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Why aren't the most powerful new technologies being used to solve the world's most important problems: hunger, poverty, conflict, employment, disease? In Link, Dr. Lorien Pratt answers these questions by exploring the solution that is emerging worldwide to take Artificial Intelligence to the next level: Decision Intelligence.
Learning to Learn
Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book inv...
The Decision Intelligence Handbook
Decision intelligence is one of the top strategic technology trends for 2022. According to Gartner, more than a third of today's large organizations are expected to be practicing the discipline by 2023. But despite the growing consensus that decision intelligence offers great value to decision-makers, there's been little practical hands-on guidance on how to implement it. With this book, Lorien Pratt and Nadine Malcolm from Quantellia offer a practical methodology for understanding an action-to-outcome decision. The methodology includes a set of business processes for finding data that drives the decision, presenting data in a way that's useful for decision-makers, and showing decision-makers how to monitor and tailor the decision over time. This handbook addresses three problems that are ubiquitous in data-driven decision-making: How can decision-makers identify the data they need to support their decisions? How can you use data assets available to support a decision to show how a decision's outcomes depend on the actions taken by the decision-maker? How can decision-makers assess their decisions and improve organizational decision-making over time?
The Decision Intelligence Handbook
Decision intelligence (DI) has been widely named as a top technology trend for several years, and Gartner reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of AI. Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies. But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data. With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers. This handbook addresses three widespread data-driven decision-making problems: How can decision makers use data and technology to ensure desired outcomes? How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments? How can organizational decision makers assess and improve their decisions over time?
Teach the Geek to Speak
While speaking in public may be a challenge that you have worked hard to avoid, as a STEM professional, one of the fastest ways for you to excel in your field is to become adept at public speaking. In the straight-forward manner that most scientists appreciate, Neil Thompson's Teach the Geek to Speakoutlines his proven process for helping you to shift your mindset about speaking in public. Neil even teaches you how to prepare, present, and assess your presentations from beginning to end. An engineer himself, Neil's witty stories will make you laugh and his easy-to-implement strategies for how he overcame his fear of speaking and evolved from a nervous wreck of a presenter into an international speaker at professional conferences around the world will have you on the path to sure success too.
The People Centered Economy
AI is cutting labor costs and workers are struggling to be valuable. The People Centered Economy (PCE) is a 'Copernican Revolution' placing people, not tasks, at the center of the economy. PCE has a handy definition of the economy: people create and exchange value, served by organization. In PCE, innovation makes people value each other more (not less) and the answer to the 'future of work' is simple: people need as much innovation for earning as there is for spending. Today there is almost none there is an untapped multi-trillion dollar market waiting to be built by "the new ecosystem for innovating jobs." The book presents new ideas, models and policies for entering a competitive people-centered economy. Co-authors, leaders from the new ecosystem, present hands-on cases from the business of raising the value of people and helping them earn a good livelihood: V R Ferose, Lorien Pratt, Sudipto Dasgupta, Ganapathy Subramanian, Thorkil Sonne, Jason Palmer, Allen Blue, Patricia Olby Kimondo, Jamie Merisotis, Jacob Hsu, Tess Posner, Monique Jeanne Morrow, Daniel Pianko, Gi Fernando Wendy Guillliesm Derek Ozkal, Jim Clifton, Sven Otto Littorin and Guido Van Nispen.
Artificial Neural Networks and Neural Information Processing - Icann/Iconip 2003
This book constitutes the refereed proceedings of the joint International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003. The 138 revised full papers were carefully reviewed and selected from 346 submissions. The papers are organized in topical sections on learning algorithms, support vector machine and kernel methods, statistical data analysis, pattern recognition, vision, speech recognition, robotics and control, signal processing, time-series prediction, intelligent systems, neural network hardware, cognitive science, computational neuroscience, context aware systems, complex-valued neural networks, emotion recognition, and applications in bioinformatics.
Theory and Novel Applications of Machine Learning
Even since computers were invented, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed. The ability of learning is one of the central features of human intelligence, which makes it an important ingredient in both traditional Artificial Intelligence (AI) and emerging Cognitive Science. Machine Learning (ML) draws upon ideas from a diverse set of disciplines, including AI, Probability and Statistics, Computational Complexity, Information Theory, Psychology and Neurobiology, Control Theory and Philosophy. ML involves broad topics including Fuzzy Logic, Neural Networks (NNs), Evolutionary Algorithms (EAs), Probability and Statistics, Decision Trees, etc. Real-world applications of ML are widespread such as Pattern Recognition, Data Mining, Gaming, Bio-science, Telecommunications, Control and Robotics applications. This books reports the latest developments and futuristic trends in ML.
Bridging Human Intelligence and Artificial Intelligence
This edited volume is based on contributions from the TCET-AECT “Human-Technology Frontier: Understanding the Learning of Now to Prepare for the Work of the Future Symposium” held in Denton, Texas on May 16-18, sponsored by AECT. The authors embrace an integrative approach to designing and implementing advances technologies in learning and instruction, and focus on the emerging themes of artificial intelligence, human-computer interactions, and the resulting instructional design. The volume will be divided into four parts: (1) Trends and future in learning and learning technologies expected in the next 10 years; (2) Technologies likely to have a significant impact on learning in the next...
Semantic Web for the Working Ontologist
Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL, Second Edition, discusses the capabilities of Semantic Web modeling languages, such as RDFS (Resource Description Framework Schema) and OWL (Web Ontology Language). Organized into 16 chapters, the book provides examples to illustrate the use of Semantic Web technologies in solving common modeling problems. It uses the life and works of William Shakespeare to demonstrate some of the most basic capabilities of the Semantic Web. The book first provides an overview of the Semantic Web and aspects of the Web. It then discusses semantic modeling and how it can support the development from chaotic information gathering to ...
