Social media streams contain large and diverse amount of information, ranging from daily-life stories to the latest global and local events and news. Twitter, especially, allows a fast spread of events happening real time, and enables individuals and organizations to stay informed of the events happening now. Event detection from social media data poses different challenges from traditional text and is a research area that has attracted much attention in recent years. In this paper, we survey a wide range of event detection methods for Twitter data stream, helping readers understand the recent development in this area. We present the datasets available to the public. Furthermore, a few research opportunities

As the Trump administration continues to advance its hardline stance towards immigration, legal or otherwise, businesses are increasingly turning to automation and robotics to fill jobs previously held by humans. However, these thinking machines are not without drawbacks. AI development has long been beset by issues of intrinsic training bias, if not outright racist and xenophobic behavior. Take Microsoft's aborted social media bots Tay and Zo, for example, or Amazon's questionably-designed facial recognition system. However this relationship is not unidirectional -- AI can impact the expression of xenophobic ideas just as xenophobic practices can impact the rate of AI development.

Artificial intelligence is no different than the cotton gin, telecommunication satellites or nuclear power plants. It's a technology, one with the potential to vastly improve the lives of every human on Earth, transforming the way that we work, learn and interact with the world around us. But like nuclear science, AI technology also carries the threat of being weaponized -- a digital cudgel with which to beat down the working class and enshrine the current capitalist status quo. Just look at how Amazon's automated facial recognition system is being marketed to law enforcement and government agencies, despite its obvious racial biases, or Wisconsin's automated sentencing tool, Compas, which determines a defendant's prison time via a proprietary and secret algorithm. It just so happens to sentence black and brown defendants to longer terms than their white counterparts for similar crimes.

Professor of Robotics at Carnegie Mellon University Illah Nourbakhsh says that since AI is rapidly evolving, we need more rapid responses to deal with its risks. "The real challenge is considering policy moves to maximize the good and minimize the bad," Nourbakhsh says. "Just as human scam artists find ever more sophisticated and nuanced ways to trick people out of their money using online scams, so AI-powered malicious actors will continuously find new pathways into our data and into our pocketbooks."

However, technologies that mimic social cues and dialogue can already be seen today, Riedel said. "What the AI looks like in'Westworld' is in some ways what game companies are trying to do in virtual worlds," Riedel said. In the show "Westworld," the hosts are given pre-installed storylines that are triggered when a person interacts with them. It's similar to how game mechanics work in action role-playing video games like "Borderlands," Riedel said. Still, "Westworld's" missions are much more complex than what our current technology enables, he said.

Self-driving cars and robots that can zoom on their own around warehouses are just some of what's possible because of artificial intelligence. But expect unforeseen consequences if researchers ignore the inherent ethical dilemmas in the emerging technology. That's one of the takeaways from a panel about AI ethics and education in San Francisco that was hosted by the Future of Life Institute, a research group focused on preventing societal problems created by the technology. Although humans typically program AI-powered robots to accomplish a particular goal, these robots will typically make decisions on their own to reach the goal, explained Benjamin Kuipers, a computer science professor and AI researcher at the University of Michigan. Get Data Sheet, Fortune's technology newsletter.

Twitter has become an important online source for real-time news dissemination. Especially, official accounts of local government and media outlets have provided newsworthy and authoritative information, revealing local trends and breaking news. In this paper, we describe TipMaster an automatically constructed knowledge base of Twitter accounts that are likely to report local news, from government agencies to local media outlets. First, we implement classifiers for detecting these accounts by integrating heterogeneous information from the accounts' textual metadata, profile images, and their tweet messages. Next, we demonstrate two use cases for TipMaster: 1) as a platform that monitors real-time social media messages for local breaking news, and 2) as an authoritative source for verifying nascent rumors. Experimental results show that our account classification algorithms achieve both high precision and recall (around 90%). The demonstrated case studies prove that our platform is able to detect local breaking news or debunk emergent rumors faster than mainstream media sources.

Turning to align itself with the hallway, it begins to move toward the near door of the goal room, which is just a few feet in front. This run should be easy, so the robot thinks. DERVISH plans to use another hallway. DERVISH's brain is an on-board MACINTOSH Later, when the robot finally reaches the node just outside the goal room, the enterroom module is called. This simple procedure aligns the robot with the doorway and then moves a prespecified distance into the room.

Czerwinski, Mary, see Nguyen, Trung 1992 AAAI Robot Exhibition and Competition see Dean, Thomas 1992 Workshop on Design Rationale Capture and Use, The, see Lee, Jintae Advances in Real-Time Expert System Technologies, see Barachini, Franz AI and Creativity: 1993 Spring Symposium Report, see Kim, Steven AI and N&Hard Problems: 1993 Spring Symposium Report, see Crawford, James AI Research and Application Development at Boeing's Huntsville Laboratories see Tanner, Steve Anick, Peter; and Simoudis, Evange-10s. Agent Architectures, see Hanks, Steve Berman, Jay I. see Wright, Jon R. Bonasso, R. Peter see Dean, Thomas Bookman, Lawrence, see Sun, Ron Brown, Karen E. see Wright, Jon R. Building Lexicons Two Winner see Congdon, Clare Carnes, Ray, see Tanner, Steve Case-Based Reasoning and Information Retrieval: 1993 Spring Symposium Report, see Anick, Peter Chandrasekaran, B.; Narayanan, N. Hari; and Iwasaki, Yumi. Charniak, Eugene, see Goldman, Robert l? Chien, Steve, see Gat, Erann. Cohen, Paul R., see Hanks, Steve Compaq Quicksource: Providing the Consumer with the Power Drummond, Mark, see Lansky, Amy Engineering Design through Constraint-Based Reasoning, see Murtagh, Niall Etzioni, Oren. Goal-Driven Learning: Fundamental Issues: A Symposium Report, see Leake, David Goldman, Robert l?; Charniak, Eugene; Gale, William; and Norvig, Peter.

Mark Waser, for instance, a longtime artificial intelligence researcher who works for a think tank called the Digital Wisdom Institute, says that once we fix some key kinks in artificial intelligence, robots will make much better decisions than humans can. Another big technical problem to solve before computers could run the country: Robots don't know how to explain themselves. In an approach called machine learning, the computer analyzes mountains of data and searches for patterns--patterns that might make sense to the computer but not to humans. In a variant approach called deep learning, a computer uses multiple layers of processors: One layer produces a rough output, which is then refined by the next layer, and that output, in turn, is refined by the next layer.