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New tools designed to help institutions meet accessibility requirements stand to personalize learning for all students. Artificial intelligence (AI) has seeped into almost every corner of higher education, popping up in the classroom, administrative offices, and even in dorm rooms and on campus grounds -- all with the promise to streamline tasks and create a more personalized college experience for students. As the technology steers colleges away from a one-size-fits-all approach, it is helping them make progress on one of their most long-running goals: making higher ed more accessible to all types of learners. It is doing that in several ways. Among them, by scanning class materials for accessibility issues, improving learning tools for students with disabilities and offering personalized resources for learners who may need additional support, such as those who speak English as a second language.

In 2010, US authors in top-rated AI journals outnumbered Chinese counterparts by two to one. That ratio has now reversed. Last year, 1,073 AI experts based at Chinese universities were credited in AI journals such as the Institute of Electrical and Electronics Engineers's Transactions on Neural Networks, compared to 492 US authors. Australia and Israel also do well on this metric. When experts are ranked according to their'H-index' – a metric of productivity and the citation impact of the publications of a scientist or scholar – Americans occupy 626 of the 1,000 top spots, including all of the top ten spots at the time of our analysis. New Zealand, Saudi Arabia and Finland's AI academics are also highly ranked.

New Delhi: Silicon Valley-based learning platform Udacity with respect to Monday said it intends to double the headcount at its New Delhi office to help its growth student and enterprise customer base in India. The organization trains laborers on cutting edge abilities, for example, Artificial Intelligence, Machine Learning, robotization, profound learning, information investigation, through its Nanodegree programs. "India is quick ascending as a conspicuous advanced first economy. Because of this quick digitisation crosswise over ventures, there is a flooding interest for new-age experts capable in cutting edge innovations, for example, AI, AI, mechanization, profound learning, information investigation, and so forth.," Udacity CEO Gabriel Dalporto said in an announcement.

Online learning is a powerful tool for analyzing iterative algorithms. However, the classic adversarial setup sometimes fails to capture certain regularity in online problems in practice. Motivated by this, we establish a new setup, called Continuous Online Learning (COL), where the gradient of online loss function changes continuously across rounds with respect to the learner's decisions. We show that COL covers and more appropriately describes many interesting applications, from general equilibrium problems (EPs) to optimization in episodic MDPs. Using this new setup, we revisit the difficulty of achieving sublinear dynamic regret. We prove that there is a fundamental equivalence between achieving sublinear dynamic regret in COL and solving certain EPs, and we present a reduction from dynamic regret to both static regret and convergence rate of the associated EP. At the end, we specialize these new insights into online imitation learning and show improved understanding of its learning stability.

Online learning to rank (LTR) focuses on learning a policy from user interactions that builds a list of items sorted in decreasing order of the item utility. It is a core area in modern interactive systems, such as search engines, recommender systems, or conversational assistants. Previous online LTR approaches either assume the relevance of an item in the list to be independent of other items in the list or the relevance of an item to be a submodular function of the utility of the list. The former type of approach may result in a list of low diversity that has relevant items covering the same aspects, while the latter approaches may lead to a highly diversified list but with some non-relevant items. In this paper, we study an online LTR problem that considers both item relevance and topical diversity. We assume cascading user behavior, where a user browses the displayed list of items from top to bottom and clicks the first attractive item and stops browsing the rest. We propose a hybrid contextual bandit approach, called CascadeHybrid, for solving this problem. CascadeHybrid models item relevance and topical diversity using two independent functions and simultaneously learns those functions from user click feedback. We derive a gap-free bound on the n-step regret of CascadeHybrid. We conduct experiments to evaluate CascadeHybrid on the MovieLens and Yahoo music datasets. Our experimental results show that CascadeHybrid outperforms the baselines on both datasets.

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