Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it. Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transformin g your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment. What You'll Learn See the history of AI and how machine learning and cognitive computing evolved Discover why cognitive computing is so important and why your business needs it Master the details of modern AI as it applies to enterprises Map the path ahead in terms of your IT-business integration Avoid common road blocks in the process of adopting cognitive computing in your business Who This Book Is For Business managers and leadership teams.
In the past few years, with the evolution of advanced persistent threats and mutation techniques, sensitive and damaging information from a variety of sources have been exposed to possible corruption and hacking. Machine learning, artificial intelligence, predictive analytics, and similar disciplines of cognitive science applications have been found to have significant applications in the domain of cyber security. Machine Learning and Cognitive Science Applications in Cyber Security examines different applications of cognition that can be used to detect threats and analyze data to capture malware. Highlighting such topics as anomaly detection, intelligent platforms, and triangle scheme, this publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact of machine learning in cyber security and the methodologies that can help improve the performance and reliability of machine learning applications.
Focusing on the cutting-edge applications of AI cognitive computing from neuromorphic to quantum cognition as applied to AI business analytics, this new volume explores AI’s importance in managing cognitive processes along with ontological modeling concepts for venturing into new business frontiers. The volume presents a selection of significant new accomplishments in the areas of AI cognitive computing ranging from neurocognition perception and decision-making in the human brain—combining neurocognitive techniques and effective computing—to basic facial recognition computing models. Topics include: Agent neurocomputing techniques for facial expression recognition Computing haptic motion and ontology epistemic Characterizations of morph schemas for visual analytics Learning and perceptive computing Functional and structural neuroimaging modeling Observed links between facial recognition and affective emotional processes Interaction of cognitive and emotional processes during social decision-making Neurocognitive processing of emotional facial expressions in individuals Neurocognitive affective system for emotive robot androids Virtual reality-based affect adaptive neuromorphic computing Executive surveys indicate that cognitive adoption is very important in business strategy for success and to remain competitive. Employing cognitive-based processes provides the way to get the right information in the right hands at the right time, which is the key to winning in the digital era and to driving business value that emphasizes competitive differentiation. Several chapters of the volume address the goal of using cognitive technology to improve search capabilities, to provide personalized customer service in business and in health and wellness, and to create better workflow management. Key features: Looks at the newest frontiers on very popular AI and analytics topics Discusses new techniques for visual analytics and data filtering Shows how AI and cognitive science merges with quantum neurocognitive computing Presents ontology models with ontology preservation data filtering techniques Provides a cross-transposition on AI and digitizations for business model innovations Artificial Intelligence and Computing Logic: Cognitive Technology for AI Business Analytics is a valuable resource that informs businesses and other enterprises the value of artificial intelligence and computing logic applications.
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.
A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data. This book helps technologists understand cognitive computing's underlying technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches based on accumulated evidence, rather than reprogramming. Detailed case examples from the financial, healthcare, and manufacturing walk readers step-by-step through the design and testing of cognitive systems, and expert perspectives from organizations such as Cleveland Clinic, Memorial Sloan-Kettering, as well as commercial vendors that are creating solutions. These organizations provide insight into the real-world implementation of cognitive computing systems. The IBM Watson cognitive computing platform is described in a detailed chapter because of its significance in helping to define this emerging market. In addition, the book includes implementations of emerging projects from Qualcomm, Hitachi, Google and Amazon. Today's cognitive computing solutions build on established concepts from artificial intelligence, natural language processing, ontologies, and leverage advances in big data management and analytics. They foreshadow an intelligent infrastructure that enables a new generation of customer and context-aware smart applications in all industries. Cognitive Computing is a comprehensive guide to the subject, providing both the theoretical and practical guidance technologists need. Discover how cognitive computing evolved from promise to reality Learn the elements that make up a cognitive computing system Understand the groundbreaking hardware and software technologies behind cognitive computing Learn to evaluate your own application portfolio to find the best candidates for pilot projects Leverage cognitive computing capabilities to transform the organization Cognitive systems are rightly being hailed as the new era of computing. Learn how these technologies enable emerging firms to compete with entrenched giants, and forward-thinking established firms to disrupt their industries. Professionals who currently work with big data and analytics will see how cognitive computing builds on their foundation, and creates new opportunities. Cognitive Computing provides complete guidance to this new level of human-machine interaction.
Within the past decade, marketing has experienced three major challenges: generation gap, prosperity polarization, and digital divide. The disconnect between older corporate executives and their younger managers and customers has proven to be a significant challenge. Digitalization brings fear of the unknown with the threats of job loss and privacy concerns. However, it also brings the promise of exponential growth and better living for humanity. Businesses must break the divide to ensure that technological advancement will move forward and not be welcomed with resentment. Developing Relationships, Personalization, and Data Herald in Marketing 5.0 contrasts the advantages and disadvantages of modern marketing over traditional marketing and focuses on identifying how companies and society can be benefited by the technological advancement of marketing. Covering topics such as customer engagement, neuromarketing, and review rating prediction, this premier reference source is an essential resource for business leaders, marketing professionals, students and educators of higher education, university libraries, researchers, and academicians.
Machine Learning Guide: A Practical Approach for Businesses Description Artificial intelligence is a growing field with many possibilities extending in many directions. Artificial intelligence encompasses many terms you may have heard and terms that we will be covering in this book like deep learning, neural networks, and machine learning. Sometimes we hear doubts out there that artificial intelligence will never be as good as humans are at navigating the choices and actions of other humans, but that is not always the case. Some systems are already at levels that perform better than humans, and while reading people can be hard, it's all based on perception and cognition-two of the areas in which machine learning are continuing to expand and strengthen around. Machine learning for competitive advantage As there is a difference between acting as a friend and passively assessing someone's mental or emotional state and attempting to actively change that state, machine learning systems are powerful at figuring out the problems that it should work on solving next and persuade others to work on these problems and implement the solutions the system suggests. Companies that are thriving have embraced the trend of putting machine learning to work for them in all of the places that are appropriate, and in all of the places where the system can effectively integrate into the business. Those who cannot seem to let go of the past will find themselves at a competitive disadvantage to those who are willing to accept machine learning. Artificial Intelligence, the most important technology of our era It has been stated in The Harvard Business Review that "artificial intelligence, especially machine learning, is the most important general-purpose technology of our era. The impact of these innovations on business and the economy will be reflected not only in their direct contributions but also in their ability to enable and inspire complementary innovations. New products and processes are being made possible by better vision systems, speech recognition, intelligent problem solving, and many other capabilities that machine learning delivers." Similar to Charles Darwin's survival of the fittest theory, those companies that adapt with the time will thrive and continue to see opportunities to use machine learning. In this book, we will discuss what machine learning is, how you can apply it to your business today, and how it can be used to quickly and accurately use the systems to trade stocks. Machine learning for business No longer a niche subfield of the rapidly growing field of computer science, artificial intelligence has been used for years by tech giants Amazon, Google, Facebook, Instagram, and Twitter. Understanding algorithms involved in machine learning is starting to become a much sought after business skills that can be applied to a business to identifying problems, giving feedback on outputs, and looking for algorithmic success based on the right set of objectives. Could machine learning be useful to you or your organization? Ask yourself if the answer you want is to be agreed upon by ten of your friends or colleagues. If a group of humans can't come to an agreement on if something is right or wrong, it is hard to expect a computer to be able to reliably take our judgment calls and calculate them into some kind of statistical pattern.
With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI.
Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.
