Breakthroughs, discoveries, and DIY tips sent every weekday. It is in these waters that marine mammal ecologist Ari Friedlaender shuts off the inflatable boat's engine and waits. This is the edge of the world--remote, hostile, and stunningly alive. Beneath the hull, the dark sea churns with wonder abound. A humpback whale (Megaptera novaeangliae) emerges, slow, deliberate, and gentle in its curious demeanor, casting a ripple across the surface.

Inverse Reinforcement Learning (IRL) presents a powerful paradigm for learning complex robotic tasks from human demonstrations. However, most approaches make the assumption that expert demonstrations are available, which is often not the case. Those that allow for suboptimality in the demonstrations are not designed for long-horizon goals or adversarial tasks. Many desirable robot capabilities fall into one or both of these categories, thus highlighting a critical shortcoming in the ability of IRL to produce field-ready robotic agents. We introduce Sample-efficient Preference-based inverse reinforcement learning for Long-horizon Adversarial tasks from Suboptimal Hierarchical demonstrations (SPLASH), which advances the state-of-the-art in learning from suboptimal demonstrations to long-horizon and adversarial settings. We empirically validate SPLASH on a maritime capture-the-flag task in simulation, and demonstrate real-world applicability with sim-to-real translation experiments on autonomous unmanned surface vehicles. We show that our proposed methods allow SPLASH to significantly outperform the state-of-the-art in reward learning from suboptimal demonstrations.

Most of the long and thin smart floor lamps we've tried are all about casting bold splashes of color on the wall, perfect for setting a mood but not must help when it comes to reading, dining, or getting something done. But with its new line of Matter-enabled lamps, Govee hopes to bring the dazzle without forgetting the productivity. Govee already has several floor lamps in its portfolio, including two that we've reviewed, the Govee Floor Lamp Pro and the Floor Lamp 2. We admired both lamps, which have long, thin, stick-light designs that cast multicolored and even animated light on your walls. But while both lamps can serve up eye-catching color scenes, they're not really designed for illuminating your reading nook or dining table. With its trio of new floor lamps, Govee is trying something different.

With the rapid advancement of text-conditioned Video Generation Models (VGMs), the quality of generated videos has significantly improved, bringing these models closer to functioning as ``*world simulators*'' and making real-world-level video generation more accessible and cost-effective. However, the generated videos often contain factual inaccuracies and lack understanding of fundamental physical laws. While some previous studies have highlighted this issue in limited domains through manual analysis, a comprehensive solution has not yet been established, primarily due to the absence of a generalized, automated approach for modeling and assessing the causal reasoning of these models across diverse scenarios. To address this gap, we propose VACT: an **automated** framework for modeling, evaluating, and measuring the causal understanding of VGMs in real-world scenarios. By combining causal analysis techniques with a carefully designed large language model assistant, our system can assess the causal behavior of models in various contexts without human annotation, which offers strong generalization and scalability. Additionally, we introduce multi-level causal evaluation metrics to provide a detailed analysis of the causal performance of VGMs. As a demonstration, we use our framework to benchmark several prevailing VGMs, offering insight into their causal reasoning capabilities. Our work lays the foundation for systematically addressing the causal understanding deficiencies in VGMs and contributes to advancing their reliability and real-world applicability.

Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. Annotated training data and code: https://github.com/laurenok24/NSAQA.

Surfers, swimmers and beachgoers face a hidden danger in the ocean: rip currents. These narrow channels of water can flow away from the shore at speeds up to 2.5 meters per second, making them one of the biggest safety risks for those enjoying the ocean. To help keep beachgoers safe, Christo Rautenbach, a coastal and estuarine physical processes scientist, has teamed up with the National Institute of Water and Atmospheric Research in New Zealand to develop a real-time rip current identification tool using deep learning. On this episode of the NVIDIA AI Podcast, host Noah Kravitz interviews Rautenbach about how AI can be used to identify rip currents and the potential for the tool to be used globally to help reduce the number of fatalities caused by rip currents. Developed in collaboration with Surf Lifesaving New Zealand, the rip current identification tool has achieved a detection rate of roughly 90% in trials.

As the global economy becomes more correlated to the internet of things, artificial intelligence (AI) stocks are becoming increasingly prominent, moving itself further to the front of tech stocks in 2022. One particular stock which has gained significant traction in the past few months, in particular, is BrainChip Holdings (ASX:BRN), which has won over global support as a result of its AI, inspired by the human brain. What is BrainChip and why is it creating a buzz? Recently, BrainChip Holdings made waves in the tech space when it was showcased in its latest collaboration with Mercedes Benz, which will use a microchip developed by the company in its latest electric vehicle (EV) – The Vision EQXX. The new EV, slated for release later this year, claims to be able to travel 1000km on just one charge though its uses of BrainChip's neural processors.

The View by Makr Shakr rooftop brings a new vibe in the metropolis of Milan that created the social ritual of aperitivo, introducing a new concept in the local drinking culture. Located on the famous fashion capital square – Piazza del Duomo – our flagship robotic bar is the perfect venue to enjoy robotic-made drinks, with a view.

We consider sparse estimation of a class of high-dimensional spatio-temporal models. Unlike classical spatial autoregressive models, we do not rely on a predetermined spatial interaction matrix. Instead, under the assumption of sparsity, we estimate the relationships governing both the spatial and temporal dependence in a fully data-driven way by penalizing a set of Yule-Walker equations. While this regularization can be left unstructured, we also propose a customized form of shrinkage to further exploit diagonally structured forms of sparsity that follow intuitively when observations originate from spatial grids such as satellite images. We derive finite sample error bounds for this estimator, as well estimation consistency in an asymptotic framework wherein the sample size and the number of spatial units diverge jointly. A simulation exercise shows strong finite sample performance compared to competing procedures. As an empirical application, we model satellite measured NO2 concentrations in London. Our approach delivers forecast improvements over a competitive benchmark and we discover evidence for strong spatial interactions between sub-regions.