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Data-driven graph learning models a network by determining the strength of connections between its nodes. The data refers to a graph signal which associates a value with each graph node. Existing graph learning methods either use simplified models for the graph signal, or they are prohibitively expensive in terms of computational and memory requirements. This is particularly true when the number of nodes is high or there are temporal changes in the network. In order to consider richer models with a reasonable computational tractability, we introduce a graph learning method based on representation learning on graphs. Representation learning generates an embedding for each graph node, taking the information from neighbouring nodes into account. Our graph learning method further modifies the embeddings to compute the graph similarity matrix. In this work, graph learning is used to examine brain networks for brain state identification. We infer time-varying brain graphs from an extensive dataset of intracranial electroencephalographic (iEEG) signals from ten patients. We then apply the graphs as input to a classifier to distinguish seizure vs. non-seizure brain states. Using the binary classification metric of area under the receiver operating characteristic curve (AUC), this approach yields an average of 9.13 percent improvement when compared to two widely used brain network modeling methods.

Seth Lazar is a Professor in the School of Philosophy at the ANU, lead CI on the ARC grant'Ethics and Risk', director of a Templeton World Charity Foundation project on'Moral Skill and Artificial Intelligence', and project leader of the major interdisciplinary research project: Humanising Machine Intelligence. In 2019, he was awarded the ANU Vice Chancellor's award for excellence in research. A central focus of his early work on the ethics of war was the necessity of taking an approach more grounded in political philosophy than in moral philosophy--the same redirection is necessary for work on the morality, law and politics of data and AI. He is also an Area Editor at Ergo, an editor of Philosophers' Imprint, and on the editorial board of Oxford Studies in Political Philosophy.

Nedjma Ousidhoum is a PhD candidate at Hong Kong University of Science and Technology. She also serves as an AIhub ambassador and has written a number of articles for us. In this interview we talk about her PhD, her research into hate speech detection, and the importance of considering AI ethics. I've been in Hong Kong for more than six years now. I came for a post-graduate internship then I stayed for a PhD. I wanted to experience living and working in Asia.

Geoffrey Hinton Distinguished Professor Emeritus, Computer Science Department, University of Toronto Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978. After five years as a faculty member at Carnegie Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto where he is now an emeritus professor. Geoffrey Hinton was one of the researchers who introduced the backpropagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionized speech recognition and object classification.

The increasing attention being paid to artificial intelligence raises important questions about its integration with social sciences and humanity, according to David De Cremer, founder and director of the Centre on AI Technology for Humankind at the National University of Singapore Business School. He is the author of the recent book, Leadership by Algorithm: Who Leads and Who Follows in the AI Era? While AI today is good at repetitive tasks and can replace many managerial functions, it could over time acquire the "general intelligence" that humans have, he said in a recent interview with AI for Business (AIB), a new initiative at Analytics at Wharton. Headed by Wharton operations, information and decisions professor Kartik Hosanagar, AIB is a research initiative that focuses on helping students expand their knowledge and application of machine learning and understand the business and societal implications of AI. According to De Cremer, AI will never have "a soul" and it cannot replace human leadership qualities that let people be creative and have different perspectives. Leadership is required to guide the development and applications of AI in ways that best serve the needs of humans. "The job of the future may well be [that of] a philosopher who understands technology, what it means to our human identity, and what it means for the kind of society we would like to see," he noted. An edited transcript of the interview appears below. AI for Business: A lot is being written about artificial intelligence. What inspired you to write Leadership by Algorithm?

Can broader datasets help developers avoid accidentally perpetuating deep-rooted biases in vital institutions like healthcare and education? AI in healthcare has a bias problem. Last year, it came to light that six algorithms used on an estimated 60-100 million patients nationwide were prioritizing care coordination for white patients over black patients for the same level of illness. The algorithm was trained on costs in insurance claims data, predicting which patients would be expensive in the future based on who was expensive in the past. Historically, less is spent on black patients than white patients, so the algorithm ended up perpetuating existing bias in healthcare.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. Every week, the crew responds to a bonus question in chat form. I recently met a guy on Tinder, where I usually don't have much luck because I'm not conventionally attractive and want to date, not just hook up. But after talking to this guy for a few days we seem practically perfect for each other!

Today is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines. Obtaining the right information at the right time and place is the key to effective disaster management. The term "disaster management" encompasses both natural and human-caused disasters. To assist citizens, our project is to create a COVID Assistant to provide the need of up to date information to be available 24 hours. With the growth in the World Wide Web, it is quite intelligible that users are interested in the swift and relatedly correct information for their hunt. A chatbot can be seen as a question-and-answer system in which experts provide knowledge to solicit users. This master thesis is dedicated to discuss COVID Assistant chatbot and explain each component in detail. The design of the proposed chatbot is introduced by its seven components: Ontology, Web Scraping module, DB, State Machine, keyword Extractor, Trained chatbot, and User Interface.

As vice president of innovation at logistics company DHL, Gina Chung oversees a 28,000-square-foot innovation facility in Chicago. Fascinated with supply chains since college ("I think it's something to do with the fact that I'm from New Zealand and grew up in a pretty isolated part of the world," she explains), she spearheads AI and robotics projects focused on front-line operations -- like automated pallet inspection and stacking, delivery route optimization, and aircraft utilization. Your reviews are essential to the success of Me, Myself, and AI. For a limited time, we're offering a free download of MIT SMR's best articles on artificial intelligence to listeners who review the show. Send your review screenshot to smrfeedback@mit.edu to receive the download. Gina Chung is vice president, Innovation Americas, at DHL, where she is responsible for DHL's Americas Innovation Center, a purpose-built platform to engage customers, startups, and industries on the future of logistics. She manages a portfolio of projects focused on the rapid testing and adoption of technologies such as collaborative robotics and artificial intelligence across logistics operations. Gina notes that "the first day for AI is the worst day": The technology improves with human input over time, achieving accuracy to a level where people trust and embrace it. She describes how success requires closely collaborating with key stakeholders, integrating change management, bringing teams along when introducing new technology, and designing solutions with the end user in mind.