Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input from which to create crop type maps. Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models. When training data is not available in one region, classifiers trained in similar regions can be transferred, but shifts in the distribution of crop types as well as transformations of the features between regions lead to reduced classification accuracy. We present a methodology that uses aggregate-level crop statistics to correct the classifier by accounting for these two types of shifts. To adjust for shifts in the crop type composition we present a scheme for properly reweighting the posterior probabilities of each class that are output by the classifier. To adjust for shifts in features we propose a method to estimate and remove linear shifts in the mean feature vector. We demonstrate that this methodology leads to substantial improvements in overall classification accuracy when using Linear Discriminant Analysis (LDA) to map crop types in Occitanie, France and in Western Province, Kenya. When using LDA as our base classifier, we found that in France our methodology led to percent reductions in misclassifications ranging from 2.8% to 42.2% (mean = 21.9%) over eleven different training departments, and in Kenya the percent reductions in misclassification were 6.6%, 28.4%, and 42.7% for three training regions. While our methodology was statistically motivated by the LDA classifier, it can be applied to any type of classifier. As an example, we demonstrate its successful application to improve a Random Forest classifier.

Nonnegative matrix factorization (NMF) is a popular model in the field of pattern recognition. It aims to find a low rank approximation for nonnegative data M by a product of two nonnegative matrices W and H. In general, NMF is NP-hard to solve while it can be solved efficiently under separability assumption, which requires the columns of factor matrix are equal to columns of the input matrix. In this paper, we generalize separability assumption based on 3-factor NMF M=P_1SP_2, and require that S is a sub-matrix of the input matrix. We refer to this NMF as a Co-Separable NMF (CoS-NMF). We discuss some mathematics properties of CoS-NMF, and present the relationships with other related matrix factorizations such as CUR decomposition, generalized separable NMF(GS-NMF), and bi-orthogonal tri-factorization (BiOR-NM3F). An optimization model for CoS-NMF is proposed and alternated fast gradient method is employed to solve the model. Numerical experiments on synthetic datasets, document datasets and facial databases are conducted to verify the effectiveness of our CoS-NMF model. Compared to state-of-the-art methods, CoS-NMF model performs very well in co-clustering task, and preserves a good approximation to the input data matrix as well.

More than $3.5 billion in funding was funneled into 35 startups last month, much of that scattered across the globe. Several Chinese companies received significant funding as the country bulks up domestic production of wafers and GPUs. In addition, with attention increasing on the need for electric vehicles and renewable energy, big investments went into battery manufacturing startups. One company making EV batteries garnered $1.5 billion, while several other large rounds were targeted at grid-scale energy storage companies. Metax designs high-performance, reconfigurable GPUs based on its own instruction set for data center, gaming, and AI. Funds will be used for R&D, and the company recently launched a corporate research institute at Zhejiang University. Based in Shanghai, China, Metax was founded in 2020.

Lee's technical explanations sit alongside Chen's fictional short stories to produce an exploration of the perils and possibilities of AI. This story revolves around a Nigerian video producer who is recruited to make an undetectable deepfake. Touching on impending breakthroughs in computer vision, biometrics, and AI security, it imagines a future world marked by cat-and-mouse games between deepfakers and detectors, and between defenders and perpetrators. As the light-rail train inched into Yaba station, Amaka pushed a button next to the door of his carriage. Even before the train came to a complete stop, the doors opened with a whoosh and Amaka hopped off.

GNW This management is possible by monitoring & regulating the processes and products for constant quality assurance, minimizing the quality gap between the manufacturing practices & end-product expectations, tracing of deviations, and make sure about the compliances. In addition, the quality management software market is estimated to register a swift growth due to the growing improvements in the capabilities of the solutions by using artificial intelligence (AI) and machine learning (ML) tools. The market of quality management software is witnessing an increasing adoption around the world because it helps in streamlining various business processes. Quality management software provides several solutions, which helps companies to gain operational efficiency that further minimizes the overall costs. Additionally, this software also enables companies to fulfil the norms and regulations, which is estimated to augment the growth of the market.

