Samsung has opened another artificial intelligence (AI) centre, this time in Montreal, Canada. It is Samsung's seventh AI centre in total since it set up its first in Seoul in November last year. It also marks its fourth in North America after Silicon Valley, New York, and Toronto. The city hosted leading AI researchers at McGill University and the University of Montreal who have had "longstanding relationships" with Samsung, with 250 researchers and 9,000 students in related programs. The South Korean tech giant said the Montreal centre would focus on machine learning, language, vision, and multi-modal interactions.
With advances in Artificial Intelligence in Education (AIEd) and the ever-growing scale of Interactive Educational Systems (IESs), data-driven approach has become a common recipe for various tasks such as knowledge tracing and learning path recommendation. Unfortunately, collecting real students' interaction data is often challenging, which results in the lack of public large-scale benchmark dataset reflecting a wide variety of student behaviors in modern IESs. Although several datasets, such as ASSISTments, Junyi Academy, Synthetic and STATICS, are publicly available and widely used, they are not large enough to leverage the full potential of state-of-the-art data-driven models and limits the recorded behaviors to question-solving activities. To this end, we introduce EdNet, a large-scale hierarchical dataset of diverse student activities collected by Santa, a multi-platform self-study solution equipped with artificial intelligence tutoring system. EdNet contains 131,441,538 interactions from 784,309 students collected over more than 2 years, which is the largest among the ITS datasets released to the public so far. Unlike existing datasets, EdNet provides a wide variety of student actions ranging from question-solving to lecture consumption and item purchasing. Also, EdNet has a hierarchical structure where the student actions are divided into 4 different levels of abstractions. The features of EdNet are domain-agnostic, allowing EdNet to be extended to different domains easily. The dataset is publicly released under Creative Commons Attribution-NonCommercial 4.0 International license for research purposes. We plan to host challenges in multiple AIEd tasks with EdNet to provide a common ground for the fair comparison between different state of the art models and encourage the development of practical and effective methods.
When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. Bestseller 26,813 students enrolled Created by Lazy Programmer Inc Apply gradient-based supervised machine learning methods to reinforcement learning Understand reinforcement learning on a technical level Understand the relationship between reinforcement learning and psychology Implement 17 different reinforcement learning algorithms Calculus (derivatives) Have experience with at least a few supervised machine learning methods Good object-oriented programming skills When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible.
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