One approach to achieving artificial general intelligence (AGI) is through the emergence of complex structures and dynamic properties arising from decentralized networks of interacting artificial intelligence (AI) agents. Understanding the principles of consensus in societies and finding ways to make consensus more reliable becomes critically important as connectivity and interaction speed increase in modern distributed systems of hybrid collective intelligences, which include both humans and computer systems. We propose a new form of reputation-based consensus with greater resistance to reputation gaming than current systems have. We discuss options for its implementation, and provide initial practical results.
This article is by Featured Blogger Cathy Hackl. In an age where our reputations and the reputations of the brands we represent are everything, how can we, as communications professionals, better prepare for the way artificial intelligence may impact reputation management? We spend countless hours building our reputations, managing them and even remedying them after a PR disaster; all the while, brands are spending millions of dollars on chatbots and AI customer service. As a futurist, I'm always trying to think about how new technologies will impact our profession and how we can be better prepared for these transformations. So, in the age of artificial intelligence, will it be business as usual or will reputation management be greatly impacted?
In today's business environment, online reputation management is increasingly important. The landscape is rapidly changing and technology is leading the way. I caught up with the CEO of www.reputation.com Q: What exactly is Online Reputation Management? Online Reputation Management is making sure a business's online reputation, including publicly available opinions and reviews about your business, matches the offline experience that people have with your business.
Machine learning methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. Despite the excitement surrounding machine learning methods, clarity regarding their proper use and potential pitfalls is lacking. This paper attempts to fill this gap in the literature through providing a machine learning "guide to practice" for public administration scholars and practitioners. Here, we take a foundational view of machine learning and describe how these methods can enrich public administration research and practice through their ability develop new measures, tap into new sources of data and conduct statistical inference and causal inference in a principled manner. We then turn our attention to the pitfalls of using these methods such as unvalidated measures and lack of interpretability. Finally, we demonstrate how machine learning techniques can help us learn about organizational reputation in federal agencies through an illustrated example using tweets from 13 executive federal agencies.