Recent advancements in Generative AI offer promising capabilities for spatial analysis. Despite their potential, the integration of generative AI with established GIS platforms remains underexplored. In this study, we propose a framework for integrating LLMs directly into existing GIS platforms, using QGIS as an example. Our approach leverages the reasoning and programming capabilities of LLMs to autonomously generate spatial analysis workflows and code through an informed agent that has comprehensive documentation of key GIS tools and parameters. The implementation of this framework resulted in the development of a "GIS Copilot" that allows GIS users to interact with QGIS using natural language commands for spatial analysis. The GIS Copilot was evaluated with over 100 spatial analysis tasks with three complexity levels: basic tasks that require one GIS tool and typically involve one data layer to perform simple operations; intermediate tasks involving multi-step processes with multiple tools, guided by user instructions; and advanced tasks which involve multi-step processes that require multiple tools but not guided by user instructions, necessitating the agent to independently decide on and executes the necessary steps. The evaluation reveals that the GIS Copilot demonstrates strong potential in automating foundational GIS operations, with a high success rate in tool selection and code generation for basic and intermediate tasks, while challenges remain in achieving full autonomy for more complex tasks. This study contributes to the emerging vision of Autonomous GIS, providing a pathway for non-experts to engage with geospatial analysis with minimal prior expertise. While full autonomy is yet to be achieved, the GIS Copilot demonstrates significant potential for simplifying GIS workflows and enhancing decision-making processes.

Black Friday is usually a good time to restock on video games and gaming gear at a discount, and this year should be no exception. If you're looking to pad out your backlog, pick up a new console or refresh your desktop with new peripherals, we're rounding up the Black Friday gaming deals that are most worth your attention below. To be candid, the selection as of Wednesday morning is fairly light -- we expect tons more discounts to pop up Thursday and Friday, after Amazon, Best Buy, PlayStation and other retailers start their official Black Friday sales in earnest. That said, a number of noteworthy Switch, PS5 and Xbox games are already down to their lowest prices to date, while the Xbox Series X and a few well-reviewed accessories are cheaper than usual too. We've dug through reviews and used price history trackers to ensure each offer below is a genuine deal, and we'll update this post regularly over the next couple of weeks as additional deals become available.

Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems, including positive social impact, nor continue to advance AI research in a scientifically sound way. In this paper, I will show that for widely considered inference settings in complex social systems the train-test paradigm does not only lack a justification but is indeed invalid for any risk estimator, including counterfactual and causal estimators, with high probability. These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large language models, and neither na\"ive scaling nor limited benchmarks are suited to address this issue. I am illustrating these results via the widely used MovieLens benchmark and conclude by discussing the implications of these results for AI in social systems, including possible remedies such as participatory data curation and open science.

Robot vacuums can help automate a chore you may loathe doing yourself. And even if you don't mind vacuuming regularly, it's undeniable that it takes time out of your day that you could be using for other things. The Black Friday and Cyber Monday time period is a great time to look for one of these smart home gadgets because you can often find them for hundreds of dollars off their usual prices -- this year is no different. These are the best Black Friday robot vacuum deals you can get this year. Check back as we get close to Black Friday proper for all of the latest deals.

Shape assembly is a ubiquitous task in daily life, integral for constructing complex 3D structures like IKEA furniture. While significant progress has been made in developing autonomous agents for shape assembly, existing datasets have not yet tackled the 4D grounding of assembly instructions in videos, essential for a holistic understanding of assembly in 3D space over time. We introduce IKEA Video Manuals, a dataset that features 3D models of furniture parts, instructional manuals, assembly videos from the Internet, and most importantly, annotations of dense spatio-temporal alignments between these data modalities. To demonstrate the utility of IKEA Video Manuals, we present five applications essential for shape assembly: assembly plan generation, part-conditioned segmentation, part-conditioned pose estimation, video object segmentation, and furniture assembly based on instructional video manuals. For each application, we provide evaluation metrics and baseline methods. Through experiments on our annotated data, we highlight many challenges in grounding assembly instructions in videos to improve shape assembly, including handling occlusions, varying viewpoints, and extended assembly sequences.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

The enormous progress in the field of artificial intelligence (AI) enables retail companies to automate their processes and thus to save costs. Thereby, many AI-based automation approaches are based on machine learning and computer vision. The realization of such approaches requires high-quality training data. In this paper, we describe the creation process of an annotated dataset that contains 1,034 images of single food products, taken under studio conditions, annotated with 5 class labels and 30 object detection labels, which can be used for product recognition and classification tasks. We based all images and labels on standards presented by GS1, a global non-profit organisation. The objective of our work is to support the development of machine learning models in the retail domain and to provide a reference process for creating the necessary training data.

Black Friday is well-known as the day when retailers slash prices to kick off the holiday shopping season and clear out stock ahead of the new year. The rise of online shopping expanded the season to include Cyber Monday and the week that follows. You don't have to wait to carve the Thanksgiving turkey and watch the Cowboys lose to snag discounts because the best early Black Friday deals are live already. The WIRED team boasts decades of experience in product testing and a nose for sniffing out the best deals, backed by price tracking tools. For Black Friday, we cross-reference our buying guide recommendations with the latest sale prices to find the best early Black Friday deals on gadgets and gizmos worth owning.

Recent advancements in session-based recommendation models using deep learning techniques have demonstrated significant performance improvements. While they can enhance model sophistication and improve the relevance of recommendations, they also make it challenging to implement a scalable real-time solution. To addressing this challenge, we propose GRAINRec: a Graph and Attention Integrated session-based recommendation model that generates recommendations in real-time. Our scope of work is item recommendations in online retail where a session is defined as an ordered sequence of digital guest actions, such as page views or adds to cart. The proposed model generates recommendations by considering the importance of all items in the session together, letting us predict relevant recommendations dynamically as the session evolves. We also propose a heuristic approach to implement real-time inferencing that meets Target platform's service level agreement (SLA). The proposed architecture lets us predict relevant recommendations dynamically as the session evolves, rather than relying on pre-computed recommendations for each item. Evaluation results of the proposed model show an average improvement of 1.5% across all offline evaluation metrics. A/B tests done over a 2 week duration showed an increase of 10% in click through rate and 9% increase in attributable demand. Extensive ablation studies are also done to understand our model performance for different parameters.

The blackest of Fridays is nearly upon us and companies have already begun rolling out the deals to separate consumers from their bank accounts. The iRobot Roomba Essential Vac is on sale for just 140, which is a discount of 44 percent. The regular price is 250. The Essential Vac features a similar design to the iRobot Roomba 694, which topped our list of the best budget robot vacuums. This one includes a three-stage cleaning system that works on both carpet and hard floors.