Valve's Steam Machine: Everything we know The company plans to offer rides to the public in Nashville sometime this year. Waymo has gotten a step closer to offering robotaxi rides to the public in Nashville, Tennessee. The company the city and making sure they can operate as fully autonomous rides before launching a paid service in the location. Waymo announced that it was planning to bring its robotaxis to Nashville in September 2025, with the intention opening up rides to the public sometime this year. The company has been testing its technology in Nashville since then, but it has yet say when it'll start accepting bookings for rides.

The global scarcity of GPUs necessitates more sophisticated strategies for Deep Learning jobs in shared cluster environments. Accurate estimation of how much GPU memory a job will require is fundamental to enabling advanced scheduling and GPU sharing, which helps prevent out-of-memory (OOM) errors and resource underutilization. However, existing estimation methods have limitations. Approaches relying on static analysis or historical data with machine learning often fail to accurately capture runtime dynamics. Furthermore, direct GPU analysis consumes scarce resources, and some techniques require intrusive code modifications. Thus, the key challenge lies in precisely estimating dynamic memory requirements, including memory allocator nuances, without consuming GPU resources and non-intrusive code changes. To address this challenge, we propose xMem, a novel framework that leverages CPU-only dynamic analysis to accurately estimate peak GPU memory requirements a priori. We conducted a thorough evaluation of xMem against state-of-the-art solutions using workloads from 25 different models, including architectures like Convolutional Neural Networks and Transformers. The analysis of 5209 runs, which includes ANOVA and Monte Carlo results, highlights xMem's benefits: it decreases the median relative error by 91% and significantly reduces the probability of estimation failure as safe OOM thresholds by 75%, meaning that the estimated value can often be used directly without causing OOM. Ultimately, these improvements lead to a 368% increase in memory conservation potential over current solutions.

Organizations increasingly adopt Retrieval-Augmented Generation (RAG) to enhance Large Language Models with enterprise-specific knowledge. However, current data quality (DQ) frameworks have been primarily developed for static datasets, and only inadequately address the dynamic, multi-stage nature of RAG systems. This study aims to develop DQ dimensions for this new type of AI-based systems. We conduct 16 semi-structured interviews with practitioners of leading IT service companies. Through a qualitative content analysis, we inductively derive 15 distinct DQ dimensions across the four processing stages of RAG systems: data extraction, data transformation, prompt & search, and generation. Our findings reveal that (1) new dimensions have to be added to traditional DQ frameworks to also cover RAG contexts; (2) these new dimensions are concentrated in early RAG steps, suggesting the need for front-loaded quality management strategies, and (3) DQ issues transform and propagate through the RAG pipeline, necessitating a dynamic, step-aware approach to quality management.

Effective prompt engineering is critical to realizing the promised productivity gains of large language models (LLMs) in knowledge-intensive tasks. Yet, many users struggle to craft prompts that yield high-quality outputs, limiting the practical benefits of LLMs. Existing approaches, such as prompt handbooks or automated optimization pipelines, either require substantial effort, expert knowledge, or lack interactive guidance. To address this gap, we design and evaluate PromptPilot, an interactive prompting assistant grounded in four empirically derived design objectives for LLM-enhanced prompt engineering. We conducted a randomized controlled experiment with 80 participants completing three realistic, work-related writing tasks. Participants supported by PromptPilot achieved significantly higher performance (median: 78.3 vs. 61.7; p = .045, d = 0.56), and reported enhanced efficiency, ease-of-use, and autonomy during interaction. These findings empirically validate the effectiveness of our proposed design objectives, establishing LLM-enhanced prompt engineering as a viable technique for improving human-AI collaboration.

Jimmy Kimmel's big TV comeback strangled as SEVENTY ABC affiliates refuse to air tonight's show Wall Street delivers clear verdict on Trump's Tylenol claims I was a devout Catholic... until I died. I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Secret Service foils'espionage' plot in NYC ahead of UN General Assembly that could have crashed Big Apple's phone network The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Sarah Ferguson sent Jeffrey Epstein fawning apology email'after he threatened to destroy her' in'Hannibal Lector-like' phone call The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Awkward moment Emmanuel Macron rings Trump for help after his motorcade is stopped by cops in New York... but ends up having to get out and walk Kate Middleton delivers a'mic drop' moment in dazzling gold dress identical to the late monarch - giving a glimpse of the Queen she plans to be William is urging his father to disown Fergie and Andrew over Epstein scandal... but King fears they could go rogue and values their loyalty Insiders speak out on Barack and Michelle Obama's secretive yacht vacation amid's**t show': 'They NEEDED this trip' Shake It Off! How Taylor Swift has ditched her Southern drawl in favour of a northern American accent, revealed In the 19 years since she released her first song, Taylor Swift's music been through several transitions, changing from country, to pop, to indie folk, and almost every genre in between. Now, a study has revealed how it's not just Swift's music that has evolved. Scientists from the University of Minnesota say the chart-topping singer's accent has also changed over time. In their study, the team analysed years of Swift's recorded interviews to track how her dialect has transformed.

