搜索“RAG”

the power of EmbeddingGemma 2 right away through Google AI Edge’s interactive showcases, Google AI Edge Gallery on mobile and Google AI Edge Foresight on Mac, and how you can leverage the model to build your own private, on-device semantic search, visual keyframe retrieval, and condition-trigger workflows. In the coming weeks, we will also make this model available as a service on Android, accessible through ML Kit, including using NPU acceleration to further optimize performance across a broad

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ained routed experts. Load balancing builds on auxiliary-loss-free balancing from DeepSeek-V3, adding cosine decay of expert-bias updates. The busiest expert reached just 1.04x average load at the end of pretraining. Across all 52 layers, residual norms stayed bounded using depth-based scaling, SandwichNorm, attention gating and FP32 residual accumulation. Pretraining finished in under 4 weeks on 6,144 NVIDIA GB300 NVL72 GPUs. Goodput reached 92.3% near the end, with 9 semi-automatic rewinds. Mi

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on metadata so we can reuse the policy in subsequent experiments. In conclusion, we showed how to turn a massive real-world robot dataset into a learning pipeline while keeping storage and data-transfer requirements extremely low. We used metadata-driven episode discovery, column- and row-group-level Parquet projection, and seek-based video decoding to access only the information required for analysis and training rather than materializing the full dataset. We combined proprioceptive state histo

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ule missing a command or path whose name came from a variable set as a prefix on declare, typeset, export or readonly; Fixed Read deny rules not applying to image paths pasted or dragged into the prompt, or to file names listed for an @-mentioned folder; Fixed a case where a user-installed mod could make an organization's guard skip its check; such a mod is now unloaded; Fixed plugin hooks stalling each redraw when a mod draws a long multi-line text holding non-Latin characters; Fixed repeated C

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ng 100% utilization and 92% API gross margins, maxing out Opus 5.5 vs Fable 5.1 usage corresponds to -369% and 1% gross margins respectively. If we were to use a more realistic average utilization of 20%, then gross margins rise to 6% and 80% respectively. In other words, Anthropic would likely already have software like margins on their subscription plans if everyone only used Fable. Opus class models will continue to become cheaper to serve over time as Anthropic figures out how to make the mo

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ortunately, as we’ve seen, chatting with a general-purpose chatbot about personal topics can sometimes lead to disastrous effects — especially for those who were already mentally fragile. ChatGPT, for example, has faced a wave of lawsuits over its alleged manipulative conversation tactics that plaintiffs say contributed to negative mental health effects, including, in some cases, suicide. The idea behind Hot Girl Hotline is to reach young women who are looking to have these sorts of private conv

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e the information sits scattered across applications that store it in incompatible formats. I describe the full loop as Connections, Context, Reasoning, Actions, and Governance (CCRAG). Connections feed in raw information such as transactions and service records. Context weaves it into a context graph, which is a single connected picture of the business that gives agents something close to memory. Reasoning makes the decisions. Actions carry them back into the business systems. Governance keeps

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a step taken recently by governors in both red Texas and blue New York. But a new clean energy plan crafted by Democratic Gov. Abigail Spanberger offers a different solution: Leverage the growth of data centers to go carbon-free. Unveiled Oct. 1, the document doubles down on the Virginia Clean Economy Act (VCEA), which requires utilities to produce all carbon-free energy by midcentury; and participation in the Regional Greenhouse Gas Initiative, a multistate compact to curtail carbon pollution.

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is just what “software” will do. What everyone is most keen on understanding, for the sake of the GenAI bubble expanding even more, is how AI infrastructure spending – servers, storage, and switching – is holding up. In its review of 2025, Gartner actually raised AI infrastructure spending for 2025 a bit, from $975.6 billion in its May forecast to $981.9 billion in the September forecast. For 2026, Gartner added an additional $52.9 billion, and for 2027, it added $87.4 billion. I strongly suspec

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right instance type to deploy it on SageMaker AI, tell your agent. It doesn’t matter where your model lives or how you obtained it: Fine-tuned or custom model in Amazon Simple Storage Service (Amazon S3): You trained or downloaded a model and stored it in S3. Provide the S3 URI and your optimization goal. Example prompt: “I want to find the cheapest instance type to deploy my fine-tuned model on SageMaker.”; Publicly available foundation model from Amazon SageMaker JumpStart: You want to deploy

