AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Only 15% of US-based organizations have reached scaled, orchestrated, multi-agent adoption, according to the latest Deloitte research.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Autonomous Computer 2 From $26,100 BuildBuild: Complete machine · 2× RTX 5090 Complete machine · 2× RTX 5090 Complete machine · 2× RTX PRO 6000 DIY kit · Case and PCIe 5.0 risers Memory 64 GB 128 GB 192 GB 256 GB Storag…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Verify 837P claims against clinical evidence Product identity PrismClaim is Zero-trust 837P professional claim verification middleware. Insight IT Solutions LLC (Insight ITS) makes it. Category: Research — 837P claim ve…

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AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.

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AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Kodamai, an enterprise AI startup, is addressing the growing concerns around the explainability and governance of AI systems by applying mathematically grounded theories.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The Blink Outdoor 2K+ security camera and Blink Battery Doorbell 2K+ are on sale in this bundle deal, but not for long.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Fairphone is finally selling its latest model in the US. The $649 handset has 11 swappable parts, and a replacement battery is only $40.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：You can now code your own mini games and 'gizmos' with no experience. Creating is just half the fun.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The Databricks workspace is purposefully built for data analysis and data engineering. However...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：In our previous blog post, we shared how Databricks uses AI to debug thousands of...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Choosing between relational and non-relational databases is one of the most consequential...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Transactional Vs Analytical Database: Choosing OLTP, OLAP, or HybridTransactional...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<p><strong>Release:</strong> <a href="https://github.com/simonw/llm-anthropic/releases/tag/0.27">llm-anthropic 0.27</a></p> <p>This release of the Anthropic plugin for <a href="https://llm.datasette.io/">LLM</a> mainly provides compatibility with the recently released <a href="https://github.com/anthropics/anthropic-sdk-python/releases/tag/v1.0.0">anthropic v1.0.0</a> Python library, which switches from <code>httpx</code> to <a href="https://github.com/pydantic/httpx2">httpx2</a>. OpenAI made the same change in their <a href="https://github.com/openai/openai-python/releases/tag/v3.0.0">v3.0.0 release</a> two weeks ago.</p> <p>Anthropic provide this <a href="https://github.com/anthropics/anthropic-sdk-python/blob/v1.0.0/MIGRATION.md">migration guide</a> for upgrading to 1.0, so I prompted Fable 5 in Claude Code with:</p> <blockquote> <p><code>Upgrade to anthropic&gt;=1 - read https://raw.githubusercontent.com/anthropics/anthropic-sdk-python/refs/heads/main/MIGRATION.md and get the tests passing</code></p> </blockquote> <p>Here's <a href="https://github.com/simonw/llm-anthropic/pull/84">the resulting PR</a>.</p> <p>Tags: <a href="https://simonwillison.net/tags/python">python</a>, <a href="https://simonwillison.net/tags/httpx">httpx</a>, <a href="https://simonwillison.net/tags/llm">llm</a>, <a href="https://simonwillison.net/tags/anthropic">anthropic</a>, <a href="https://simonwillison.net/tags/claude">claude</a></p>

