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ost harmful effects, and that’s where our 10 companies come in. They’re working on everything from mobile flood barriers and semi-solid-state batteries to compressed-CO₂ energy storage and next-generation nuclear reactors. They also reflect the biggest shifts in climate tech—including the surging energy demands of AI data centers. Meet the 10 companies working on some of the biggest challenges in climate tech . Over the coming days, we’ll take a closer look at each of the 10 companies, along wit

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ike Indonesia, the value of open models goes beyond access to powerful AI. It is about adapting that intelligence to our own language, culture, data and real-world needs,” said Chirag Sukhadia, chief data and AI officer of Indosat Ooredoo Hutchison. “Sahabat-AI puts this into practice, using open models as a foundation to build AI that understands Indonesia and can be developed for local applications. This allows us not only to adopt AI, but to build capabilities around it and enable more Indone

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with the Data Orchestration Engine for taking in, preparing, labeling, and enriching data, complete with unified data pipelines, distributed control that decouples compute from storage, and native access to Nvidia NIM microservices, AI Blueprints, and templates. The Data Engines for analytics, processing, and search helps organizations find the use the right data they need in hours instead of weeks, Chhabra said. Then come the Storage Engines that include PowerScale for network attached storage

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uinely accessible across different languages; We find that in-language reasoning can be achieved through data mixing built on three pillars. First, ensuring a broader language coverage in reasoning training data improves generalization to languages without reasoning supervision. Second, including a small share of multilingual non-reasoning data boosts both accuracy and in-language reasoning. And third, using English reasoning data provides the task-solving backbone; Our resulting model, Tiny Aya

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aker-attributed word error rate, because every score and summary depends on the underlying transcript; An effective platform also needs real-time and batch modes, multilingual coverage, entity detection, and CRM integration, which ElevenLabs delivers through Scribe v2 and Scribe v2 Realtime with support for SOC 2, HIPAA, GDPR, and EU data residency What is conversation intelligence and how is it used? Conversation intelligence is software that records and transcribes customer conversations, then

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Demand for energy storage is surging, with capacity expected to grow eightfold in the next decade. To meet that demand, Moment Energy wants to shortcut a supply chain that now flows largely through China, by domestically repurposing EV batteries into new grid-connected storage systems. Moment Energy’s four founders met while studying mechatronics engineering in college and worked together on a team that built and raced electric cars. After graduating, they scattered to companies including Tesla

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o waste. The Xe-100 might produce 10 times the volume of spent nuclear fuel per unit of energy as existing reactors, although its lower-level waste will need less sophisticated storage . The US Nuclear Regulatory Commission has issued X-energy’s subsidiary with a fuel fabrication license for TRISO-X pebbles, with its first factory due for completion in 2028. An Xe-100 to supply heat and power for a large chemical facility in Texas operated by Dow has also cleared its first regulatory hurdle , an

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e affordably than fossil fuels in most places, but their output varies based on season, time of day, and weather. To compensate, utilities are increasingly investing in battery storage systems, which can charge when power is abundant and discharge when the grid needs a boost. Lithium-ion batteries are the fastest-growing storage technology. Although light and energy-dense, they require pricey metals and are costly to scale, and few of the large batteries of this type used in industry are designe

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ist includes two Chinese companies: WeLion is building semi-solid-state batteries for electric vehicles, and Envision Energy is installing huge amounts of wind power and energy storage in China and around the world. Several sectors stood out this year as being of particular importance. One of the biggest stories of the year is the rising demand for electricity, driven in part by AI data centers. Energy storage is a key piece of that puzzle because it can smooth out variations in the electricity

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xercises and activities, but many times — because storing data and doing something with it afterwards is resource intensive — it just gets erased, deleted, or dumped into a big storage area where it loses context over time, and people don’t even know it’s there. Jordan Schneider: Your point is that whatever autonomy we’re seeing in Ukraine today is because they’ve spent three years collecting and labeling data. On the one hand, we’re not fighting a war, so there’s less data to collect. But it’s

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ed about the capabilities and risks of powerful AI systems, and tries to empower and entertain people in the face of radical uncertainty.” As you look at the landscape of tech coverage and AI coverage, what’s missing from the reporting and commentary that you and Casey want to address with the new show? We just feel like it was high time that two men had a place to talk about AI. I've been saying this for years. I want more men talking about tech. Look, there is obviously no shortage of podcasts

