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.

The world's food system is in disarray. One in ten people is undernourished. One in four is overweight. More than one-third of the world's population cannot afford a healthy diet. Food supplies are disrupted by heatwaves, floods, droughts and wars.

We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient's bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic. LR, SVM and MLP are evaluated as baseline classifiers and compared with deep architectures such as CNN, LSTM, and Resnet50 using a leave-one-out cross-validation scheme. We find that the studied classifiers can use either acceleration or audio signals to distinguish between coughing and other activities including sneezing, throat-clearing, and movement on the bed with high accuracy. However, in all cases, the deep neural networks outperform the shallow classifiers by a clear margin and the Resnet50 offers the best performance by achieving an AUC exceeding 0.98 and 0.99 for acceleration and audio signals respectively. While audio-based classification consistently offers a better performance than acceleration-based classification, we observe that the difference is very small for the best systems. Since the acceleration signal requires less processing power, and since the need to record audio is sidestepped and thus privacy is inherently secured, and since the recording device is attached to the bed and not worn, an accelerometer-based highly accurate non-invasive cough detector may represent a more convenient and readily accepted method in long-term cough monitoring.

Evolutionary clustering algorithms have considered as the most popular and widely used evolutionary algorithms for minimising optimisation and practical problems in nearly all fields. In this thesis, a new evolutionary clustering algorithm star (ECA*) is proposed. Additionally, a number of experiments were conducted to evaluate ECA* against five state-of-the-art approaches. For this, 32 heterogeneous and multi-featured datasets were used to examine their performance using internal and external clustering measures, and to measure the sensitivity of their performance towards dataset features in the form of operational framework. The results indicate that ECA* overcomes its competitive techniques in terms of the ability to find the right clusters. Based on its superior performance, exploiting and adapting ECA* on the ontology learning had a vital possibility. In the process of deriving concept hierarchies from corpora, generating formal context may lead to a time-consuming process. Therefore, formal context size reduction results in removing uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this work aims to propose a framework to reduce the ambiguity of the formal context of the existing framework using an adaptive version of ECA*. In turn, an experiment was conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reduction of formal context size, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context was evaluated to the original one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the original one.