The shipping industry is one of the strongest anthropogenic emitters of $\text{NO}_\text{x}$ -- substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a ship, which makes global and continuous emission monitoring impossible. A promising approach is the application of remote sensing. Studies showed that some of the $\text{NO}_\text{2}$ plumes from individual ships can visually be distinguished using the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/S5P). To deploy a remote sensing-based global emission monitoring system, an automated procedure for the estimation of $\text{NO}_\text{2}$ emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data as well as the absence of ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of $\text{NO}_\text{2}$ plumes produced by seagoing ships using supervised machine learning on TROPOMI/S5P data. We show that the proposed approach leads to a more than a 20\% increase in the average precision score in comparison to the methods used in previous studies and results in a high correlation of 0.834 with the theoretically derived ship emission proxy. This work is a crucial step toward the development of an automated procedure for global ship emission monitoring using remote sensing data.

As AI advances, could it replace your travel agent? To investigate just how effective a holiday planner AI can be, MailOnline Travel asked two chatbots - ChatGPT, created by California AI firm OpenAI, and Google's Bard - to plan a variety of trips. Scroll down to see the answers the chatbots provided, from hotel recommendations in Iraq to advice on planning budget sun holidays, honeymoons and stag weekends away. For a budget break in the sun, Bard recommended jetting off to Bulgaria, where it says that you can find a week-long all-inclusive holiday'for as little as £200'. MailOnline Travel asked ChatGPT and Google's Bard to plan a variety of holidays.

Transformer networks have revolutionized NLP representation learning since they were introduced. Though a great effort has been made to explain the representation in transformers, it is widely recognized that our understanding is not sufficient. One important reason is that there lack enough visualization tools for detailed analysis. In this paper, we propose to use dictionary learning to open up these "black boxes" as linear superpositions of transformer factors. Through visualization, we demonstrate the hierarchical semantic structures captured by the transformer factors, e.g., word-level polysemy disambiguation, sentence-level pattern formation, and long-range dependency. While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights. We hope this visualization tool can bring further knowledge and a better understanding of how transformer networks work. The code is available at https://github.com/zeyuyun1/TransformerVis

Sea surface temperature (SST) forecasts help with managing the marine ecosystem and the aquaculture impacted by anthropogenic climate change. Numerical dynamical models are resource intensive for SST forecasts; machine learning (ML) models could reduce high computational requirements and have been in the focus of the research community recently. ML models normally require a large amount of data for training. Environmental data are collected on regularly-spaced grids, so early work mainly used grid-based deep learning (DL) for prediction. However, both grid data and the corresponding DL approaches have inherent problems. As geometric DL has emerged, graphs as a more generalized data structure and graph neural networks (GNNs) have been introduced to the spatiotemporal domains. In this work, we preliminarily explored graph re-sampling and GNNs for global SST forecasts, and GNNs show better one month ahead SST prediction than the persistence model in most oceans in terms of root mean square errors.

Futurists of the 1990s predicted that we'd be living underwater or riding flying cars by this point -- but now experts are warning of a much scarier future. Other predictions include making contact with aliens -- but whether or not that's a bad thing remains unknown. It's not all doom and gloom, though, with technology expected to have made the afterlife possible. AI'overlords' could turn everyone into serfs Right now, people are focused on AI potentially causing job losses - but the reality could be far worse. That's according to George Stakhov, chief strategy officer for the global ad agency DDB EMEA who created an AI tool named'The Uncreative Agency'.

Which specific insights are you interested in? I agree that IoT Analytics GmbH may process my information in accordance with its privacy statement to contact me and notify me of future research updates. Machine vision (MV) has the highest return on investment (ROI) and quickest amortization time of all Industry 4.0 technologies: For machine vision, this number is also among the lowest of all Industry 4.0 technologies. "In our latest project involving the implementation of an AI-based machine vision system for quality inspection of car assemblies, we achieved amortization in half a year." MV is the combination of different technologies and methods to automate the extraction of image information for providing operational guidance/key data for machines to execute a given task, in industrial and non-industrial settings.