We may earn revenue from the products available on this page and participate in affiliate programs. Let's face it: Your phone's built-in sound sucks, so you need a portable Bluetooth speaker. Sure, everything is relative, and those phone speakers are amazing compared to what, say, a 2005 flip phone sounded like. But do we really want to justify our tech based on when people published think-pieces on how texting was the new hotness? So while we can admit you can hear musical cues right out of your pocket, if you want to feel the actual emotional resonance that makes the music special, the speakers on even the best smartphone, the best tablet, the best laptop … ultimately suck. But the best portable Bluetooth speakers--from the compact Bose SoundLink Plus to the more substantial Brane X, for example--do not suck, so we're ready to help you select the right speaker for any situation. We test a lot of Bluetooth speakers throughout the year, giving us deep insight into what's on the marketplace and what's worth your money. Whether you're looking for something budget or audiophile, chances are we've heard at least one model from whatever brand you're considering. We combine these experiences with other users' impressions, then top it all off with extensive research on what you should be looking for: IP rating, frequency range, battery life, Bluetooth range … we've got you! This lets us find the perfect balance of specs and special features from a fairly dense pool of possibilities. From extreme durability to supreme connectivity, we've got you covered when it comes to the best portable Bluetooth speakers. Whether you're always on the go or simply need something to take to the front porch, these speakers will deliver quality sound without any cables or wires weighing you down. Why it made the cut: The Bose SoundLink Plus portable Bluetooth speaker is styled for motion, tuned for emotion, with high cost being the primary shortcoming. New for 2025, the 269 SoundLink Plus is built with a powder-coated steel grille and a shock-resistant chassis wrapped in color-matched silicone.

Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathematical and logical reasoning. However, these methods encounter challenges with complex planning tasks, primarily due to extended reasoning steps, diverse constraints, and the challenge of handling multiple distinct sub-tasks. To address these challenges, we propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning. The hypertree structure enables LLMs to engage in hierarchical thinking by flexibly employing the divide-and-conquer strategy, effectively breaking down intricate reasoning steps, accommodating diverse constraints, and managing multiple distinct sub-tasks in a well-organized manner. We further introduce an autonomous planning framework that completes the planning process by iteratively refining and expanding the hypertree-structured planning outlines. Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6 times performance improvement over o1-preview.

Federated Learning (FL) is a decentralized machine learning (ML) paradigm in which models are trained on private data across several devices called clients and combined at a single node called an aggregator rather than aggregating the data itself. Many organizations employ FL to have better privacy-aware ML-driven decision-making capabilities. However, organizations often operate independently rather than collaborate to enhance their FL capabilities due to the lack of an effective mechanism for collaboration. The challenge lies in balancing trust and resource efficiency. One approach relies on trusting a third-party aggregator to consolidate models from all organizations (multilevel FL), but this requires trusting an entity that may be biased or unreliable. Alternatively, organizations can bypass a third party by sharing their local models directly, which requires significant computational resources for validation. Both approaches reflect a fundamental trade-off between trust and resource constraints, with neither offering an ideal solution. In this work, we develop a trust-based cross-silo FL framework called UnifyFL, which uses decentralized orchestration and distributed storage. UnifyFL provides flexibility to the participating organizations and presents synchronous and asynchronous modes to handle stragglers. Our evaluation on a diverse testbed shows that UnifyFL achieves a performance comparable to the ideal multilevel centralized FL while allowing trust and optimal use of resources.

The role and use of race within health - related artificial intelligence and machine learning (AI/ML) models has sparked increasing attention and controversy. Despite the complexity and breadth of related issues, a robust and holistic framework to guide stakeholders in their examination and resolution remains lacking . This perspective provides a broad - based, systematic, and cross - cutting landscape analysis of race - related challenges, structured around the AI/ML lifecycle and framed through " p oints to c onsider " to support inquiry and decision - making. INTRODUCTION The role and use of the social construct of race within health - related artificial intelligence and machine learning (AI/ML) models has become a subject of increased attention and controversy. As noted in the National Academies recent report " Ending Unequal Treatment ", it is increasingly clear that race in all its complexity is a powerful predictor of unequal treatment and health care outcomes.

Recent advancements in AI models are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources. To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE's unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications.