We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications. GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs. Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN's applications in different fields.

Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit relation. However, there is a more natural and intuitive relevancy among entities being always ignored, which is that how one entity is close to another semantically, without the consideration of any explicit relation. We name such semantic phenomenon in knowledge graph as proximity pattern. In this work, we explore the problem of how to define and represent proximity pattern, and how it can be utilized to help knowledge graph embedding. Firstly, we define the proximity of any two entities according to their statistically shared queries, then we construct a derived graph structure and represent the proximity pattern from global view. Moreover, with the original knowledge graph, we design a Chained couPle-GNN (CP-GNN) architecture to deeply merge the two patterns (graphs) together, which can encode a more comprehensive knowledge embedding. Being evaluated on FB15k-237 and WN18RR datasets, CP-GNN achieves state-of-the-art results for Knowledge Graph Completion task, and can especially boost the modeling capacity for complex queries that contain multiple answer entities, proving the effectiveness of introduced proximity pattern.

My last blog discussed AI innovations in the health care sector, and this one will share a few perspectives of new developments in this industry. According to Business Wire, the Artificial Intelligence (AI) spend in media and entertainment industry in the United States forecast period (2019-2025) is expected to grow at a CAGR record of 28.1%, increasing from US$ 329 million in 2019 to reach US$ 1,860.9 million by 2025. Some of the top application areas used in this sector are: gaming, fake story detection, plagiarism detection, personalization, production planning and management, sales and marketing and talent identification. One of the areas which is very exciting is understanding how AI is being used in news. AI is making major impacts in aggregating massive data analysis of the conversations across the world-wide web and classifying into themes to appreciate topics trending globally or even identify increasing risks of terrorism or even health risks, like a pandemic.

A "like" icon seen through raindrops. WASHINGTON: Researchers at Georgetown University's Center for Security and Emerging Technology (CSET) are raising alarms about powerful artificial intelligence technology now more widely available that could be used to generate disinformation at a troubling scale. The warning comes after CSET researchers conducted experiments using the second and third versions of Generative Pre-trained Transformer (GPT-2 and GPT-3), a technology developed by San Francisco company OpenAI. GPT's text-generation capabilities are characterized by CSET researchers as "autocomplete on steroids." "We don't often think of autocomplete as being very capable, but with these large language models, the autocomplete is really capable, and you can tailor what you're starting with to get it to write all sorts of things," Andrew Lohn, senior research fellow at CSET, said during a recent event where researchers discussed their findings.

Such is our faith in technology that we tend to overlook the'how' in favor of focusing solely on the result. In the case of artificial intelligence technologies, provided that the results are accurate enough, companies, institutions, and individuals are likely to trust them without caring how they were reached. Without data, there would be no artificial intelligence. With only a small amount of data, artificial intelligence would be, well, not so intelligent. The truth is that AI systems thrive on data, and the more of it we give them, the better they are able to formulate an accurate understanding of what they are being asked to quantify.

Artificial intelligence's development has altered many facets of our existence. Video editing software is one example of a shift. When it comes to operating simple applications like video editing software, AI technology is starting to engage and even replace workers in some situations. The fundamental notion is that AI will learn from its mistakes and make more precise choices than a human ever could. For individuals who rely on their employment as video editors, this may seem frightening, but there are still numerous ways humans can help in the management that cannot be handled by an algorithm just yet.

In order to continue our impressive track record in setting global technological standards, it is important we ensure American small and medium-sized businesses have a seat at the table. Many of most remarkable AI technological breakthroughs are being developed by smaller innovators who simply do not have the financial resources of a CCP-backed megacorporation. Accordingly, we are proud to have introduced the Leadership in Global Tech Standards Act of 2021, legislation that would provide small businesses throughout the country with the financial backing they need to participate in setting global AI standards. This bipartisan legislation, which is also being co-sponsored by Reps. Jason CrowJason CrowOvernight Defense & National Security -- Presented by AM General -- Afghan evacuation still frustrates Bipartisan momentum builds for war on terror memorial Democrats face full legislative plate and rising tensions MORE (D-Colo.) and Jerry McNerneyGerlad (Jerry) Mark McNerneyHouse passes host of bills to strengthen cybersecurity in wake of attacks In defense of misinformation House Democrats want to silence opposing views, not'fake news' MORE (D-Calif.),

When Ludwig von Beethoven died in 1827, he was three years removed from the completion of his Ninth Symphony, a work heralded by many as his magnum opus. He had started work on his 10th Symphony but, due to deteriorating health, wasn't able to make much headway: All he left behind were some musical sketches. Ever since then, Beethoven fans and musicologists have puzzled and lamented over what could have been. His notes teased at some magnificent reward, albeit one that seemed forever out of reach. Now, thanks to the work of a team of music historians, musicologists, composers, and computer scientists, Beethoven's vision will come to life.

Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves, however, they adjust their speaking and writing style to a social context, an audience, an interlocutor or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence. A systematic review of text style transfer methodologies using deep learning is presented in this paper. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.

Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are "missing completely at random" (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of "latent confounders", i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the observed matrix. For example, in the context of movie recommender systems -- a canonical application for matrix completion -- a user who vehemently dislikes horror films is unlikely to ever watch horror films. In general, these confounders yield "missing not at random" (MNAR) data, which can severely impact any inference procedure that does not correct for this bias. We develop a formal causal model for matrix completion through the language of potential outcomes, and provide novel identification arguments for a variety of causal estimands of interest. We design a procedure, which we call "synthetic nearest neighbors" (SNN), to estimate these causal estimands. We prove finite-sample consistency and asymptotic normality of our estimator. Our analysis also leads to new theoretical results for the matrix completion literature. In particular, we establish entry-wise, i.e., max-norm, finite-sample consistency and asymptotic normality results for matrix completion with MNAR data. As a special case, this also provides entry-wise bounds for matrix completion with MCAR data. Across simulated and real data, we demonstrate the efficacy of our proposed estimator.