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A 20-Year Community Roadmap for Artificial Intelligence Research in the US

arXiv.org Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.


Neural Educational Recommendation Engine (NERE)

arXiv.org Machine Learning

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.


Software Called Aristo Can Take on High School Science Exams

AITopics Original Links

During which season of the year would a rabbit's fur be thickest? A computer program called Aristo can tell you because it read about bears growing thicker pelts during winter in a fourth-grade study guide, and it knows rabbits are mammals, too. Aristo is being developed by researchers at the Allen Institute for Artificial Intelligence in Seattle, who want to give machines a measure of common sense about the world. The institute's CEO, Oren Etzioni, says the best way to benchmark the development of their digital offspring is to use tests designed for schoolchildren. He's trying to convince other AI researchers to adopt standardized school tests as a way to measure progress in the field.


Web-STAR: A Visual Web-Based IDE for a Story Comprehension System

arXiv.org Artificial Intelligence

We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation. The platform includes a web-based IDE, integration with the STAR system, and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features, including a community repository for public story sharing with a built-in commenting system, and tools for collaborative story editing that can be used for team development projects and for educational purposes.


Will robots take your job before you even graduate?

#artificialintelligence

With each passing year, parents are getting more worried about how their children will fare once it's time to take that step from school to the workforce. They have good reason to fret. Some 17 million Americans under age 30--about one third of the under-30 population--are saddled with student debt. Many are worried about their career prospects despite having invested--heavily, in some cases--in education. The cost of college is being hotly debated.