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be more easily scaled to help grids around the world. Here’s how the company is using carbon dioxide to store renewable energy . —Casey Crownhart —Thore Graepel, a core member of DeepMind’s AlphaGo team Ten years ago, I watched a program I helped build stun the world by beating Go champion Lee Sedol. AlphaGo won after making a move so strange that some commentators thought it was a programming glitch. It was AlphaGo’s powers of reasoning that made this creative choice—and these are powers that

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ddingGemma 2: Google’s first natively multimodal open embedding model covers text, code, image, video and audio in one space. It is built on Gemma 4 and released under Apache 2.0 (DeepMind).Specs: It is modular, with 740M omni, 440M text+vision, 570M text+audio and 270M text-only variants. It has Matryoshka dimensions from 768 down to 128, 8,192 context and a reported +14% on MTEB Code (Phil Schmid); Footprint: It uses roughly 191–567MB of active RAM and handles up to 5.5 minutes of audio or 58

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yFetch (full pages as markdown, JavaScript included), TinyBrowser for logins and forms, and TinyAgent for multi-step jobs. Search and Fetch are free. Asif Razzaq Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4; Asif RazzaqMistral AI Releases Mistral Large 4 (Le Chonk): A 1.05T Parameter Multimodal MoE Model; Asif RazzaqBeyond Domain-Specific World Models: JEPA-Anything Uses 1 Recipe for 7 Fields; Asif RazzaqMeet Together Link: A Free

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stantial improvement from Nano Banana 2: +80 pts in Text-to-Image, +67 pts in Multi-Image Edit, and +38 pts in Image Edit! By category, Nano Banana 2.1 stands out in: - Multi-Image Edit: 3 in Commercial Design and 3D, and 4 across Cartoon/Fantasy, Photorealistic, and Text Rendering - Text-to-Image: 4 Photorealistic, and 5 in both 3D and Text Rendering - Image Edit: 4 across Commercial Design, 3D, Cartoon/Fantasy, and Text Rendering Congrats to the team at @googledeepmind @google on this release!

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stantial improvement from Nano Banana 2: +80 pts in Text-to-Image, +67 pts in Multi-Image Edit, and +38 pts in Image Edit! By category, Nano Banana 2.1 stands out in: - Multi-Image Edit: 3 in Commercial Design and 3D, and 4 across Cartoon/Fantasy, Photorealistic, and Text Rendering - Text-to-Image: 4 Photorealistic, and 5 in both 3D and Text Rendering - Image Edit: 4 across Commercial Design, 3D, Cartoon/Fantasy, and Text Rendering Congrats to the team at @googledeepmind @google on this release!

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yFetch (full pages as markdown, JavaScript included), TinyBrowser for logins and forms, and TinyAgent for multi-step jobs. Search and Fetch are free. Asif Razzaq Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4; Asif RazzaqBeyond Domain-Specific World Models: JEPA-Anything Uses 1 Recipe for 7 Fields; Asif RazzaqMeet Together Link: A Free CLI That Runs Open Models Like Kimi K3 and GLM 5.3 Inside Claude Code, Codex, and OpenCode; Asif Ra

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JEPA-Anything提出并排序实验，然后将结果反馈给模型。代码和模型已公开。 LeCun于2022年提出JEPA，作为生成模型的替代方案。2025年6月，Meta发布了V-JEPA 2，这是一个拥有12亿参数的JEPA视频模型，无需额外训练即可在陌生环境中控制机械臂。2025年11月，LeCun与Randall Balestriero随后推出了LeJEPA，这是一个旨在无需常规变通手段即可保持训练稳定的理论基础。LeCun目前正通过其初创公司AMI Labs推进这一方法，该公司于2026年3月融资超过十亿美元，用于构建世界模型。 Google Deepmind已于2025年10月在实验室中测试了一个由AI生成的癌症假说。其基于Gemma的模型C2S-Scale 27B提出使用药物silmitasertib使肿瘤细胞更容易被免疫系统识别，而利用人类细胞模型进行的实验证实了这一预测。Google Deepmind的多智能体系统Co-Scientist如今能够规划实验并操作实验室设备，覆盖了JEPA-Anything团队想要构建的循环中的部分环节。不过，将样本装入机器仍然需要人工完成。

