The Strategic Data Project (SDP), an initiative of the Center for Education Policy Research at Harvard University, is offering a one-year Senior Data Fellow position starting in the summer of 2021. The fellow will work at a US Federal Agency in Washington, DC and engage in research projects related to the department's focus. Federal research leaders are working to realize the potential of previously untapped data collected across multiple federal agencies. To carry out the work of bringing together new data sets and tools and publishing novel insights, SDP will host the inaugural cohort of post-doctoral and senior level data fellows to work with federally collected education data and to connect those data across departments to generate new insights about the health, well-being, and achievement of students. The Senior Data Fellow will have the opportunity to work with Harvard faculty advisors focused on education policy research, such as Tom Kane and Chris Avery.

Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and sparsity of the data. The local and global structure of the real-world networks can be reflected by dynamical transfer behaviors among nodes. This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models. We first design a degree-weight biased random walk model to capture the transfer behaviors on the network. Then a deep network embedding method is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into conventional deep prediction models, including Long Short-Term Memory Network and Recurrent Neural Network, to utilize the sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various datasets including social networks, citation networks, biomedical network, collaboration network and language network. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization, classification, reconstruction and link prediction, and achieve promising performance compared with state-of-the-arts.

Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset. To study the reason behind the low performance and to further our understandings of these tasks, we design controlled experiments on both input data and network designs. Guided by the hindsight from these experiment results, we propose a simple contrastive learning approach along with other network modifications to improve the baseline performance. Our approach uses a self-supervised binary classification network to compare the line drawing differences between various views of any two similar 3D objects. It enables deep networks to effectively learn detail-sensitive yet view-invariant line drawing representations of 3D objects. Experiments show that our method could significantly increase the baseline performance in SPARE3D, while some popular self-supervised learning methods cannot.

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data. Although it is recognized that statistical heterogeneity of the client local data yields slower global model convergence, it is less commonly recognized that it also yields a biased federated global model with a high variance of accuracy across clients. In this work, we aim to provide federated learning schemes with improved fairness. To tackle this challenge, we propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server). Empirical results on a suite of datasets demonstrate the effectiveness of our methods on simultaneously improving the test accuracy and fairness.

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According to a research report, artificial intelligence in the global education market is projected to reach USD3.68 billion by 2023, registering a CAGR of 47%. The role of AI in education is huge and imperative in the current scenario. AI along with other disruptive technology has given rise to EdTech and smart learning methods. It has now entered into another significant area, which is Intelligent Tutoring. Intelligent tutoring systems, as the name suggests is an intelligent computer system that can effectively provide instructions to the learners and enables a feedback system with minimal human intervention.

Between the disruption to foreign manufacturing and its planned German silicon facility, much of the recent focus has been on Apple's overseas supply chain. But, amid a push to re-start the US economy, Apple is now highlighting its domestic contributions. The iPhone-maker announced today that it will open a new campus and engineering hub in North Carolina, as part of a five-year plan to pour $430 billion into the US economy. Apple will spend $1 billion on the new site located in the Research Triangle area, home to a trifecta of higher-education institutions including North Carolina State University, Duke University and the University of North Carolina. The project will see Apple employ 3,000 people in machine learning, artificial intelligence and software engineering posts.

In the series, we considered both the teaching of AI and machine learning itself, and the use of AI techniques to improve education in general. You can also find out more about conferences and events, and other interesting research at the intersection of AI and education. There are a number of conferences and workshops that focus on the education side of AI. In our focus series we heard from the co-chairs of the Symposium on Educational Advances in Artificial Intelligence (EAAI), which was held in February this year. This event is held as an independent symposium within the AAAI conference, and provides the opportunity for researchers, educators, and students to share educational experiences involving AI.

In the past few years, Python has become the preferred programming language for machine learning and deep learning. Most books and online courses on machine learning and deep learning either feature Python exclusively or along with R. Python has become very popular because of its rich roster of machine learning and deep learning libraries, optimized implementation, scalability, and versatile features. But Python is not the only option for programming machine learning applications. There's a growing community of developers who are using JavaScript to run machine learning models. While JavaScript is not a replacement for the rich Python machine learning landscape (yet), there are several good reasons to have JavaScript machine learning skills.

The recent analysis of Machine Learning in Education market size has been methodically put together to impart an in-depth understanding of the key …