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Training for a fall marathon? Snag one of these Garmin running watches we've tested at a discount.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 planner is normally tuned by engineers before the vehicles hit the road so that there’s little scope for passengers to adjust a vehicle’s driving style on the fly. But now researchers at the Delft University of Technology (TU Delft) in the Netherlands have developed a system that uses a large language model (LLM) to translate natural-language user requests such as “I am running late, go fast” into adjustments to a self-driving control system. The researchers posted their preprint on arXiv and are presenting the work at the IEEE Intelligent Transportation Systems Conference in September. LLMs Personalize Autonomous Driving The system doesn’t give users direct control over the vehicle’s driving decisions; it simply tunes the parameters of a safety-aware motion-planning algorithm, which helps to keep the vehicle’s behavior within safe bounds. And the system keeps the human in the loop by describing how it’s going to alter its behavior in nontechnical language, and by asking the passenger to confirm before making changes. When the system was tested in simulation, the researchers found it adjusted the speed and smoothness of driving in line with natural-language instructions. “The motion-planning problem is not only about reaching a place while avoiding collisions, it’s also how you do it,” says lead author Diego Martinez-Baselga, a postdoctoral researcher at TU Delft. “The motivation here is trying to make the way the autonomous car drives adaptable by end users easily, just by talking to the car.” Previous research has investigated the potential of using LLMs and video-language models (VLMs) to direct decision-making for self-driving vehicles, but the researchers deliberately targeted driving style instead. Using LLMs and VLMs to directly control vehicles faces several challenges, says Martinez-Baselga. These include relatively slow response times, which can make these models unsuitable for the fast-paced decision-making required in driving, and the fact that they can’t provide concrete performance guarantees in the way a deterministic motion planner can. Instead, the researchers used an LLM’s language and reasoning capabilities to translate fuzzy human preferences into something a vehicle’s motion planner can use. The system relies on a model predictive-path integral controller previously developed by the researchers, which identifies multiple paths the vehicle could take to reach its goal and then judges them on various criteria, including speed, steering angle, and collision probability. It then finds an optimal path that is a combination of the trajectories that scored best on those judging criteria. The team combined this with OpenAI’s GPT-4o-mini model to parse passengers’ natural-language suggestions and use them to tune how the controller chooses its path. The model is given the users’ prompt and a natural-language description of the scenario the vehicle is operating in. The description was handwritten by the researchers for the purposes of the study, but it could ultimately be provided directly by a car’s perception system, says Martinez-Baselga. The model doesn’t directly tweak the settings of the controller; it uses the prompt to rate the relative importance of the judging criteria the controller uses to assess trajectories. This rating is then used to adjust each criteria up or down either side of a safe baseline set by the researchers. So, if a user says they are feeling dizzy, the LLM will dial up parameters that encourage smooth steering and gentle acceleration to make the vehicle favor more sedate travel. Prior to making any changes, however, the model first presents the user with a natural-language description of the adjustments it plans to implement. The user can then sign off on the plan or make further suggestions. The system is also interactive, so the user can request further adjustments if the vehicle’s behavior doesn’t match expectations or the user‘s preferences change. Martinez-Baselga says this human-in-the-loop system allows the passenger to catch instances when the model misinterprets prompts. But it also helps deal with the inherent subjectivity of suggestions like “go faster” or the possibility that models don’t accurately describe changes they plan to make. In that case the passenger can simply follow up with additional prompts “as you would do if you were in a taxi or with a friend that is driving,” says Martinez-Baselga. The researchers tested the system in the popular self-driving simulator nuPlan in scenarios that involved merging onto a busy highway. Across eight different prompts, the system changed the controller’s parameters in ways matching user intent, with requests for a more comfortable ride dialing up smoothness and those indicating urgency leading to higher speeds. This isn’t the first time LLMs have been used to tune a self-driving car’s motion planner. Nicolas Baumann, a Ph.D. student at ETH Zurich in Switzerland, published research last year in which an LLM tweaked the parameters of a model racing-car controller, allowing the user to alter driving style but also give more concrete instructions like “reverse the car” or “maintain a specific speed.” The strength of the approach, says Baumann, is that separating the LLM from the main controller means that even if the model hallucinates, it can’t do anything dangerous. “You get the possibility of language interaction, but you can guarantee that it is going to be within the constraints of this classical controller, so you can bake in safety,” he says. However, setting these constraints requires considerable engineering work, he adds. And if you want provable safety, you need to go a step further, says Matthias Althoff, a professor of cyberphysical systems at the Technical University of Munich. His group built a system that gets an LLM to suggest driving decisions, but then uses a mathematical process to check them against traffic rules and predictions about the behavior of other road users. This makes it possible to verify their safety before committing to them, something the Delft paper doesn’t provide. “As with any LLM, it is not guaranteed that the result is correct,” says Althoff. “For that reason, we safeguard the decisions of the LLM in our works.”

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Summer may be almost over, but you can find solid Walmart deals on headphones, TVs, laptops, and more for Labor Day.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The vendor said this chip is fast enough to handle real-time driving conditions.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：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 […]