The quantification of positively buoyant marine plastic debris is critical to understanding how concentrations of trash from across the world's ocean and identifying high concentration garbage hotspots in dire need of trash removal. Currently, the most common monitoring method to quantify floating plastic requires the use of a manta trawl. Techniques requiring manta trawls (or similar surface collection devices) utilize physical removal of marine plastic debris as the first step and then analyze collected samples as a second step. The need for physical removal before analysis incurs high costs and requires intensive labor preventing scalable deployment of a real-time marine plastic monitoring service across the entirety of Earth's ocean bodies. Without better monitoring and sampling methods, the total impact of plastic pollution on the environment as a whole, and details of impact within specific oceanic regions, will remain unknown. This study presents a highly scalable workflow that utilizes images captured within the epipelagic layer of the ocean as an input. It produces real-time quantification of marine plastic debris for accurate quantification and physical removal. The workflow includes creating and preprocessing a domain-specific dataset, building an object detection model utilizing a deep neural network, and evaluating the model's performance. YOLOv5-S was the best performing model, which operates at a Mean Average Precision (mAP) of 0.851 and an F1-Score of 0.89 while maintaining near-real-time speed.

Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of disturbances, and environmental conditions. Since state-of-the-art attitude estimators do not generalize well over these characteristics, their parameters must be tuned for the individual motion characteristics and circumstances. We propose RIANN, a real-time-capable neural network for robust IMU-based attitude estimation, which generalizes well across different motion dynamics, environments, and sampling rates, without the need for application-specific adaptations. We exploit two publicly available datasets for the method development and the training, and we add four completely different datasets for evaluation of the trained neural network in three different test scenarios with varying practical relevance. Results show that RIANN performs at least as well as state-of-the-art attitude estimation filters and outperforms them in several cases, even if the filter is tuned on the very same test dataset itself while RIANN has never seen data from that dataset, from the specific application, the same sensor hardware, or the same sampling frequency before. RIANN is expected to enable plug-and-play solutions in numerous applications, especially when accuracy is crucial but no ground-truth data is available for tuning or when motion and disturbance characteristics are uncertain. We made RIANN publicly available.

The application of AI technologies in four areas – agriculture, water, energy and transport – have the potential to increase global GDP by up to $5.2 trillion by 2030, according to a new report from Microsoft and Pricewaterhouse Coopers. That is an increase of 4.4% in global GDP over the next 11 years, relative to business as usual. At the same time, these technologies could reduce global greenhouse gas emissions by up to 4%. That is equivalent to the predicted 2030 annual emissions of Australia, Canada and Japan combined. This map shows where those changes could occur.

Here is first part of the guide on implementing ML and AI solutions into manufacturing company. Following guide can be applied to all types of factories and products, which generate structured data preferable stored in the databases. It will work best for high volume products that get tested and have measurable output properties e.g.: resistance, latency, frequency, torque, power, energy consumption, pressure, speed, vibration, strength, clearance, efficiency, timing, thrust and all possible numeric or categorical properties that can be measured and are important for final characteristic and production yield. In terms of name for that system it could be called: Automated Production Optimisation or simply Digital Twin of Process and/or product. As you can imagine following examples suggest it can be used for things such as: PCB, Jet and rocket engines, gearboxes, combustion engines and all other mechanical, electronic, pneumatic and hydraulic devices.

I have always disliked exaggerated claims of imminent scientific and technical breakthroughs, such as inexpensive fusion, cheap supersonic travel, and the terraforming of other planets. But I am fond of the simple devices that do so much of the fundamental work of modern civilization, particularly those that do so modestly--or even invisibly. No device fits this description better than a transformer. Non-engineers may be vaguely aware that such devices exist, but they have no idea how they work and how utterly indispensable they are for everyday life. If you buy something using links in our stories, we may earn a commission.

AI offers great potential to increase the adoption of renewable energy. Within two months, Omdena's AI community built an interactive map showing the top Nigerian regions for solar power installments. The solutions will provide helpful insights for the government and policy makers to take make decisions on where to allocate resources in the most effective way. Many communities are not connected to the national electricity grid altogether. Most of them work with environmentally devastating fossil fuel, which is expensive, unsustainable, noisy, and health-threatening.

As artificial systems (AI) get increasingly complex, they are being used to make forecasts – or rather generate predictive model results – in more and more areas of our lives. At the same time, concerns are on the rise about reliability, amid widening margins of error in elaborate AI predictions. Management science offers a set of tools that can make AI systems more trustworthy. The discipline that brings human decision-makers to the top of their game can also be applied to machines, according to Thomas G Dietterich, Professor Emeritus and Director of Intelligent Systems Research at Oregon State University. Human intuition still beats AI hands down in making judgment calls in a crisis. People – and especially those working in their areas of experience and expertise – are simply more trustworthy.

We've been hearing how "data is the new oil" for more than a decade now, and in certain sectors, it's a maxim that has more than panned out. From marketing and logistics to finance and product, decision-making is now dominated by data at all levels of most big private orgs (and if it isn't, I'd be getting a resume put together, stat). So it might be a something of a surprise to learn that data, which could transform how we respond to the increasingly deadly disasters that regularly plague us, has been all but absent from much of emergency response this past decade. Far from being a geyser of digital oil, disaster response agencies and private organizations alike have for years tried to swell the scope and scale of the data being inputted into disaster response, with relatively meager results. That's starting to change though, mostly thanks to the internet of things (IoT), and frontline crisis managers today increasingly have the data they need to make better decisions across the resilience, response and recovery cycle.

The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning. But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems? In this work, we address this question by comparing sparse agents who have to address the non-stationarity of the exploration-exploitation problem with supervised agents trained to imitate an expert. We show that feed-forward networks trained via reinforcement learning and imitation learning can be pruned to the same level of sparsity, suggesting that the distributional shift has a limited impact on the size of winning tickets. Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in both learning paradigms can be attributed to the identified mask rather than the weight initialization. The input layer mask selectively prunes entire input dimensions that turn out to be irrelevant for the task at hand. At a moderate level of sparsity the mask identified by iterative magnitude pruning yields minimal task-relevant representations, i.e., an interpretable inductive bias. Finally, we propose a simple initialization rescaling which promotes the robust identification of sparse task representations in low-dimensional control tasks.

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.