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e are set to ship "later this month," according to the company. Beam is still going through final safety testing, and an early version is available to select users for now. Former Deepmind researchers backed by $2 billion Reflection was founded in 2024 by former Google Deepmind researchers Misha Laskin and Ioannis Antonoglou. Laskin led reward modeling for Gemini, and Antonoglou helped build AlphaGo. The startup launched in March 2025 with $130 million in seed funding and the goal of building su

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EU border LATEST DEVELOPMENTS REFLECTION AI 🇺🇸 Reflection AI enters the open-model race Image source: Reflection The Rundown: U.S.-based Reflection AI, the $25B startup by former DeepMind researchers, just introduced Beam, an open-weight model for coding and agents that the company says competes with Chinese rivals at lower cost and greater efficiency. The details: Reflection claims Beam needs a fraction of the computing power of Chinese lab Z.ai’s GLM-5.2, but achieves similar scores across re

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f resources and manpower. I think that AI can do incredible things for people who suffer from disease. I don’t think that’s all marketing BS. There’s a story in the book about the DeepMind protein-folding AI system that won the Nobel Prize, and there’s this incredibly touching moment where a mother of a kid who has a life-threatening genetic condition writes to the DeepMind researchers after this breakthrough and asks them, “Could this help my kid?” They have to give her the honest answer, which

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ed interventions improved 12.90%, and 6-step free rollout improved 8.58%. JEPA-Anything improved reported metrics on all 10 matched dynamics tasks. Benchmarks include CausalWorld, DeepMind Control, PDEBench and WeatherBench2. On APEBench Burgers, 6-step rollout error dropped about 44.7%, improving in every seed. For 100-step molecular rollouts with a TrajCast-style backbone, it posted the lowest MAE and RMSD on water, quartz, paracetamol and benzene. Planning results are mixed. With parameters m

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Beam outscores Inkling on four coding tests where both report results, but Inkling is a multimodal model and Beam is text-only. Reflection was founded in 2024 by two former Google DeepMind researchers and has raised roughly $4.7 billion from backers, including Nvidia, Sequoia Capital, and Lightspeed Venture Partners, per PitchBook. Its last round valued the company at a $25 billion pre-money valuation . The startup has also been locking up compute — a key ingredient needed to train frontier mode

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s popular than ICE. Yet the number of people using AI is still skyrocketing. ChatGPT hit a billion monthly users in May, according to the market analysis firm Sensor Tower. Google DeepMind’s Gemini is close behind, logging 950 million users in July. According to Pew, half of US adults now say they use a chatbot , more than twice the number who said they did in 2023. One in four people say they do so every day. And it’s a global habit: More than a third of adults across all 38 OECD countries (a g

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日益增多，而世界两个人工智能大国在过去两年内未曾围绕这一对当今时代具有深远影响的技术举行正式对话，备受世界关注。 如果中美两国希望此次重启对话取得成功，就需要迅速采取行动，秉持清晰的目标推进对话。 令人鼓舞的是，两国开始至少在一个问题上趋于一致，即人工智能安全的重要性。两国的前沿人工智能实验室的负责人近期更加公开地谈论如何为通用人工智能时代的到来做准备。谷歌DeepMind联合创始人、时任首席执行官德米斯·哈萨比斯（Demis Hassabis）在7月的首篇Substack博客文章中强调，“随着人类日益接近通用人工智能（AGI），必须立即采取行动，应对可能产生的风险”。 中国也正在形成类似的共识。DeepSeek首席执行官梁文锋和智谱AI（Z.ai）联合创始人唐杰近期均公开谈及通用人工智能，强调长期发展、安全评估和负责任部署。更广泛而言，中国前沿人工智能企业越来越愿意公开参与治理问题的讨论。 各国政府也开始将人工智能安全提升到政策议程的重要位置。6月，特朗普政府先后发布一项关于人工智能安全的行政命令和一份总统备忘录。两份文件均将人工智能安全置于国家安全框架之内，强调管理能力日益增强的A