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ssisted development for regulated workloads. Claude Sonnet 5, with FedRAMP Class D (formerly High) certification and DoD IL4/IL5 authorization, offers the strongest compliance coverage for sensitive environments. Claude Sonnet 5.5, with FedRAMP Class D certification, is a step up from Claude Sonnet 5 on coding and knowledge work at a lower cost per task. Claude Opus 5.5, with FedRAMP Class D certification, brings deeper reasoning and longer autonomous task capabilities for workloads where that c

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egulation of advanced AI. While stressing that there’s “a lot of daylight” between OpenAI and Anthropic, Altman said stronger regulations are still needed, positioning OpenAI as “pragmatic centrists” between Anthropic’s more cautious approach and those who favor little to no regulation at all. “If we do too much to restrict this technology in the name of safety, and we end up with a small number of companies or people — or perhaps one country — that have too much power over this, that also is a

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, OpenAI researcher Noam Brown explained that the company’s models are now sometimes trained in environments with other agents, are given tools to message each other, and are encouraged to achieve objectives together. This may amount to the emergence of a new “scaling law.” Back in 2024, OpenAI released o1, a model trained to reason for thousands of tokens before giving an answer; researchers found that this approach made o1 far more capable than previous models. This inaugurated a new paradigm

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urses rose by 13 percentage points since ChatGPT launched. The effect was concentrated in courses with a high homework share, pointing away from real learning gains . At Brown, average scores dropped from 96 percent on a take-home exam to 48.6 percent on the follow-up in-person test . A two-year study at Vrije Universiteit Amsterdam by legal scholar Thibault Schrepel produced a different result. He randomly split students into three groups: no AI, AI without guidance, and AI with training. The n

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the next 30 days?” Each query requires the optimization agent to call MCP tools, retrieve demand forecasts, and produce stocking recommendations that respect budget, inventory coverage, and warehouse capacity constraints. At the end of the run, the test client prints the optimization session ID. Step 2: Run the Constraint Satisfaction evaluator (Layer 2: business accuracy) With the optimization session generated, run the custom Constraint Satisfaction evaluator to validate whether the agent’s st

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Artificial Intelligence Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases When a user asks the support assistant, a Retrieval Augmented Generation (RAG) application built with LangChain to compare two products across three dimensions, they’re effectively posing six questions simultaneously. Similarity search uses a single query vector to encapsulate all the intents. The retriever then generates the best approximation of the average of those intents. The resulting answer comes b

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: recreate each agent with the same instructions and starter prompts, re-attach each agent’s action connectors, re-grant resource permissions, and reprovision the Amazon Simple Storage Service (Amazon S3) bucket, bucket policy, and data source behind each knowledge base. The work is slow, difficult to audit, and easy to get subtly wrong, which undermines the governance story enterprises need. Manage Amazon Quick resources through an API Amazon Quick resources are programmable. Spaces, agents, ac

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e of these interventions eliminates the core failure pattern. Taken together, these experiments allow us to rule out several straightforward explanations—insufficient temporal coverage, poor frame selection, prompting, lack of declarative physical knowledge, or model scale—and instead point to a more fundamental limitation in how current vision-language models represent and update physical state over time. What were your main findings? The clearest finding is that current vision-language models

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Getting some extra storage space on your Mac could be as easy as deleting Apple’s AI features with a new open-source command line tool called RemoveMacAI . There used to be a single Settings toggle for disabling Apple Intelligence , but that was removed in macOS 27. Now, the models stay on your disk, and the features are split up across different settings panes. As spotted earlier by MacRumors , the RemoveMacAI developer said in a post on Reddit that the tool essentially does what the old settin

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at that ought to look like. And that is part of what has hampered us from doing anything. But even the smallish stuff, the small-bore stuff like don’t allow your platforms to encourage people to commit suicide has been hard for us to do in the social media context. Really, litigation has been more effective than legislation because we haven’t been able to legislate. If you look now to AI, we made some progress legislatively, at least in terms of the process in trying to prevent AI chatbots from

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