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le. The models that do best also tend to be Arabic-native or Arabic-focused to begin with, which lines up with what we saw during our own ablations: general Arabic and dialect coverage is a necessary starting point, but it still takes targeted, dialect-specific work to close the rest of the gap, particularly on the hardest parts of Alyah, like poetry, heritage knowledge, and the language-and-dialect category itself. This also matches what came out of the Alyah benchmark release more broadly: eve

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III, scientific analysis: Factor analysis nominated IL-18 plus CD73 blockade as a cancer intervention. Wet-lab tests supported it in co-cultures, patient-derived organoids, tumor fragments and mice. Latent orbital modes also recovered Kepler’s law with a fitted slope of −1.4991 against the theoretical −1.5. How does JEPA-Anything compare with other world models? Sources: each project’s GitHub README, linked in the header row. JEPA-Anything checkpoint status from its Hugging Face card. Verified O

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round a small task-based workflow engine, so long-running, multiplayer agents can suspend and resume anywhere (Pi).Design: The core is 15K lines of TypeScript with SQLite/JSONL storage and runs on Bun or Cloudflare Durable Objects. Control is separated from execution environments (review); Effect.ts: The authors explain they skipped Effect because it does not provide durability (Zechner); Agent memory: Cognition launched Devin “Dreaming,” which prunes and links a memory graph overnight. It is op

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程的参与。但若能与经济社会的多样发展需求相结合，即使是目前的自动化智能技术同样也能起到提高而非取代劳动力的作用。UNCTAD报告记录了北京师范大学、华南农业大学等利用开源数据集、深度神经网络技术，结合农户的生产经验，开发出提高农作物收成预测精准度的智能产品。在企业应用端，目前大语言模型还难以形成精准、专业化的输出。一些企业探索出“基础模型+检索增强生成技术（RAG）”的应用方法，一方面突破了大模型难以本地化部署的性能限制，另一方面更加强调对于工程师和熟练工人的经验加以结构化处理的重要性。这些案例利用智能技术辅助劳动生产过程，使人工智能先进技术为解决具体的发展问题服务。 然而，少数科技巨头主导了人工智能的创新发展前沿，使得全世界技术精英几乎都主要围绕着既定的主流路线实施探索，很少主动结合经济社会多样发展需求。甚至有科技精英宣称可以通过结合学习模式与神经符号模型来超越各类大语言模型，突破“波兰尼悖论”，从而彻底取代人类劳动。[17] 少数科技巨头之所以能主导人工智能创新方向，与人工智能的创新特性有关。人工智能的基础研究与应用研究之间的边界非常模糊，相比于大学科研以及政府支持的其他实验室研究

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agent apps, too. How Agent Controller works Back in June, we shipped it as Harness. The name made sense at first, but then we thought: if Mastra already provides tools, memory, storage, and the runtime around an agent, isn't Mastra itself a harness? So we renamed it to AgentController to better reflect what it does. How we use it We built Agent Controller by extracting the best parts out of Mastra Code, and then we adjusted it to build the Factory on top of it. The two products use it in differe

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Booz Allen’s security assessments run for a fixed period of time, and engineers can read only so much source code, configuration, and compiled software before they end. Wider coverage alone wouldn't close that gap, because real attacks usually work by chaining small weaknesses across code, configuration, identity, and deployment settings, and tracing those connections by hand across a large software portfolio is slow even for a skilled operator. Booz Allen used Claude Mythos Preview to review f

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like Pi provides tools for an LLM to call, it does so by supplying some tool definitions which then translate into some token structure on the server side. Whether a model is encouraged to call a tool is the result of the reinforcement learning process. Something I wrote about before if you want to learn more. One of the reasons we strongly lean towards CLI and bash is because it allows easy composition of calls, and because the model also learns how the file system works when it’s trained. So w

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Facebook; Twitter; LinkedIn; Mail Modern search and retrieval augmented generation (RAG) applications increasingly need to work across diverse content types, from technical documentation and source code to images, video clips, and audio recordings. The challenge is finding models that deliver strong retrieval accuracy while maintaining low latency across all these formats without requiring massive compute infrastructure to run and index. EmbeddingGemma 2 is designed to provide a single, compact

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nd network levels, see the cloud environments docs. LOCAL OR CLOUD? Cloud sessions don't replace local ones, and most people use both. The table shows where they differ, and the paragraphs after it say when each one fits. Stay local when the task needs something only your machine has. That covers a database with real local data, a service you reach over VPN, a GPU, a phone simulator, or hardware on your desk. Stay local too for tight visual loops where you want to see each change in your own bro

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