Information and Communication Technology has been deemed a prime sector in Bangladesh. It is clear that the nation is developing technologically given that this sector has the ability to result in effective forums, the production of jobs, and a booming presence. To control the big data wave put forth through our every move in the internet world, and to make sense of the data that at first glance appears to be incoherent, there is an increasing demand for data scientists. According to the World Economic Forum's Future of Work Report 2020, data scientists will continue to be in great demand and have the fastest growth over the next ten years. Data Science Professionals are transformative figures in every organization out there.

In this paper, we focus on ensemble UQ techniques, either Bayesian Recent advances in deep neural networks have demonstrated or non-Bayesian, as this group is less explored compared to remarkable performance in a wide variety of applications, the solely Bayesian techniques. An ensemble model aggregates ranging from recommendation systems and improving user the predictions of multiple individual base-learners (or experience to natural language processing and speech recognition ensemble members), which in our case are neural networks (Abiodun et al. 2018). Nevertheless, blindly relying (NNs), and the empirical variance of their predictions gives on the outcome of these models can have harmful effects, an approximate measure of uncertainty. The idea behind this especially in high-stake domains such as healthcare heuristic is highly intuitive: the more the base-learners disagree and autonomous driving, as models can provide inaccurate on the outcome, the more uncertain they are. Therefore, predictions when queried in out-of-distribution data the goal of ensemble members is to have a great level points (Amodei et al. 2016). Consequently, correctly quantifying of disagreement (variability) in the areas where little or no the uncertainty of models' predictions is an admissible data is available, and to have a high level of agreement in mechanism to distinguish where a model can or cannot regions with abundance of data (Pearce et al. 2018).

Visibility underwater is challenging, and degrades as the distance between the subject and camera increases, making vision tasks in the forward-looking direction more difficult. We have collected underwater forward-looking stereo-vision and visual-inertial image sets in the Mediterranean and Red Sea. To our knowledge there are no other public datasets in the underwater environment acquired with this camera-sensor orientation published with ground-truth. These datasets are critical for the development of several underwater applications, including obstacle avoidance, visual odometry, 3D tracking, Simultaneous Localization and Mapping (SLAM) and depth estimation. The stereo datasets include synchronized stereo images in dynamic underwater environments with objects of known-size. The visual-inertial datasets contain monocular images and IMU measurements, aligned with millisecond resolution timestamps and objects of known size which were placed in the scene. Both sensor configurations allow for scale estimation, with the calibrated baseline in the stereo setup and the IMU in the visual-inertial setup. Ground truth depth maps were created offline for both dataset types using photogrammetry. The ground truth is validated with multiple known measurements placed throughout the imaged environment. There are 5 stereo and 8 visual-inertial datasets in total, each containing thousands of images, with a range of different underwater visibility and ambient light conditions, natural and man-made structures and dynamic camera motions. The forward-looking orientation of the camera makes these datasets unique and ideal for testing underwater obstacle-avoidance algorithms and for navigation close to the seafloor in dynamic environments. With our datasets, we hope to encourage the advancement of autonomous functionality for underwater vehicles in dynamic and/or shallow water environments.

More than one hundred benchmarks have been developed to test the commonsense knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems. However, these benchmarks are often flawed and many aspects of common sense remain untested. Consequently, we do not currently have any reliable way of measuring to what extent existing AI systems have achieved these abilities. This paper surveys the development and uses of AI commonsense benchmarks. We discuss the nature of common sense; the role of common sense in AI; the goals served by constructing commonsense benchmarks; and desirable features of commonsense benchmarks. We analyze the common flaws in benchmarks, and we argue that it is worthwhile to invest the work needed ensure that benchmark examples are consistently high quality. We survey the various methods of constructing commonsense benchmarks. We enumerate 139 commonsense benchmarks that have been developed: 102 text-based, 18 image-based, 12 video based, and 7 simulated physical environments. We discuss the gaps in the existing benchmarks and aspects of commonsense reasoning that are not addressed in any existing benchmark. We conclude with a number of recommendations for future development of commonsense AI benchmarks.