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2026-08-25

待翻譯:Democratizing institutional knowledge: Building an AI-powered knowledge management system with AWS

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how to build a customizable, smart-caching knowledge management system on AWS that captures and delivers institutional (tribal) knowledge through a voice-first AI avatar. The accelerator uses Amazon Bedrock Knowledge Bases for retrieval-augmented generation and deploys in hours with AWS CloudFormation.

AWS Machine Learning BlogAgent / 研究來源內容 · 翻譯待補全待翻譯:Democratizing institutional knowledge: Building an AI-powered knowledge management system with AWS
待翻譯:Mathematical Theories Could Be the Key to Explainable AI Systems

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Kodamai, an enterprise AI startup, is addressing the growing concerns around the explainability and governance of AI systems by applying mathematically grounded theories.

AI Business政策 / 創業融資來源內容 · 翻譯待補全待翻譯:Mathematical Theories Could Be the Key to Explainable AI Systems
待翻譯:UK to use Ukraine battlefield data to train AI to protect sensitive sites

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:London and Kyiv in deal to help stop protesters and hostile states targeting military bases and critical infrastructure AI models trained on Ukrainian battlefield data will be used to stop protesters and foreign states targeting UK defence sites, railways and energy plants under a deal struck between London and Kyiv. Private companies will also be given access to the vast trove of data from Ukraine’s Avengers AI lab to help build new systems, the first agreement of its kind in the UK. Continue reading...

The Guardian AI模型來源內容 · 翻譯待補全待翻譯:UK to use Ukraine battlefield data to train AI to protect sensitive sites
待翻譯:Generating scenarios for extreme events, without extreme data

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.

MIT News AI研究來源內容 · 翻譯待補全待翻譯:Generating scenarios for extreme events, without extreme data
待翻譯:Toyota Scales Enterprise AI with Deep Agents and LangSmith

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:See how Toyota North America uses Deep Agents and LangSmith to run 50+ production agents, cut delivery from 6 months to 4 days, and track AI ROI.

LangChain BlogAgent / 研究來源內容 · 翻譯待補全待翻譯:Toyota Scales Enterprise AI with Deep Agents and LangSmith
待翻譯:Physical AI’s moment has arrived – but moving from demo to deployment is the hard part. AWS wants to fix that

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Physical artificial intelligence is emerging as the next major phase of AI. These systems not only generate content or analyze data but also perceive, reason about and act in the physical world. The opportunity is massive, but so are the operational, data, latency and lifecycle-management challenges. That is why Amazon Web Services Inc. last month […] The post Physical AI’s moment has arrived – but moving from demo to deployment is the hard part. AWS wants to fix that appeared first on SiliconANGLE.

SiliconANGLE AIAgent來源內容 · 翻譯待補全待翻譯:Physical AI’s moment has arrived – but moving from demo to deployment is the hard part. AWS wants to fix that
待翻譯:Run, debug, and scale Databricks workloads from your local IDE

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The Databricks workspace is purposefully built for data analysis and data engineering. However...

Databricks BlogAgent來源內容 · 翻譯待補全待翻譯:Run, debug, and scale Databricks workloads from your local IDE
待翻譯:How Databricks Uses AI to Accelerate Incident Investigation

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:In our previous blog post, we shared how Databricks uses AI to debug thousands of...

Databricks BlogAgent來源內容 · 翻譯待補全待翻譯:How Databricks Uses AI to Accelerate Incident Investigation
待翻譯:Relational vs Non-Relational Database: Choosing the Right Data Store

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Choosing between relational and non-relational databases is one of the most consequential...

Databricks BlogAgent來源內容 · 翻譯待補全待翻譯:Relational vs Non-Relational Database: Choosing the Right Data Store
待翻譯:llm-anthropic 0.27

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯: Release: llm-anthropic 0.27 This release of the Anthropic plugin for LLM mainly provides compatibility with the recently released anthropic v1.0.0 Python library, which switches from httpx to httpx2. OpenAI made the same change in their v3.0.0 release two weeks ago. Anthropic provide this migration guide for upgrading to 1.0, so I prompted Fable 5 in Claude Code with: Upgrade to anthropic>=1 - read https://raw.githubusercontent.com/anthropics/anthropic-sdk-python/refs/heads/main/MIGRATION.md and get the tests passing Here's the resulting PR. Tags: python, httpx, llm, anthropic, claude

Simon Willison's Weblog模型 / 研究來源內容 · 翻譯待補全待翻譯:llm-anthropic 0.27
待翻譯:Agentic Resource Discovery (ARD): An open specification for agent discovery

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:AWS Agent Registry gives your organization a centralized, searchable catalog for agents, tools, and skills. It works with the open Agentic Resource Discovery (ARD) standard to enable cross-environment discovery and governance at scale.

AWS Machine Learning BlogAgent / 政策來源內容 · 翻譯待補全待翻譯:Agentic Resource Discovery (ARD): An open specification for agent discovery
待翻譯:Building a restaurant telephony AI host with Amazon Connect

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how to build a voice ordering system for restaurants that answers a phone call and takes an order end to end, with no app, no website, and no sign-in. It uses Amazon Connect for telephony, Amazon Connect Agentic Voice for real-time speech, an Amazon Connect AI agent for reasoning, and Amazon Bedrock AgentCore Gateway to reach backend tools through MCP.

AWS Machine Learning BlogAgent來源內容 · 翻譯待補全待翻譯:Building a restaurant telephony AI host with Amazon Connect

2026-08-24

待翻譯:Data Intelligence: Building Your Competitive Advantage in the Era of AI

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:To keep pace with modern business, data strategy is shifting toward more autonomous real-time systems that deliver intelligence at the moment decisions are made. Driven by agentic AI, modern data teams are moving beyond simply looking at what happened. Now they’re automating complex workflows that analyze what’s happening, anticipate what might happen next, and recommend […]

O'Reilly AI & ML RadarAgent / 研究來源內容 · 翻譯待補全待翻譯:Data Intelligence: Building Your Competitive Advantage in the Era of AI
待翻譯:AI-powered metadata correction and harmonization

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Metadata harmonization (standardizing labels, identifiers, and formats so datasets can work together) is still largely manual. This post shows how AI-powered metadata correction works in practice, covering two approaches, human-in-the-loop validation and autonomous agent-driven workflows, plus governance considerations for production deployment.

AWS Machine Learning BlogAgent / 研究來源內容 · 翻譯待補全待翻譯:AI-powered metadata correction and harmonization
待翻譯:Self-Driving Cars Could Someday Take Requests

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. The idea of letting a machine do the driving for you may put a lot of people off autonomous vehicles. But research could make it possible to backseat-drive an autonomous vehicle just as you might with a human driver. Self-driving cars carefully balance a host of parameters to ensure a smooth ride, including things like speed, acceleration, and the smoothness of turns. But human driving preferences can often vary depending on how much of a rush they’re in, whether they’re feeling carsick, or how busy the traffic is. These cars have a software component called the motion planner, which is responsible for choosing a safe and efficient path through traffic. The motion p…

IEEE Spectrum AI模型 / 研究 / 政策來源內容 · 翻譯待補全待翻譯:Self-Driving Cars Could Someday Take Requests
待翻譯:Waymo Develops Its Own Chip for Self-Driving

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The vendor said this chip is fast enough to handle real-time driving conditions.

AI Business晶片來源內容 · 翻譯待補全待翻譯:Waymo Develops Its Own Chip for Self-Driving
待翻譯:I dumped Firefox, but these 5 features finally won me back - here's why

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:It's been a bumpy road for Mozilla's browser, but as the dust of doubt settles, Firefox has once again become my default browser on all platforms.

ZDNet AI研究來源內容 · 翻譯待補全待翻譯:I dumped Firefox, but these 5 features finally won me back - here's why
待翻譯:How XPUs Meet a World-Class AI Factory

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators. Hyperscalers and AI-native companies building custom XPUs must consider […]

NVIDIA Blog晶片 / Agent來源內容 · 翻譯待補全待翻譯:How XPUs Meet a World-Class AI Factory
待翻譯:With Groq 3 LPX in Full Production, NVIDIA Extends Vera Rubin Inference for Agents

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The next era of AI inference won’t be defined by a single breakthrough chip, network or system. It’ll be defined by how every layer of the AI factory works together. That’s why NVIDIA is extending Vera Rubin NVL72 with fast token generation for agentic systems. Announced today, the NVIDIA Vera Rubin rack-scale system NVIDIA Groq […]

NVIDIA BlogAgent / 晶片來源內容 · 翻譯待補全待翻譯:With Groq 3 LPX in Full Production, NVIDIA Extends Vera Rubin Inference for Agents
待翻譯:Up to 30x More Work Per Watt: NVIDIA Vera Rubin NVL72 Sets a New Efficiency Standard for AI Agents

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:According to OpenRouter data, agentic AI workloads consume 15x more tokens than a simple chat request. Why? Consider what happens when an AI agent researches a company for an investment decision. The agent queries financial databases, searches news and filings, invokes a sub-agent to run peer comparisons and model valuations, then synthesizes everything into a […]

NVIDIA BlogAgent來源內容 · 翻譯待補全待翻譯:Up to 30x More Work Per Watt: NVIDIA Vera Rubin NVL72 Sets a New Efficiency Standard for AI Agents
待翻譯:Generalist AI Releases GEN-1.5: A Robot Foundation Model That Learns New Tasks From One 3–12 Second Demo

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Generalist AI has released GEN-1.5, a robot foundation model that learns a new physical task from a single demonstration. Drop 3–12 seconds of sensorimotor data into its 30-second context window, and the robot performs the task. No gradient updates, no fine-tuning, no task-specific programming. Across 10 diverse manipulation tasks, this one-shot in-context prompting averaged 59% […] The post Generalist AI Releases GEN-1.5: A Robot Foundation Model That Learns New Tasks From One 3–12 Second Demo appeared first on MarkTechPost.

MarkTechPost模型 / 機器人 / 研究來源內容 · 翻譯待補全待翻譯:Generalist AI Releases GEN-1.5: A Robot Foundation Model That Learns New Tasks From One 3–12 Second Demo