Generative Adversarial Networks (GANs) were proposed in 2014 by Goodfellow et al., and have since been extended into multiple computer vision applications. This report provides a thorough survey of recent GAN research, outlining the various architectures and applications, as well as methods for training GANs and dealing with latent space. This is followed by a discussion of potential areas for future GAN research, including: evaluating GANs, better understanding GANs, and techniques for training GANs. The second part of this report outlines the compilation of a dataset of images `in the wild' representing each of the 7 basic human emotions, and analyses experiments done when training a StarGAN on this dataset combined with the FER2013 dataset.

Companies of all shapes and sizes are chasing the holy grail of data-driven transformation, driven by the need to unlock insights to fuel new revenue opportunities, drive efficiencies, and deliver innovative products and services. In a survey of 2,300 global business and IT leaders by MIT Technology Review Insights, in association with Pure Storage, 87% of respondents said data is key to new business growth and for delivering better results for customers and clients. Eighty-six percent of those surveyed viewed data as the foundation for making important business decisions. Companies are facing a variety of challenges transforming data into meaningful business value. Sometimes the hardest part is figuring out exactly where to start.

Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized every aspect of our life and disrupted how we do business, unlike any other technology in the the history of mankind. Such disruption brings many challenges for professionals and businesses. In this article, I will provide an introduction to one of the most commonly used machine learning methods, K-Means. Machine learning is a scientific method that utilizes statistical methods along with the computational power of machines to convert data to wisdom that humans or the machine itself can use for taking certain actions. "It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention."

West Bengal, India, Oct 11, 2019 Innovative technologies have always been a norm for empowering new and old businesses. In this competitive era, it has been seen that the revenue generation of a company is directly proportional to the development done in the field of technology. Studies have shown that digital marketing industry is changing and evolving at a rapid scale. Based on a study, it has been found that 76% of digital marketing success can be obtained from search engine optimization alone. With that being said, people from all around the planet are working immensely hard for getting the right amount of search optimization done for their online inventory or websites. The harsh reality is that this is increasing the competitiveness of the industry at a much greater scale.

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted US consumers' interest in AI working alongside their physicians, the current porn-heavy state of deepfakes, the potential business benefits of Robotic Process Automation (RPA), and the impact of automation on incomes and employment. The internet is home to at least 14,678 deepfakes, according to a new report by DeepTrace. Funds run by computers that follow rules set by humans account for 35% of America's stockmarket, 60% of institutional equity assets and 60% of trading activity. Exchange-traded funds (etfs) and mutual funds automatically track indices of shares and bonds--in September 2019 these vehicles had $4.3trn invested in American equities, exceeding the sums actively run by humans for the first time [The Economist] Only 16% expect to reduce training budgets; 52% of the U.S. employees surveyed believe they have the necessary skills to be successful in an AI-enabled workplace, 20% saying they do not possess the right skills, and 28% reporting they aren't sure; 25% of employers and 20% of employees see a definite gap in workers' skills; 63% of U.S. employees expressed willingness to use a virtual or digital assistant to help them self-manage tasks and deadlines [Genesys surveys of 303 employers and 1,001 employees] Only 29% of finance departments that have deployed Robotic Process Automation (RPA) have utilized the technology for financial reporting; the average amount of avoidable rework in accounting departments can take up to 30% of a full-time employee's overall time. The first comprehensive review of studies published since 2012 comparing analysis of medical images by healthcare professionals and deep learning systems found humans and machines are on a par.

AI heralds a step-change in the performance and capability of wireless networks and other critical infrastructures. However, it may also cause irreversible environmental damage due to their high energy consumption. Here, we address this challenge in the context of 5G and beyond, where there is a complexity explosion in radio resource management (RRM). On the one hand, deep reinforcement learning (DRL) provides a powerful tool for scalable optimization for high dimensional RRM problems in a dynamic environment. On the other hand, DRL algorithms consume a high amount of energy over time and risk compromising progress made in green radio research. This paper reviews and analyzes how to achieve green DRL for RRM via both architecture and algorithm innovations. Architecturally, a cloud based training and distributed decision-making DRL scheme is proposed, where RRM entities can make lightweight deep local decisions whilst assisted by on-cloud training and updating. On the algorithm level, compression approaches are introduced for both deep neural networks and the underlying Markov Decision Processes, enabling accurate low-dimensional representations of challenges. To scale learning across geographic areas, a spatial transfer learning scheme is proposed to further promote the learning efficiency of distributed DRL entities by exploiting the traffic demand correlations. Together, our proposed architecture and algorithms provide a vision for green and on-demand DRL capability.

Learned representations of source code enable various software developer tools, e.g., to detect bugs or to predict program properties. At the core of code representations often are word embeddings of identifier names in source code, because identifiers account for the majority of source code vocabulary and convey important semantic information. Unfortunately, there currently is no generally accepted way of evaluating the quality of word embeddings of identifiers, and current evaluations are biased toward specific downstream tasks. This paper presents IdBench, the first benchmark for evaluating to what extent word embeddings of identifiers represent semantic relatedness and similarity. The benchmark is based on thousands of ratings gathered by surveying 500 software developers. We use IdBench to evaluate state-of-the-art embedding techniques proposed for natural language, an embedding technique specifically designed for source code, and lexical string distance functions, as these are often used in current developer tools. Our results show that the effectiveness of embeddings varies significantly across different embedding techniques and that the best available embeddings successfully represent semantic relatedness. On the downside, no existing embedding provides a satisfactory representation of semantic similarities, e.g., because embeddings consider identifiers with opposing meanings as similar, which may lead to fatal mistakes in downstream developer tools. IdBench provides a gold standard to guide the development of novel embeddings that address the current limitations.

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.

Artificial Intelligence is a major game-changer technology which is ranked next behind Data Science. Within the years to come AI is expected to completely revolutionize the face of the world we live in. As per the experts view AI considered to become the core of everything humans are going to be interacting with over the coming few years. The applications of AI are quite fascinating & based on the type of tasks carried out by AI robots, AI has been divided into different categories. As AI is termed as the future revolutionary technology, having a clear knowledge of the types in AI will surely help you prepare for this future technology.

The Research Insights has added an innovative statistics, titled as Artificial Intelligence (AI) In Construction Market. To explore the desired data, it uses primary and secondary exploratory techniques. Different aspects of the businesses are examined to provide the accurate data of market. The artificial intelligence in construction market was esteemed at USD 434 million out of 2018, and is relied upon to arrive at an estimation of USD 2,486 million by 2025, at a CAGR of 33%, during the conjecture time frame (2019 – 2025). Computerized reasoning enables PC frameworks to settle on keen choices by applying the required abilities.