This trend also brings about a unique opportunity and good assurance for solving different critical problems in medical and healthcare systems as well as engineering applications of Artificial Intelligence (AI) and Operations Research (OR). However, such an assurance strongly depends on the extent to which researchers can discover useful patterns, find informative mechanisms underlying the fragmented and diverse data sets, as well as convert this knowledge into intelligent decisions. AI techniques have been recently studied and applied as promising tools for the development and application of intelligent systems in the healthcare context. AI-based systems can generally learn from data and evolve according to real-time changes and fluctuations by considering the indisputable uncertainty of health data and processes. Many attempts have been made so far that employ different techniques including, inter alia, Machine Learning (ML), neural networks, optimization, computational intelligence and human–machine interface.

The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. It has profoundly impacted several areas, including computer vision, natural language processing, and transportation. However, the use of rich data sets also raises significant privacy concerns: They often reveal personal sensitive information that can be exploited, without the knowledge and/or consent of the involved individuals, for various purposes including monitoring, discrimination, and illegal activities. The second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21) held at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) builds on the success of last year's AAAI PPAI to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI. Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples.

IMAGE: Andrew Trapp, an associate professor in the Foisie Business School at Worcester Polytechnic Institute (WPI), and PhD student Narges Ahani are working on an NSF-funded grant to develop software to... view more Built upon ongoing work with an international team of computer scientists and economists, the tool integrates machine learning and optimization algorithms, along with complex computation of data, to match refugees to communities where they will find appropriate resources, including employment opportunities. "There is a great deal of information to consider when helping a refugee begin a new life in the United States," said Trapp, associate professor in the Foisie School and lead investigator for the project. "It is a labor-intensive process whose ultimate goal is to help a refugee land in a place where he or she has as many opportunities as possible to successfully integrate and contribute to the community. Technological solutions can have a profound societal impact." Each year, tens of thousands of refugees--many fleeing war, violence, and persecution--are resettled in dozens of host countries around the world.

Learning ability in Robotics is acknowledged as one of the major challenges facing artificial intelligence. Although in the numerous areas within Robotics machine learning (ML) has long identified as a core technology, recently Robot learning, in particular, has been witnessing major challenges due to the theoretical advancement at the boundary between optimization and ML. In fact the integration of ML and optimization reported to be able to dramatically increase the decision-making quality and learning ability in decision systems. Here the novel integration of ML and optimization which can be applied to the complex and dynamic contexts of Robot learning is described. Furthermore with the aid of an educational Robotics kit the proposed methodology is evaluated.

Al Martin: Hi folks, this is Al Martin from Making Data Simple, the series, if you will. Today I have Jean-Francois Puget. Jean-Francois Puget: Yes, you did great. You passed your French test. Al Martin: All right, good, I'm going to give you the [name] JFP from now on, is that all right? So JFP is the distinguished engineer for machine learning and optimization, that's the topic today and we're going to go into that. I also have with me [Steve Moore], who is a senior content designer and storage strategist. Al Martin: So Steve wanted to join the conversation, ask a few questions. So he'll ask the intelligent questions, I will ask the normal, blockhead questions, if you will. So, thank you for being here. We've done a lot, well we've done at least, I think two podcasts on machine learning. We've done one on machine 1.15 learning for dummies, one for IBM machine learning, how to [help], if you haven't heard those, go back, so we can't do enough, and I notice that on your title JFP is machine learning and optimization.

Machine learning is becoming an increasingly important artificial intelligence approach to building autonomous and robotic systems. One of the key challenges with machine learning is the need for many samples, the amount of data needed to learn useful behaviors is high. In addition, the robotic system is often non-operational during the training phase. This requires debugging to occur in real-world experiments with an unpredictable robot. Microsoft's Aerial Informatics and Robotics platform has a solution for these two problems: It will provide realistic simulation tools for designers and developers to generate the training data needed and will also leverage recent innovations in physics to create accurate, real-world simulations.