Machine learning is becoming so smart that algorithms designed to set prices in online marketplaces are mirroring each others' behaviour to raise prices. Algorithms using self-learning AI are popular systems that have become adopted by Amazon to constantly learn and set the best prices in order to drive website profit. An experiment by researchers in Bologna used algorithms similar to those manipulated by online shopping sites and found they were able to'collude' to artificially hike up prices. The researchers showed that this could happen entirely out of human control, as the independent AI systems were able to learn each others' behaviours. Machine learning is becoming so smart that online price setting algorithms are mirroring each others' behaviour to raise prices and with a goal to raise profits.

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

This book has been adapted in large part from the author's doctoral thesis [Shortliffe, l 974b]. Portions of the work appeared previously in Computers And Biomedical Research [Shortliffe, 1973, l 975b], Mathematical Biosciences [Shortliffe, 1975a], and the Proceedings Of The Thirteenth San Diego Biomedical Symposium [Shortliffe, l 974a]. To Stanford's Medical Scientist Training Program, which is supported by the National Institutes of Health Contents

JANICE S. AIKINS Dr. Aikins received her Ph.D. in computer science from Stanford University in 1980. She is currently a research computer scientist at IBM's Palo Alto Scientific Center. She specializes in designing systems with an emphasis on the explicit representation of control knowledge in expert systems. ROBERT L. BLUM Dr. Blum received his M.D. from the University of California Medical School at San Francisco in 1973. From 1973 to 1976 he did an internship and residency in the Department of Internal Medicine at the Kaiser Foundation Hospital in Oakland, California, where he was chief resident in 1976.

Edward H. Shortliffe Chapter 6 Details of the Revised Therapy Algorithm

Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.

This is especially true when AI systems tackle difficult problems whose solution cannot be accurately defined by a traditional rule-based approach but require the data-driven and/or learning approaches increasingly being used in AI. Indeed, data-driven AI systems, such as those using machine learning, are very successful in terms of accuracy and flexibility, and they can be very "creative" in solving a problem, finding solutions that could positively surprise humans and teach them innovative ways to resolve a challenge. However, creativity and freedom without boundaries can sometimes lead to undesired actions: the AI system could achieve its goal in ways that are not considered acceptable according to values and norms of the impacted community. Thus, there is a growing need to understand how to constrain the actions of an AI system by providing boundaries within which the system must operate. This is usually referred to as the "value alignment" problem, since such boundaries should model values and principles required for the specific AI application scenario.

I don't know about you, I've never been comfortable with the use of the term Artificial Intelligence (AI) by mainstream media, and the way the ubiquitous sales and marketing folk have recently begin to fling it around only add to my general angst over it all. When we talk about Artificial Intelligence, to many, this conjures up thoughts of killer robots, a dystopian future and being enslaved to mega-corporations (gulp, is that bit already here?!). Apart from all that, there is a general misconception that you need a Ph.D. to be able to use these technologies, let alone understand them. To all of the above, I simply say - balderdash good sir! balderdash! I have recently taken a deep dive into the field of AI by involving myself (yet again) in some further university study.

AI for business is an incredibly helpful tool for enterprises when used correctly. Just take a look at some numbers recently published in a Forbes Magazine article: 38% of 235 enterprises the NBRI looked at are already using AI for a variety of tasks; and more importantly, 62% of these enterprises expect to be using AI by 2018. But here's the rub: AI is a massively broad catch all term. Over the last few years, people have termed all sorts of machine coding techniques as'AI;' in fact, saying that your business uses AI is kind of like saying your garden has plants. In other words, AI is an umbrella for a whole host of technologies.

Despite improvements in technology, implementation of master data management (MDM) solutions have long been a known pain for many organizations pushing to improve data quality and competency. The source of this pain is often due to the fact that traditional MDM solutions solve the data mastering problem using deterministic, rule-based approaches that do not easily accommodate nor scale for the increasing flow of messy, diverse data coming from disparate data systems. Faster technology has not been able to remove this pain, but it can be relieved with a fresh approach to MDM. In previous blog posts, my colleagues have examined in detail this new approach while explaining the need for organizations to adopt anagile approach to the data mastering problem, as well as why this approach is critical to anorganization's digital transformation. Tamr's API-driven, machine learning capability makes agile data mastering possible as it fundamentally changes the way we can tackle the data mastering problem.