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追溯原始文献，独立自主且具有批判性地思考。”马冬昕说。 中国科学院自动化研究所研究员李林静观察到，科研智能体已经能够完成“文献研读、实验规划、参数迭代、结果分析的闭环自主科研流程”。在北京大学干细胞研究中心，一套占地6平方米的智能平台能独立完成从指尖采血到干细胞培育的120多个步骤，实验误差从最高10%降至1%以下。 效率令人振奋，但隐患同样真实。 谷歌旗下DeepMind曾用AI预测出220万种新的晶体结构，其中38万种被判定为“稳定”。然而，得到实验验证的不到0.2%。中国工程院院士李国杰将这一现象概括为：AI产生了海量“看似合理”的假说，绝大多数无法被验证，导致科研资源被浪费在低优先级选项上。 “看似合理”四个字，精准击中了许多一线研究者的焦虑。 中国科学院空天信息创新研究院国家对地观测科学数据中心常务副主任张连翀的观察很直接：“通用AI系统缺乏主动拒答的能力——即便面对超出其知识范围的问题，它仍会尽力生成一个看似合理的回答，而该回答的准确性并无保障。” 栾浩从技术原理上解释了这种“一本正经胡说”的根源：当问题超出大模型的知识库，或者大模型对于问题的理解有偏差时，它会“强行搜索”

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Anthropic, OpenAI and Google DeepMind shipped 4 frontier-class models within 30 days. Claude Fable 5.1 arrived on September 1. GPT-6 Astra followed on September 3. GPT-6.1 Sol and Gemini 4 Argon landed in the last days of September. We covered each launch on its own. This piece puts them side by side. The benchmark scores overlap more than the launch posts suggest. The prices, access rules and cost per task do not. One change frames the lineup. OpenAI cancelled GPT-6.1 Astra on September 28 afte

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ce , trained on Landsat and Sentinel-2 imagery of the entire contiguous US and adapted for flood and wildfire mapping. IBM had announced Prithvi as part of its Watsonx platform . IBM developed TerraMind, the lunar model's base, in 2025 with ESA and Forschungszentrum Jülich for Earth observation. Google Deepmind is pursuing a related approach with AlphaEarth Foundations , compressing optical satellite imagery, radar, lidar scans, and climate simulations into compact embeddings of Earth's surface.

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neers before release (Logan Kilpatrick); Contested readiness: A circulated Bloomberg report attributed coding weaknesses to anonymous insiders; a subsequent post reported a senior DeepMind engineer rejecting that account. Treat the practical coding-quality dispute as unresolved, rather than interpreting either benchmarks or employee reactions as decisive (reported criticism, reported rebuttal); GPT-6.1 Sol: OpenAI’s update is primarily an efficiency story. Sam Altman called it the company’s fast

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data; Brex improved AI answer accuracy to 90% Give your agent a single source of truth. GOOGLE 🧬 Google’s AI watermarks cross into the lab Image source: Google The Rundown: Google DeepMind just introduced SynthID Bio, a proof of concept that extends its AI watermarking tech to proteins, potentially helping DNA suppliers trace unfamiliar designs before filling an order. The details: SynthID Bio tweaks Google’s protein-folding AlphaFold 3 model to leave a hidden pattern in its predicted protein sh

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ects it was trained on large amounts of 3D data like Blender scenes . 3D software makers are already gearing up for these kinds of agents. Unity has released official plugins for Claude Code and Codex . Other approaches skip code entirely and reconstruct scenes directly inside the model, like the Atlas world model from World Labs . Google Deepmind takes yet another route with GenCeption, using a video model for depth estimation and segmentation that matches the performance of specialized models.

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