Sensor and control data of modern mechatronic systems are often available as heterogeneous time series with different sampling rates and value ranges. Suitable classification and regression methods from the field of supervised machine learning already exist for predictive tasks, for example in the context of condition monitoring, but their performance scales strongly with the number of labeled training data. Their provision is often associated with high effort in the form of person-hours or additional sensors. In this paper, we present a method for unsupervised feature extraction using autoencoder networks that specifically addresses the heterogeneous nature of the database and reduces the amount of labeled training data required compared to existing methods. Three public datasets of mechatronic systems from different application domains are used to validate the results.

Novelty detection is the task of recognizing samples that do not belong to the distribution of the target class. During training, the novelty class is absent, preventing the use of traditional classification approaches. Deep autoencoders have been widely used as a base of many unsupervised novelty detection methods. In particular, context autoencoders have been successful in the novelty detection task because of the more effective representations they learn by reconstructing original images from randomly masked images. However, a significant drawback of context autoencoders is that random masking fails to consistently cover important structures of the input image, leading to suboptimal representations - especially for the novelty detection task. In this paper, to optimize input masking, we have designed a framework consisting of two competing networks, a Mask Module and a Reconstructor. The Mask Module is a convolutional autoencoder that learns to generate optimal masks that cover the most important parts of images. Alternatively, the Reconstructor is a convolutional encoder-decoder that aims to reconstruct unperturbed images from masked images. The networks are trained in an adversarial manner in which the Mask Module generates masks that are applied to images given to the Reconstructor. In this way, the Mask Module seeks to maximize the reconstruction error that the Reconstructor is minimizing. When applied to novelty detection, the proposed approach learns semantically richer representations compared to context autoencoders and enhances novelty detection at test time through more optimal masking. Novelty detection experiments on the MNIST and CIFAR-10 image datasets demonstrate the proposed approach's superiority over cutting-edge methods. In a further experiment on the UCSD video dataset for novelty detection, the proposed approach achieves state-of-the-art results.

OpenAI is an artificial intelligence research lab dedicated to promoting and developing friendly AI for the benefit of humanity. OpenAI was founded in 2015 by Elon Musk and Sam Altman. This startup received a $ 1 billion investment from Microsoft in 2019. OpenAI was created in part out of the founders' fear of potential disaster as a result of the negligence and misuse of general-purpose AI. In the long term, the company pays special attention to fundamental advances in AI and its capabilities.

The amount of data being collected is drastically increasing day-by-day with lots of applications, tools, and online platforms booming in the present technological era. To handle and access this humongous data productively, it's necessary to develop valuable information extraction tools. One of the sub-areas that's demanding attention in the Information Extraction field is the fetching and accessing of data from tabular forms. To explain this in a subtle way, imagine you have lots of paperwork and documents where you would be using tables, and using the same, you would like to manipulate data. Conventionally, you can copy them manually (onto a paper) or load them into excel sheets. However, with table extraction, no sooner have you sent tables as pictures to the computer than it extracts all the information and stacks them into a neat document. This saves an ample of time and is less erroneous. As discussed in the previous section, tables are used frequently to represent data in a clean format. We can see them so often across several areas, from organizing our work by structuring data across tables to storing huge assets of companies.

Researchers at the University of Barcelona have developed an open access, deep learning-based web app that will enable the detection and quantification of floating plastics in the sea with a reliability of over 80%. Floating sea macro-litter is a threat to the conservation of marine ecosystems worldwide. According to UNESCO, plastic debris causes the deaths of more than a million seabirds every year, as well as more than 100,000 marine mammals. Eroded fragments, known as micro-plastics, are now prevalent across the food chain. The largest density of floating litter is found in the great ocean gyres (systems of circular currents) with litter being caught and spun in these vast cycles.

According to OpenAI, more than 300 applications are using GPT-3, which is part of a field called natural language processing. An average of 4.5 billion words are written per day. Some say the quality of GPT-3's text is as good as that written by humans. What follows is GPT-3's response to topics in general investing. MarketWatch: "How to invest in cryptocurrencies by GPT-3."

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers, has paved the way for Artificial Intelligence (AI) and Big Data in support of smart manufacturing. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, Big data, Blockchain, 5G communications, in support of smart manufacturing and the dynamical processes in modern industries. In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and Big Data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and Big Data methods in smart industries with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. In a nutshell, we have explored the significance of AI and Big data towards Industry 4.0 applications through panoramic reviews and discussions. We believe, this work will provide a baseline for future research in the domain.

Air contamination in urban areas has risen consistently over the past few years. Due to expanding industrialization and increasing concentration of toxic gases in the climate, the air is getting more poisonous step by step at an alarming rate. Since the arrival of the Coronavirus pandemic, it is getting more critical to lessen air contamination to reduce its impact. The specialists and environmentalists are making a valiant effort to gauge air contamination levels. However, its genuinely unpredictable to mimic subatomic communication in the air, which brings about off base outcomes. There has been an ascent in using machine learning and deep learning models to foresee the results on time series data. This study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error. However, the LSTM outperforms all other models with reference to mean absolute percentage error.

We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop dataset creation: annotators seek to create examples that a target model will misclassify, but that another person will not. In this paper, we argue that Dynabench addresses a critical need in our community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios. With Dynabench, dataset creation, model development, and model assessment can directly inform each other, leading to more robust and informative benchmarks. We report on four initial NLP tasks, illustrating these concepts and highlighting the promise of the platform, and address potential objections to dynamic benchmarking as a new standard for the field.

We consider a power transmission system monitored with Phasor Measurement Units (PMUs) placed at significant, but not all, nodes of the system. Assuming that a sufficient number of distinct single-line faults, specifically pre-fault state and (not cleared) post-fault state, are recorded by the PMUs and are available for training, we, first, design a comprehensive sequence of Neural Networks (NNs) locating the faulty line. Performance of different NNs in the sequence, including Linear Regression, Feed-Forward NN, AlexNet, Graphical Convolutional NN, Neural Linear ODE and Neural Graph-based ODE, ordered according to the type and amount of the power flow physics involved, are compared for different levels of observability. Second, we build a sequence of advanced Power-System-Dynamics-Informed and Neural-ODE based Machine Learning schemes trained, given pre-fault state, to predict the post-fault state and also, in parallel, to estimate system parameters. Finally, third, and continuing to work with the first (fault localization) setting we design a (NN-based) algorithm which discovers optimal PMU placement.