Humans gather information by engaging in conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong conversational and reading comprehension models on CoQA. The best system obtains an F1 score of 65.1%, which is 23.7 points behind human performance (88.8%), indicating there is ample room for improvement. We launch CoQA as a challenge to the community at http://stanfordnlp.github.io/coqa/

Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and passages to predict corresponding answers. However, despite intense interest in the topic, with hundreds of published papers vying for leaderboard dominance, basic questions about the difficulty of many popular benchmarks remain unanswered. In this paper, we establish sensible baselines for the bAbI, SQuAD, CBT, CNN, and Who-did-What datasets, finding that question- and passage-only models often perform surprisingly well. On $14$ out of $20$ bAbI tasks, passage-only models achieve greater than $50\%$ accuracy, sometimes matching the full model. Interestingly, while CBT provides $20$-sentence stories only the last is needed for comparably accurate prediction. By comparison, SQuAD and CNN appear better-constructed.

There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.

Higher education has undergone a palpable change in the past decades with the appearance of social media, smartphones and various other technologies on the learning landscape. Artificial Intelligence is one of the key technologies in bringing about this educational overhaul. With its arrival on the scene, we are constantly moving towards smart, personalized automation which promotes productive learning behaviors. A report by the research firm Technavio states that the global market for artificial intelligence in the higher education sector is projected to grow at a CAGR (compound annual growth rate) of 39% by 2020! With the rising infiltration of Artificial Intelligence technologies in all walks of life, it is only normal that education followed suit.

IBM explained on RPI's website that the lab's overall mission is to "explore and advance natural, collaborative problem-solving among groups of humans and machines." The lab is built around a futuristic "situations room" that can be adapted to industry-specific environments, and is housed on the Rensselaer campus in the Curtis R. Priem Experimental Media and Performing Arts Center. Educause reported recently that use of AI will increase on college campuses over the next few years, though experts said colleges sometimes have been slow to adopt such advanced technologies. Some experts say it can be used particularly effectively in STEM classes where it might be better suited for delivering the content, but others argue that it is also help in teaching students writing, where it can initially guide them, offer prompts and even assess their work. AI systems also might allow some instructors more time for individual interaction with students because it will arm them with more information about their performance as a group and individually, thus helping instructors adjust their teaching and assessments.

There is a strong and natural relationship between robots and rubber duckies. Being small, cheap, colorful, and pleasingly compliant, duckies became a sort of physical Stanford Bunny--when you want to show the scale of a robot, or give a robot something to visually locate or grasp or something, just toss a duckie in there. This relationship was formalized through the 2016 ICRA conference, where duckies inspired a bunch of videos and some poetry that is surprisingly not terrible. Since then, duckies have been taking over in robotics--at this point, I'm fairly certain that Andrea Censi at ETH Zurich is held hostage by (and doing the bidding of) a small army of little yellow duckies. This would explain why an entire duckie village full of duckie-sized autonomous cars that you can learn how to program is now on Kickstarter, with the hope that you'll help them take over the entire world.

Today, I am going to deliver some kind of educational content. If you are still unaware about the advantages that Skychain has over the competitors, this presentation material is for you. I would like to start by underlining the key idea of our project once again -- bringing the key players together within blockchain infrastructure. The whole message is being a cornerstone since the conception of the project. But what is so important and unique in doing it?

Minecraft: Education Edition is heading to the iPad and educators will have access to it starting next month. The education version of the game launched in 2016 and Microsoft says there are now 35 million licensed users in 115 countries. "Minecraft: Education Edition on iPad unlocks new and intuitive ways of collaborating and sharing and has revolutionized the way our students and teachers explore curriculum and projects," Kyriakos Koursaris, head of education technology for PaRK International School, said in a statement. "The features allow for deep and meaningful learning, and the values it promotes, from inclusivity to 21st century skills, empower everyone to use technology with extraordinary results," said Koursaris. Minecraft: Education Edition for iPad will be available in September and comes with a Microsoft 365 for Education license.

Recent research shows that the customer experience is still growing in importance for consumers when it comes to making their purchasing decisions. Many organisations are struggling to deliver a positive CX as they deal with more contact channels than ever before. Fortunately, there is a powerful new CX approach to help them build better relationships with customers: combining artificial intelligence and humans. Join Creative Virtual for an inside look at bringing together AI and human input to provide quality self-service options, improve live chat and contact centre performance, and increase customer satisfaction scores. Through a series of live demonstrations, you'll learn: Register now to see how organisations around the globe are already using the combination of humans and AI to provide perfect customer service and help them deliver a positive CX.

Proponents say that professors freed from routine tasks by AI technology can further challenge students to get a deeper understanding of material and assess them more accurately. For instance, adaptive courseware allows a professor to use a dashboard to assess how students are doing with homework and quizzes, check their mastery of the work and then perhaps offer a weekly lesson to fill gaps. Beyond that, researchers are developing technology that can provide study tips based on student behavior and work, or even guide teachers to provide teachers with feedback. Grades, researchers say, don't change much when AI is used. Nevertheless, a 2018 Gallup-Northeastern University survey shows that of 3,297 U.S. citizens interviewed, only 22% with a bachelor's degree said their education left them "well" or "very well prepared" to use AI in their jobs.