It seemed as if the world was plunging from one crisis to another this year. Just as most countries broke free from the shackles of the pandemic, the horror of war returned to Europe, millions around the world suffered at the hands of extreme weather, and the double pain of energy shortages and inflation arrived. Here's our rundown of the best news to come out of 2022. More than one-fifth of all electricity in the US now comes from hydropower, wind, and solar, meaning that renewables have narrowly overtaken coal and nuclear, which make up 20 percent and 19 percent of the energy mix respectively. The only other year this was the case was 2020--but back then overall power generation was reduced due to the pandemic.

Market Statistics: The report provides market sizing and forecast across 7 major currencies - USD, EUR, JPY, GBP, AUD, CAD, and CHF. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2020 are considered as historical years, 2021 as the base year, 2022 as the estimated year, and years from 2023 to 2027 are considered as the forecast period. Market Segmentation & Coverage: This research report categorizes the Artificial Intelligence in Oil & Gas to forecast the revenues and analyze the trends in each of the following sub-markets: Based on Function, the market was studied across Field Services, Material Movement, Predictive Maintenance & Machine Inspection, Production Planning, Quality Control, and Reclamation. Based on Component, the market was studied across Hardware, Services, and Software.

Ethical AI will need careful planting of many ecosystems. Ethical AI has been a concern of AI leaders, and practitioners for many years, but finally it seems, global jurisdictions are starting to move from policy formulation and stakeholder engagement to putting some teeth into drafting legal bills or acts. Expect many new laws to pass in 2023, tightening up citizen privacy and creating risk frameworks and audit requirements for data bias, privacy and security risks. At the same time, regulators are going to have to evolve an entire global ecosystem to ensure AI audits are effectively conducted and many questions loom as to who will validate certifications for AI audit practices and will we over burden AI innovations like we have done in so many other regulated operating practices that the risk and costs of non-conformance inhibit's innovation and capital funding? Finding a balance will be key.

Given the wealth inequality worldwide, there is an urgent need to identify the mode of wealth exchange through which it arises. To address the research gap regarding models that combine equivalent exchange and redistribution, this study compares an equivalent market exchange with redistribution based on power centers and a nonequivalent exchange with mutual aid using the Polanyi, Graeber, and Karatani modes of exchange. Two new exchange models based on multi-agent interactions are reconstructed following an econophysics approach for evaluating the Gini index (inequality) and total exchange (economic flow). Exchange simulations indicate that the evaluation parameter of the total exchange divided by the Gini index can be expressed by the same saturated curvilinear approximate equation using the wealth transfer rate and time period of redistribution and the surplus contribution rate of the wealthy and the saving rate. However, considering the coercion of taxes and its associated costs and independence based on the morality of mutual aid, a nonequivalent exchange without return obligation is preferred. This is oriented toward Graeber's baseline communism and Karatani's mode of exchange D, with implications for alternatives to the capitalist economy.

In this paper, we consider the problem of adjusting the exploration rate when using value-of-information-based exploration. We do this by converting the value-of-information optimization into a problem of finding equilibria of a flow for a changing exploration rate. We then develop an efficient path-following scheme for converging to these equilibria and hence uncovering optimal action-selection policies. Under this scheme, the exploration rate is automatically adapted according to the agent's experiences. Global convergence is theoretically assured. We first evaluate our exploration-rate adaptation on the Nintendo GameBoy games Centipede and Millipede. We demonstrate aspects of the search process, like that it yields a hierarchy of state abstractions. We also show that our approach returns better policies in fewer episodes than conventional search strategies relying on heuristic, annealing-based exploration-rate adjustments. We then illustrate that these trends hold for deep, value-of-information-based agents that learn to play ten simple games and over forty more complicated games for the Nintendo GameBoy system. Performance either near or well above the level of human play is observed.

Automatic font generation without human experts is a practical and significant problem, especially for some languages that consist of a large number of characters. Existing methods for font generation are often in supervised learning. They require a large number of paired data, which are labor-intensive and expensive to collect. In contrast, common unsupervised image-to-image translation methods are not applicable to font generation, as they often define style as the set of textures and colors. In this work, we propose a robust deformable generative network for unsupervised font generation (abbreviated as DGFont++). We introduce a feature deformation skip connection (FDSC) to learn local patterns and geometric transformations between fonts. The FDSC predicts pairs of displacement maps and employs the predicted maps to apply deformable convolution to the low-level content feature maps. The outputs of FDSC are fed into a mixer to generate final results. Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts. To distinguish different styles, we train our model with a multi-task discriminator, which ensures that each style can be discriminated independently. In addition to adversarial loss, another two reconstruction losses are adopted to constrain the domain-invariant characteristics between generated images and content images. Taking advantage of FDSC and the adopted loss functions, our model is able to maintain spatial information and generates high-quality character images in an unsupervised manner. Experiments demonstrate that our model is able to generate character images of higher quality than state-of-the-art methods.

Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume all of the views are complete. However, in real applications, the multi-view data are often incomplete, i.e., some views of instances are missing, which will result in the failure of these methods. Besides, while the data arrive in form of streams, these existing methods will suffer the issues of high storage cost and expensive computation time. To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view streaming data. By jointly considering the consistent and complementary information across different views, I$^2$MUFS embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model, which can learn a consensus clustering indicator matrix and fuse different latent feature matrices with adaptive view weights. Furthermore, we introduce the incremental leaning mechanisms to develop an alternative iterative algorithm, where the feature selection matrix is incrementally updated, rather than recomputing on the entire updated data from scratch. A series of experiments are conducted to verify the effectiveness of the proposed method by comparing with several state-of-the-art methods. The experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of the clustering metrics and the computational cost.

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.

Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural networks have achieved competitive performance, the resulting policies are often over-parameterized black boxes that are difficult to interpret and deploy efficiently. More recent symbolic RL frameworks have shown that high-level domain-specific programming logic can be designed to handle both policy learning and symbolic planning. However, these approaches rely on coded primitives with little feature learning, and when applied to high-dimensional visual scenes, they can suffer from scalability issues and perform poorly when images have complex object interactions. To address these challenges, we propose \textit{Differentiable Symbolic Expression Search} (DiffSES), a novel symbolic learning approach that discovers discrete symbolic policies using partially differentiable optimization. By using object-level abstractions instead of raw pixel-level inputs, DiffSES is able to leverage the simplicity and scalability advantages of symbolic expressions, while also incorporating the strengths of neural networks for feature learning and optimization. Our experiments demonstrate that DiffSES is able to generate symbolic policies that are simpler and more and scalable than state-of-the-art symbolic RL methods, with a reduced amount of symbolic prior knowledge.

Forecasts from Persistence Market Research indicate that by the end of the forecast period in 2027, the worldwide deep learning market would be worth US$ 261,113.0 This indicates a 49.0% compound annual growth rate that was seen over the anticipated period. This development can be ascribed to the demand for improved processing hardware, an increase in global R&D activity in particular industries, and the quick global adoption of cloud-based technologies. A recent research from Persistence Market Research offers a complete review of the worldwide deep learning market. In-depth analysis of the deep learning concept and the performance of the global deep learning market across significant end-use industry sectors throughout seven significant geographies are provided in this study.