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DURHAM – The National Science Foundation has awarded Duke University a $3 million, five-year Research Traineeship grant to develop a program for graduate students to develop expertise in using artificial intelligence (AI) for materials science research. The aiM (AI for Understanding and Designing Materials), program will fill a vital workforce gap by training the next generation in the new convergent field of materials and computer science research. "To achieve the promise of the U.S. Materials Genome Initiative of accelerated discovery, design and application of new materials, we must integrate the traditional tools of experimentation, theory and computation with the emerging tools of data science to transform the way we approach materials understanding and discovery," said Cate Brinson, chair of the Department of Mechanical Engineering & Materials Science and director of aiM. The Materials Genome Initiative (MGI), launched in 2011, is a multi-agency federal government effort to accelerate the development and deployment of new, advanced materials to address a host of challenges in clean energy, national security, health and welfare. "The MGI promoted a paradigm shift from slow individual experiments and computation to the beginnings of data-driven AI approaches in materials science research," added Brinson.

How is AI Ethics and Responsible AI currently being taught in Computer Science and Engineering Curriculums across Africa? What issues related to this topic are relevant to students and faculty? And what roadblocks or challenges are instructors facing to bring more discussion of AI ethics to classrooms? The goal of this workshop is to foster a discussion on how to effectively integrate AI Ethics into Computer Science/Engineering programs at African Universities. This is an initial step to gather perspectives on the current situation at representative universities in different countries in Africa, and to initiate a discussion on how we can better support each other with lessons learned and share materials/curriculums to further develop AI ethics programs in higher education. After identifying the current state, the interests of students and faculty and the needs of departments in this workshop session, the goal is to continue the series with more in-depth workshops on specific topics.

Because this year's UseR 2020 in Munich couldn't happen as an in-person event, I will be giving my workshop on Deep Learning with Keras and TensorFlow as an online event on You can register for FREE via Eventbrite. Deep learning is an artificial intelligence that mimics the workings of a human brain in processing different data, creating patterns and interpreting information that is used for decision making. It is a subfield of machine learning in artificial intelligence and Its networks has the capability to learn, supervised or unsupervised, from data that is either structured or labelled. It is one of the hottest trends in machine learning at the moment and there are many problems where deep learning shines, such as Self Driving Cars, Natural Language Processing, Machine Translations, image recognition and Artificial Intelligence (AI) and so on.

Grab a copy of The Elements of Statistical Learning ("the machine learning bible") and you might be a little overwhelmed by the mathematics. For example, this equation (p.34), for a cubic smoothing spline, might send shivers down your spine if math isn't your forte: In order to grasp that equation, nested firmly in the "Introductory" section of the book, you need to know function notation, sigma (summation) notation, derivatives, and Greek letters. Basically, if you haven't taken a calculus class, you're not going to be able to follow along. But, do you really need to know all of that math to grasp the fundamentals of ML? An Introduction to Statistical Learning covers much of the same material, but in a less mathematical way.

So you're new to coding, and you're not quite sure whether Python is the right programming language for you to learn? If that sounds familiar, you're in the right place. In this article, I will walk you through the most significant advantages of Python compared to other popular programming languages. You will learn why Python can be an excellent tool to add under your belt. We won't just focus on the lucrative career opportunities Python can offer. We'll also look at things that affect your learning experience as a beginner.

For non-native speaking English students, trying to get good grades while learning a new language can be challenging at the best of times, but as classes turn virtual some students are being left behind. BUCKEYE, Az. -- Virtual classrooms are the new normal for many students, but for non-native speaking English students, trying to get good grades can be challenging in the best of times. As classes turn virtual due to COVID-19, some students are being left behind. Valeria Gonzalez, 11, told Fox News that her school in Buckeye, Az., doesn't offer a virtual English as a second language (ESL) program. All of her classes are taught by an English speaking teacher with no Spanish translation.

This paper focuses on the problem of unsupervised relation extraction. Existing probabilistic generative model-based relation extraction methods work by extracting sentence features and using these features as inputs to train a generative model. This model is then used to cluster similar relations. However, these methods do not consider correlations between sentences with the same entity pair during training, which can negatively impact model performance. To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information. Given multiple sentences with the same entity pair as inputs, self-supervised learning is deployed by predicting the shortest path between entity pairs on the dependency graph of one of the sentences. After that, we extract the relation information using the well-trained encoder. Then, entity pairs that share the same relation are clustered based on their corresponding relation information. Each cluster is labeled with a few words based on the words in the shortest paths corresponding to the entity pairs in each cluster. These cluster labels also describe the meaning of these relation clusters. We compare the triplets extracted by our proposed framework (CURE) and baseline methods with a ground-truth Knowledge Base. Experimental results show that our model performs better than state-of-the-art models on both New York Times (NYT) and United Nations Parallel Corpus (UNPC) standard datasets.

The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to outputs. This limits their applicability to more dynamic situations, where input to output mapping may change with different contexts. A salient example is continual learning - learning new independent tasks sequentially without forgetting previous tasks. Continual learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby a previously learned mapping of an old task is erased when learning new mappings for new tasks. Here, we propose a new biologically plausible type of deep neural network with extra, out-of-network, task-dependent biasing units to accommodate these dynamic situations. This allows, for the first time, a single network to learn potentially unlimited parallel input to output mappings, and to switch on the fly between them at runtime. Biasing units are programmed by leveraging beneficial perturbations (opposite to well-known adversarial perturbations) for each task. Beneficial perturbations for a given task bias the network toward that task, essentially switching the network into a different mode to process that task. This largely eliminates catastrophic interference between tasks. Our approach is memory-efficient and parameter-efficient, can accommodate many tasks, and achieves state-of-the-art performance across different tasks and domains.

Offered through a collaboration with Microsoft, this microcredential will teach you the fundamentals of AI and provide you with the skills to design and build an AI solution using Microsoft Azure. We will prepare you for the Microsoft Azure Fundamentals (AZ-900) and Microsoft Azure AI Engineer Associate (AI-100) certification; the cost of this microcredential includes vouchers for those exams. Artificial intelligence is one of the key drivers of the Fourth Industrial Revolution. Accordingly, artificial intelligence skills are frequently listed among the most in-demand workplace skills in the current and future job market, as organisations seek to harness AI to revolutionise their operations. While in-demand tech skills are changing, employers are faced with a shortfall of qualified candidates.