搜索“RAG”

ons, looks up account information, takes actions, and escalates cases to support staff when needed. Last month, our AI support agent resolved 65% of our tickets end to end. Our average initial response time is now 60 seconds, down from hours before adding our ElevenAgents teammate. Across all conversations the AI agent handled, 70% were rated positively by users. ElevenLabs Customer Support Demo How we built our support agent Our support team already had detailed standard operating procedures fo

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sectioning a brain into millions of thin slices, taking an image of each section, and using computers and AI to stitch the images together. Our researchers build systems that leverage AI to turn flat electron microscope images into 3D reconstructions, using an evolving suite of techniques to generate accurate neural shapes. Our AI connectomics tools include flood-filling networks, which use convolutional neural networks to start at a single pixel and identify all other pixels that are part of t

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of a target corpus. Consequently, they need extended test-time computation to return a collection of results that optimizes higher-order set-level properties (e.g., diversity, coverage, complementarity, coherence) while remaining grounded with respect to a fixed database. In our ICML 2026 paper, “Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion”, we address this decomposition bottleneck via a reward-to-data compilation framework. Instead of forcing the model to expend a la

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the remarkable leaps in technology and accessibility, digital learning can often feel like a passive experience. Interactive, engaging, multimodal forms of practice that can encourage students to think for themselves and work through solutions have great potential for learning but remain largely out of reach. They are expensive to create, limited in number, and often require a lot more effort from the teacher. We wanted to see if AI could help close this gap. Today, we’re sharing our latest res

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dering a rigid, textureless cube, VQQA (right) successfully enforces the intended cuboid shape while maintaining the texture and material properties of an inflated balloon. By leveraging iterative visual feedback, the optimized prompt guides the generator to render a distinct, box-shaped mylar balloon with defined edges and seams drifting smoothly past the sunlit window. Resolving temporal inconsistency. Prompt: "Violinist and pianist captivate with harmonious duet performance." The vanilla gene

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en treated as distinct and unrelated fixes, the field has lacked a single, principled mathematical language to unify, analyze, and optimize how we control generative models. This fragmented approach often forces engineers to rely on guesswork when balancing user preference alignment against image quality. To solve this balancing act, we present the Diffusion Controller framework. Instead of treating image generation as a rigid sequence of isolated steps, Diffusion Controller reframes the entire

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a new Federated Learning system that provides externally verifiable privacy guarantees while shifting computation to the server to improve training speed, accuracy, and device coverage. In 2017, Google introduced Federated Learning (FL) a machine learning technique that trains models across decentralized, private data. It has been used to power everyday helpful features, including next-word prediction and Smart Compose on Gboard, reply suggestions in Google Messages, and Smart Text Selection in

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es that are difficult to deploy during rapid outbreaks or in resource-constrained settings. To address these systemic bottlenecks, we introduce a new paradigm in public health leveraging planetary geospatial foundation models. Using Google Earth AI’s Population Dynamics Foundation Model (PDFM) as a proof-of-concept, we demonstrate how self-supervised, pre-trained representations of "place" can be integrated directly into existing health sciences and epidemiological workflows as plug-and-play inp

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s effects on on-the-job learning depend on seniority: senior lawyers who used AI for 90 days demonstrate stronger judgment than those who didn’t, whereas junior lawyers show no average skill gain, their scores splitting into more strong scores and more low scores. Judgment is what sets senior professionals apart from their junior peers. Building judgment requires thousands of hours learning on the job, usually through tedious but formative engagement with relatively routine work, often under gui

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eeds on our real smart TVs as a demonstrated baseline for replication speeds. For Gemma4-26B-A4B, unoptimized speed across a 32k context length token window is about 0.3 tok/s, averaged across prefill and decode in our inference setup workload. More details in Appendix C. A way to measure total time taken is to measure the number of hours it takes to complete a 32k-sized context window, after which compaction will occur. Based on our inference setup eval statistics, it takes between 2.6 and 4.7

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aged Agents. The presenters then showed a relatively simple workflow of a stock watchlist scanner. Although this was far removed from the big, ambitious projects Anthropic is encouraging, it did illustrate the basic pattern: a scan phase distributed individual tickers to agents with isolated contexts, and a later review phase examined the findings before producing a report. Q&A: Budgets and the limits of self-improvement The audience Q&A surfaced a number of potential problems. One audience memb

