Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant behavior. From an AI model perspective it means given the same input the user would expect the same output, especially for correct outputs, or in other words consistently correct outputs. This paper studies a model behavior in the context of periodic retraining of deployed models where the outputs from successive generations of the models might not agree on the correct labels assigned to the same input. We formally define consistency and correct-consistency of a learning model. We prove that consistency and correct-consistency of an ensemble learner is not less than the average consistency and correct-consistency of individual learners and correct-consistency can be improved with a probability by combining learners with accuracy not less than the average accuracy of ensemble component learners. To validate the theory using three datasets and two state-of-the-art deep learning classifiers we also propose an efficient dynamic snapshot ensemble method and demonstrate its value.

Angie Wisdom and Dr. Chirag Shah discuss how artificial intelligence could play a role in online and professional relationships. Artificial intelligence (AI) is already providing significant benefit to people searching for love, negotiating a business deal and struggling with depression. However, according to experts, an over reliance on the technology could cause a "devaluation" of human connection and lead to diminishing authenticity. Rijul Gupta, CEO and co-founder of DeepMedia, told Fox News Digital that as AI continues to evolve, the technology has the potential to enrich social interactions, deliver companionship, and provide support in areas like child-rearing and elder care. Furthermore, Gupta predicted AI is set to "revolutionize" online dating by enhancing existing platforms, such as Tinder and Hinge, with new algorithms that provide more "personalized" and "efficient" matchmaking.

Expert Systems: Where Are We? And Where Do We Go From Here? This article has benefited from careful readings by and suggestions from Mike Brady, Bruce Buchanan, Ed Feigenbaum, Jo& Gon&lez, Pete Szolovits, and Pat Winston Editor's note: This article was the author's "Invited Lecture" at IJCAI 7 Where has the Accepted Wisdom succeeded? One important goal of this part of the journey will be to calibrate the current state of the art-Given what we know, what can we do and how quickly can we do it? Does building a system typically require several months or several years?