University of California San Diego School of Medicine and Moores Cancer Center say they have created a new artificial intelligence (AI) system called …

The Millennium Institute for Foundational Research on Dataa (IMFD) started its operations in June 2018, funded by the Millennium Science Initiative of the Chilean National Agency of Research and Development.b IMFD is a joint initiative led by Universidad de Chile and Universidad Católica de Chile, with the participation of five other Chilean universities: Universidad de Concepción, Universidad de Talca, Universidad Técnica Federico Santa María, Universidad Diego Portales, and Universidad Adolfo Ibáñez. IMFD aims to be a reference center in Latin America related to state-of-the-art research on the foundational problems with data, as well as its applications to tackling diverse issues ranging from scientific challenges to complex social problems. As tasks of this kind are interdisciplinary by nature, IMFD gathers a large number of researchers in several areas that include traditional computer science areas such as data management, Web science, algorithms and data structures, privacy and verification, information retrieval, data mining, machine learning, and knowledge representation, as well as some areas from other fields, including statistics, political science, and communication studies. IMFD currently hosts 36 researchers, seven postdoctoral fellows, and more than 100 students.

Latin America, with its rich and varied cultural heritage, is a region widely known by its diverse musical rhythms. Indeed, music and dance constitute an important part of Latin American cultural assets and identity.2 Some of these rhythms, although famous worldwide, belong to specific regions; for example, samba is from Brazil, tango is from Argentina, merengue is from the Dominican Republic, corrido is from Mexico and vallenato is from Colombia, among many other examples. Most of them were created by the cultural interaction between people from African, Native American, and European cultures that shared their music and instruments. Those heterogeneous cultural characteristics made these music styles appealing to an international audience.

While deliberate misinformation and deception are by no means new societal phenomena, the recent rise of fake news5 and information silos2 has become a growing international concern, with politicians, governments and media organizations regularly lamenting the issue. A remedy to this situation, we argue, could be found in using technology to empower people's ability to critically assess the quality of information, reasoning, and argumentation through technological means. Recent empirical findings suggest "false news spreads more than the truth because humans, not robots, are more likely to spread it."10 Thus, instead of continuing to focus on ways of limiting the efficacy of bots, educating human users to better recognize fake news stories could prove more effective in mitigating the potentially devastating social impact misinformation poses. While technology certainly contributes to the distribution of fake news and similar attacks on reasonable decision-making and debate, we posit that technology--argument technology in particular--can equally be employed to counterbalance these deliberately misleading or outright false reports made to look like genuine news.

We live in uncertain times. A global pandemic has disrupted our lives. Our broken economies are rapidly restructuring. Climate change looms, disinformation abounds, and war, as ever, hangs over the lives of millions. And at the heart of every global crisis are the chronically underserved, marginalized, oppressed, and persecuted, who are often the first to befall the tragedies of social, economic, environmental, and technological change.3

Transaction data is like a friendship tie: both parties must respect the relationship and if one party exploits it the relationship sours. As data becomes increasingly valuable, firms must take care not to exploit their users or they will sour their ties. Ethical uses of data cover a spectrum: at one end, using patient data in healthcare to cure patients is little cause for concern. At the other end, selling data to third parties who exploit users is serious cause for concern.2 Between these two extremes lies a vast gray area where firms need better ways to frame data risks and rewards in order to make better legal and ethical choices.

CEO at Leaders Press, a USA Today best-selling press, where we turn book ideas into best-sellers. Go to Leaders Press to get started! Can artificial intelligence (AI) write a book? As the founder of a press where we offer ghostwriting services for our authors, I might feel threatened by the current capabilities of AI. AI-written novels are currently unreadable.

Warren Vickers is a man who has always marched to the beat of his own drum – quite literally.

It offers the overall analysis of the segments such as global Artificial Intelligence Application market opportunities, import/export details, market dynamics, …

Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited candidates for decentralized learning. Oftentimes, this decentralized graph support changes with time due to link failures or topology variations. These changes create a mismatch between the graphs on which GNNs were trained and the ones on which they are tested. Online learning can be used to retrain GNNs at testing time, overcoming this issue. However, most online algorithms are centralized and work on convex problems (which GNNs rarely lead to). This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms. The WD-GNN comprises two components: the wide part is a bank of linear graph filters and the deep part is a GNN. At training time, the joint architecture learns a nonlinear representation from data. At testing time, the deep part (nonlinear) is left unchanged, while the wide part is retrained online, leading to a convex problem. We derive convergence guarantees for this online retraining procedure and further propose a decentralized alternative. Experiments on the robot swarm control for flocking corroborate theory and show potential of the proposed architecture for distributed online learning.