frontier AI Models Race: Mythos, Spud, and Google’s Response
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We've now seen Claude Mythos and know what's possible. OpenAI has repeatedly indicated that "Spud" is likely to have similar quality and power. Google, in turn, has the most compute (5m H100 equivalent) and, with DeepMind, an outstanding research institution. I expect their new Gemini equivalent, "Mythos," to be unveiled no later than May at I/O. The competition is now forcing Frontier Labs to catch up and move forward. In that sense, Mythos was just the beginning.
→ View original post on X — @kimmonismus, 2026-04-08 09:00 UTC
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AI Replaces Animal Testing in Laboratory
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AI will save laboratory rabbits! An AI analyzed the irritant potential of over 3,000 chemical substances on human skin. Result: 19,000 rabbits spared. No more need to put products in their eyes for 14 days to see if it burns. 190 million animals used each year in labs worldwide. AI, digital twins, and organs-on-chips are making all that obsolete. For once, technological progress is also moral progress. [Translated from EN to English]
→ View original post on X — @alex_tsico, 2026-04-08 08:47 UTC
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From Recording Reality to Reconstructing It: Platform Shift
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My biggest takeaway: We are moving from recording reality to reconstructing it. That is a major platform shift, and the companies that understand it early will have an edge in how they train, build, and analyze. Watch the full video to see what this looks like in practice, and check out what InSpatio is doing with InSpatio World. Where do you think navigable video becomes most valuable first, robotics, gaming, training, or real-world analysis? Don't miss out on the latest AI advancements! Sign up here to stay informed! intelligentworld.org/discove…
→ View original post on X — @ronald_vanloon, 2026-04-08 08:32 UTC
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Steel Meets Shaolin: Humanoid Robots Learning Kung Fu
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Steel Meets Shaolin: Humanoid #Robots Take on Kung Fu
— Ronald van Loon (@Ronald_vanLoon) 8 avril 2026
by @reborn_agi
#AI #MachineLearning #Robotics #ArtificialIntelligence #Innovation #ML #Technology pic.twitter.com/5zrLCPMTWiSteel Meets Shaolin: Humanoid #Robots Take on Kung Fu by @reborn_agi #AI #MachineLearning #Robotics #ArtificialIntelligence #Innovation #ML #Technology
→ View original post on X — @ronald_vanloon, 2026-04-08 08:27 UTC
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Simulated Data Limitations in AI and Autonomous Vehicles
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Great I still do not believe it is as good as capturing people manipulating the real world with their hands. How can simulated data figure out everything humans need to do? Self driving cars are same. Simulated data might get you 99% of the way there but the 1% is still a big
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Cheers: Unified Multimodal Model for Image Understanding Generation
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AI could understand and generate images from a single, efficient model!
— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026
Tsinghua University, Xi'an Jiaotong University, and University of Chinese Academy of Sciences present Cheers!
This unified multimodal model decouples fine image details from their core semantic meaning.… pic.twitter.com/s0MgsejA97AI could understand and generate images from a single, efficient model! Tsinghua University, Xi'an Jiaotong University, and University of Chinese Academy of Sciences present Cheers! This unified multimodal model decouples fine image details from their core semantic meaning. This new architecture stabilizes AI's understanding while boosting image generation fidelity by selectively re-injecting those details. Cheers matches or outperforms advanced unified multimodal models in both visual understanding and generation. It notably beats Tar-1.5B on GenEval and MMBench, using only 20% of the training cost and achieving 4x token compression. Breakthrough efficiency! Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation Project: github.com/AI9Stars/Cheers Model: huggingface.co/ai9stars/Chee… Paper: arxiv.org/abs/2603.12793 Our report: mp.weixin.qq.com/s/EK6cyCJz5… 📬 #PapersAccepted by Jiqizhixin
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Asia surpasses Europe technologically according to Alexandre Tsicopoulos
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L’Asie a pris l’avantage technologiquement sur l’Europe.
— Michel Levy Provençal (@mikiane) 8 avril 2026
Extrait de mon interview avec @Alex_Tsico pic.twitter.com/7mLU62whyNAsia has gained a technological advantage over Europe. Excerpt from my interview with @Alex_Tsico [Translated from EN to English]
→ View original post on X — @alex_tsico, 2026-04-08 08:00 UTC