The fast pace of artificial intelligence (AI) and automation is propelling strategists to reshape their business models. This is fostering the integration of AI in the business processes but the consequences of this adoption are underexplored and need attention. This paper focuses on the overall impact of AI on businesses - from research, innovation, market deployment to future shifts in business models. To access this overall impact, we design a three-dimensional research model, based upon the Neo-Schumpeterian economics and its three forces viz. innovation, knowledge, and entrepreneurship. The first dimension deals with research and innovation in AI. In the second dimension, we explore the influence of AI on the global market and the strategic objectives of the businesses and finally, the third dimension examines how AI is shaping business contexts. Additionally, the paper explores AI implications on actors and its dark sides.

President Trump began his trade war with China out of concern that Beijing was using unfair economic practices to prevent the United States from dominating next-generation technologies like autonomous vehicles, advanced telecommunications and artificial intelligence. But as the two countries move closer to a trade deal, it seems increasingly unlikely that China will give ground in a crucial area that could determine which country wins the technology race. Despite months of pressure from the White House, Chinese negotiators have so far refused to relax tight regulations that block multinational companies from moving data they gather on their Chinese customers' purchases, habits and whereabouts out of the country. Such data is crucial as industries build next-generation technologies. With another round of trade talks underway this week, the United States and China appear headed toward an agreement that could end the monthslong trade war and lift tariffs on hundreds of billions of dollars of products.

Bhimavaram: Andhra Pradesh State Skill Development Corporation conducted classes in Machine Learning and Nano Technology for students, said SRKR Engineering College in-charge principal Dr M Jagapati Raju. He said that 52 students in Machine Learning and 17 students on Nano Technology received the certificates. On this occasion, college secretary and correspondent SV Rangaraju distributed the certificates to the students. Technology Centre head Dr N Gopala Krishna said that UDI is the organisation that was teaching the students all over the world on Machine Learning and Nano Technology. The students who showed better performance will get job opportunities with high packages, he said.

Lurking behind the Trump administration's trade conflict with China lies an abiding fear that the United States could be losing its advantage in the global technology race. In US policymaking circles more broadly, China's "Made in China 2025" policy – intended to ensure Chinese dominance in cyber capabilities, artificial intelligence (AI), aeronautics, and other frontier sectors – is viewed not just as an economic challenge, but as a geopolitical threat. Everything from US telecommunications infrastructure and intellectual property to America's military position in East Asia are considered to be at risk. The fact that technology is driving geopolitical tensions runs against the predictions of many scholars and policymakers. As recently as the mid-2000s, some suspected that geography would no longer play a meaningful role in the functioning of global markets.

Long an obsession of science fiction writers, "artificial intelligence" in the modern era of fast-paced technological innovation is a term that is as ubiquitous as it is nebulous. For the payments technology industry, however, the term describes advanced analytical technology that has an outsized potential to improve the payments ecosystem for banks, payments processors, merchants and consumers. In fact, financial services companies will spend US$11 billion on AI in 2020, according to an analysis by IDC -- more than any other industry cited. They'll stand to make a nice return on their investment as well, according to PwC estimates. In North America alone, AI is projected to increase the GDP of the financial and professional services industry as much as 10 percent by 2030, driven by increases in both productivity and consumption.

By the third century B.C., engineers in Hellenistic Alexandria, in Egypt, were building real mechanical robots and machines. And such science fictions and historical technologies were not unique to Greco-Roman culture. In my recent book "Gods and Robots," I explain that many ancient societies imagined and constructed automatons. Chinese chronicles tell of emperors fooled by realistic androids and describe artificial servants crafted in the second century by the female inventor Huang Yueying. Techno-marvels, such as flying war chariots and animated beings, also appear in Hindu epics. One of the most intriguing stories from India tells how robots once guarded Buddha's relics.

Artificial Intelligence (AI), defined in its most simple form, is a technological tool that makes machines intelligent. Since learning is at the core of intelligence, machine learning poses itself as a core sub-field of AI. Then there comes a subclass of machine learning, known as deep learning, to address the limitations of their predecessors. AI has generally acquired its prominence over the past few years due to its considerable progress in various fields. AI has vastly invaded the realm of research. This has led physicists to attentively direct their research towards implementing AI tools. Their central aim has been to gain better understanding and enrich their intuition. This review article is meant to supplement the previously presented efforts to bridge the gap between AI and physics, and take a serious step forward to filter out the "Babelian" clashes brought about from such gabs. This necessitates first to have fundamental knowledge about common AI tools. To this end, the review's primary focus shall be on deep learning models called artificial neural networks. They are deep learning models which train themselves through different learning processes. It discusses also the concept of Markov decision processes. Finally, shortcut to the main goal, the review thoroughly examines how these neural networks are capable to construct a physical theory describing some observations without applying any previous physical knowledge.

Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a natural appearance. In this work, we enhance the image quality by using a Conditional Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique that stabilizes the training and minimizes the degree of artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.

Weight quantization is one of the most important techniques of Deep Neural Networks (DNNs) model compression method. A recent work using systematic framework of DNN weight quantization with the advanced optimization algorithm ADMM (Alternating Direction Methods of Multipliers) achieves one of state-of-art results in weight quantization. In this work, we first extend such ADMM-based framework to guarantee solution feasibility and we have further developed a multi-step, progressive DNN weight quantization framework, with dual benefits of (i) achieving further weight quantization thanks to the special property of ADMM regularization, and (ii) reducing the search space within each step. Extensive experimental results demonstrate the superior performance compared with prior work. Some highlights: we derive the first lossless and fully binarized (for all layers) LeNet-5 for MNIST; And we derive the first fully binarized (for all layers) VGG-16 for CIFAR-10 and ResNet for ImageNet with reasonable accuracy loss.

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multilabel sampling methods alleviate the (global) imbalance of multi-label datasets. However, performance degradation is mainly due to rare subconcepts and overlapping of classes that could be analysed by looking at the local characteristics of the minority examples, rather than the imbalance of the whole dataset. We propose a new method for synthetic oversampling of multi-label data that focuses on local label distribution to generate more diverse and better labeled instances. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed approach in a variety of evaluation measures, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.