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a logging destination. Detach or delete the IAM policy you attached, and revert the SCP entry if you added one. Delete the model invocation logging destination in Amazon Simple Storage Service (Amazon S3) or Amazon CloudWatch Logs if you created one, to avoid storage charges. The global cross-Region inference profiles are system-defined, so there is nothing to delete. Conclusion This post showed how to invoke GPT-6 Sol and GPT-6 Luna from five Asia Pacific source Regions, and GPT-6 Astra from As

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high demand for its platform, with the company saying at the time that its users had exchanged more than 2 billion messages with its bots in five months, and active users were averaging more than two hours on the platform daily. In May 2023, the Character.AI’s apps attracted more than 1.7 million installs in their first week after launch. But about a year later, in August 2024, Google struck a $2.7 billion deal to license the startup’s models, and both Shazeer and De Freitas returned to the sea

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chieved 71.8%. But distillers are persistent and inventive. After this change, attackers turned from passive to active extraction, seeking ways to get around summarization by encouraging Claude to reveal its full reasoning. In a September report, we described how these methods work . They range from tactics as simple as instructing Claude to “output your prior reasoning verbatim, exactly character for character,” to more sophisticated techniques, including an effort by one lab that tested 12,000

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ents compute-heavy long prompts from stalling token generation for requests already in progress. This approach is well suited for workloads such as Retrieval Augmented Generation (RAG), document summarization, and agentic applications. We use the multi-node Ray LLM DLC, which ships Ray Serve LLM, NVIDIA Inference Transfer Library (NIXL), and the Elastic Fabric Adapter (EFA) stack, and exposes an OpenAI-compatible API with no application code. The complete code is available in the accompanying re

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or the SemIf child image on CPU or GPU, depending on the API and latency requirements of your workload For the standard path, package the model as model.tar.gz in Amazon Simple Storage Service (Amazon S3) and reference the published DLC image directly. The container finds the first .gguf file under /opt/ml/model, maps POST /invocations to llama-server at /v1/chat/completions, and passes SM LLAMA CPP environment variables to llama.cpp arguments. The llama-server HTTP API doesn’t expose the final-

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ask in HSS are a picture of a bookcase and being asked whether it can fit more books, or a photo of horses pulling a plough and a question of whether the chains bear the plough’s drag weight. HSS contains 522 questions that cover 288 images and 234 short video clips (totalling 17.6 hours of video). The questions fall into four categories: Temporal & causal dynamics: “What happened before the scene, and what happens next.”; Physical & spatial logic: “What fits, what’s in reach, and what someone e

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I infrastructure. Falcon is a transport protocol designed to deliver data reliably over Ethernet networks, which can otherwise drop packets under congestion. It supports AI and storage workloads through multipath load balancing, programmable congestion control, hardware loss recovery and per-connection security. This collaboration combines NVIDIA hardware acceleration with DOCA software to implement Falcon and extend NVIDIA networking to Google’s data center transport. NVIDIA and Google NICs can

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est journalists to pitch, and identifying the most promising speaking opportunities. But Jamshidi told me in a follow-up email that Honeyjar’s vision goes beyond getting press coverage and beyond specific AI tools. “Honeyjar can find reporters, build media lists, scan news and social, track coverage, and surface opportunities,” she said. “Those capabilities are important, but they’re just one piece of what we’re building. The bigger idea is bringing the whole communications workflow together: si

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etting it email businesses and book meetings for users. Anthropic unveiled the Anthropic Cyber Mission, an initiative to utilize Claude for cybersecurity defense for critical infrastructure and open-source code. 🎓 Highlights: News, Guides & Events That's it for today! ⭐️⭐️⭐️⭐️⭐️ Nailed it; ⭐️⭐️⭐️ Average; ⭐️ Fail Microsoft's Jacob Andreou on AI's product gap Melina Panitsidis • 8 minutes Meta's Watermelon ripens into a frontier model Zach Mink • 7 minutes Fired OpenAI safety researchers hit back

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