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Best practices to run inference on Amazon SageMaker HyperPod

Best practices to run inference on Amazon SageMaker HyperPod

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Deploying and scaling foundation models for generative AI inference presents challenges for organizations. Teams often struggle with complex infrastructure setup, unpredictable traffic patterns that lead to over-provisioning or performance bottlenecks, and the operational overhead of managing GPU resources efficiently. These pain points result in delayed time-to-market, suboptimal model performance, and inflated costs that can make AI initiatives unsustainable at scale. This post explores how Amazon SageMaker HyperPod addresses these challenges by providing a comprehensive solution for inference workloads. […]

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How Guidesly built AI-generated trip reports for outdoor guides on AWS

How Guidesly built AI-generated trip reports for outdoor guides on AWS

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This is guest post by David Lord, Taylor Lord, Shiva Prasad, Anup Banasavalli Hiriyanagowda, Nikhil Chandra from Guidesly. Guidesly is reshaping how outdoor recreation is booked, run, and experienced. Founded in 2019, it began as a way to connect anglers, hunters, divers, and outdoor recreation enthusiasts with trusted guides, dive shops, and charters. It has since grown into a vertical AI software as a service (SaaS) system serving the entire industry. With Guidesly Pro, outdoor professionals gain a […]

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Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization

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Large language models can be aligned with human preferences through offline reinforcement learning (RL) on small labeled datasets. While single-objective alignment is well-studied, many real-world applications demand the simultaneous optimization of multiple conflicting rewards, e.g. optimizing both catalytic activity and specificity in protein engineering, or helpfulness and harmlessness for chatbots. Prior work has largely relied on linear reward scalarization, but this approach provably fails to recover non-convex regions of the Pareto front. In this paper, instead of scalarizing […]

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Google introduces "Skills" in Chrome to make Gemini prompts instantly reusable

Google introduces “Skills” in Chrome to make Gemini prompts instantly reusable

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Chrome is the most popular browser in the world, and the competition is not even close. So the browser is a key part of Google’s efforts to get everyone using its AI tools. The company’s chatbot has already infused various parts of the Chrome UI, and you can even turn Gemini loose to control the browser. The latest AI addition to Chrome comes in the form of “Skills,” reusable prompts you can access while browsing with a single […]

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Physicists think they've resolved the proton size puzzle

Physicists think they’ve resolved the proton size puzzle

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There has been considerable debate among physicists over the last 15 years about conflicting measurements of the charge radius of a hydrogen atom’s proton—some confirming the predictions of our strongest theoretical models, others suggesting it was smaller than expected. The discrepancy hinted at possible exciting new physics. Now the debate seems to be winding down with the latest experimental measurements, described in two recent papers published in the journals Nature and Physical Review Letters, respectively. And the evidence […]

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Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks

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Deep neural networks are typically trained by uniformly sampling large datasets across epochs, despite evidence that not all samples contribute equally throughout learning. Recent work shows that progressively reducing the amount of training data can improve efficiency and generalization, but existing methods rely on fixed schedules that do not adapt during training. In this work, we propose Adaptive Data Dropout, a simple framework that dynamically adjusts the subset of training data based on performance feedback. Inspired by self-regulated […]

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Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version

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This paper studies continuous-time stochastic control problems whose controlled states are fully non-Markovian and depend on unknown model parameters. Such problems arise naturally in path-dependent stochastic differential equations, rough-volatility hedging, and systems driven by fractional Brownian motion. Building on the discrete skeleton approach developed in earlier work, we propose a Monte Carlo learning methodology for the associated embedded backward dynamic programming equation. Our main contribution is twofold. First, we construct explicit dominating training laws and Radon–Nikodym weights for […]

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NASA chose the right crew to launch a new era of human space exploration

NASA chose the right crew to launch a new era of human space exploration

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HOUSTON—Their mission is complete. The four people who flew beyond the Moon on NASA’s Artemis II mission are back home in Houston with their families. But the lessons from Artemis II are just beginning to be told. There are tangible, objective takeaways from the nine-day mission. How did NASA’s Space Launch System rocket perform? Nearly perfectly. Was the Orion spacecraft up to the job of flying to the Moon and back? Absolutely. Will engineers need to make any […]

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Frequency-aware Decomposition Learning for Sensorless Wrench Forecasting on a Vibration-rich Hydraulic Manipulator

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Force and torque (F/T) sensing is critical for robot-environment interaction, but physical F/T sensors impose constraints in size, cost, and fragility. To mitigate this, recent studies have estimated force/wrench sensorlessly from robot internal states. While existing methods generally target relatively slow interactions, tasks involving rapid interactions, such as grinding, can induce task-critical high-frequency vibrations, and estimation in such robotic settings remains underexplored. To address this gap, we propose a Frequency-aware Decomposition Network (FDN) for short-term forecasting of vibration-rich […]

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Google will begin punishing sites for back button hijacking in June

Google will begin punishing sites for back button hijacking in June

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So you thought you’d just read that webpage and then go back to the previous page? A bold assumption. All too often, clicking the back button in your browser doesn’t actually take you back. It’s called back button hijacking, and Google has thus far tolerated it. That ends in June, when the company will designate it a “malicious practice,” and any site continuing to do it will face consequences. Back button hijacking is a way of wringing more […]

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