Can machines learn to use a search engine as an interactive tool for finding information? That would have far reaching consequences for making the world's knowledge more accessible. This paper presents first steps in designing agents that learn meta-strategies for contextual query refinements. Our approach uses machine reading to guide the selection of refinement terms from aggregated search results. Agents are then empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results. We develop a novel way of generating synthetic search sessions, which leverages the power of transformer-based generative language models through (self-)supervised learning. We also present a reinforcement learning agent with dynamically constrained actions that can learn interactive search strategies completely from scratch. In both cases, we obtain significant improvements over one-shot search with a strong information retrieval baseline. Finally, we provide an in-depth analysis of the learned search policies.

Mobile Robots Market is forecast to reach $54.2 billion by 2026, growing at a CAGR 20.0% from 2021 to 2026. Autonomous Mobile robot is an integration of artificial intelligence with physical robots, and has a locomotive feature, which ensures that they have the capacity to navigate around physically. They are powered by fleet management software and use sensors and other gears to identify and understand their surroundings. Robot technology is experiencing increased acceptance in different commercial and industrial environments. Hospitals, for example, are now using autonomous mobile robots to move supplies and monitor patient health.

The deepest roots of climate change begin with the second industrial revolution and the widespread adoption of fossil fuel-based machinery. As the world enters the fourth industrial revolution, the adoption of advanced technologies such as artificial intelligence (AI) introduces complex challenges and opportunities for the now-inevitable and as-yet-undetermined issues of climate change. This chapter explores those challenges and opportunities, and whether or to what extent the fourth industrial revolution will enhance Africa's ability to cope with climate change. Technologies associated with the fourth industrial revolution (4IR) include blockchain, the Internet of things (IoT), artificial intelligence, cloud computing, quantum computing, advanced wireless communications, and 3D printing, among others. Although these technologies are, at times and in various ways, interrelated, this chapter focuses mainly on AI and the impact that it will have on Africa's ability to cope with climate change.

Artificial intelligence is going to reorganize the world and change the course of human history. With China increasingly using technology to usher in a new form of authoritarianism, the world's democracies must come together and stand up for their own values and strategic interests. The world is only starting to grapple with how profound the artificial-intelligence revolution will be. AI technologies will create waves of progress in critical infrastructure, commerce, transportation, health, education, financial markets, food production, and environmental sustainability. Successful adoption of AI will drive economies, reshape societies, and determine which countries set the rules for the coming century.

Bongs Lainjo Cybermatic International, Montréal, QC, Canada Correspondence: Bongs Lainjo Email [email protected] Abstract: The objective of the study is to conduct an exploratory review of the Covid-19 pandemic by focusing on the theme of Covid-19 pandemic morbidity and mortality, considering the dynamics of artificial intelligence and quality of life (QOL). The methods used in this research paper include a review of literature, anecdotal evidence, and reports on the morbidity of COVID-19, including the scope of its devastating effects in different countries such as the US, Africa, UK, China, and Brazil, among others. The findings of this study suggested that the devastating effects of the coronavirus are felt across different vulnerable populations. These include the elderly, front-line workers, marginalized communities, visible minorities, and more. The challenge in Africa is especially daunting because of inadequate infrastructure, and financial and human resources, among others. Besides, AI technology is being successfully used by scientists to enhance the development process of vaccines and drugs. However, its usage in other stages of the pandemic has not been adequately explored. Ultimately, it has been concluded that the effects of the Covid-19 are producing unprecedented and catastrophic outcomes in many countries. With a few exceptions, the common and current intervention approach is driven by many factors, including the compilation of relevant reliable and compelling data sets. On a positive note, the compelling trailblazing and catalytic contributions of AI towards the rapid discovery of COVID-19 vaccines are a good indication of future technological innovations and their effectiveness. History has a way of reminding us that while the good times are great, a business as usual comes with many unforeseen risks and challenges. On a positive note, stress, anxiety, and other mental health issues have turned around many mindsets in certain groups. There are now significant and unprecedented levels of compassion, empathy, and more, originating from many populations. One such instance, wherein significant challenges were posed to the community is at the time of the First World War. Besides, there was the Spanish plague, there was the second world war and for the last 60 plus years, we have had to live in a world of misgivings; ranging from populism to political unrests and instability in several parts of the world, primarily the Middle East and some parts of Asia.