Millions of patients live in rural or developing areas where disease treatment isn't the problem: it's overcoming the 1:1,000 doctor to patient ratio and screening for preventable diseases. High school student Kavya Kopparapu shares the future of artificial intelligence in this field-- as a substitute for doctors in these areas to deliver much-needed medical diagnoses. Kavya Kopparapu is the Founder and CEO of GirlsComputingLeague and current junior at Thomas Jefferson High School for Science and Technology. She is dedicated to sharing her passion for computer science with others, especially young girls, as the field has given her a world of opportunity, and has been recognized by organizations such as the White House and the National Center for Women in Information Technology (NCWIT). Her journey with computer science began in elementary school, when she was introduced to the Scratch programming language and developed robots using the Mindstorms programming language.

The Jupyter Notebook (http://jupyter.org/) is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and explanatory text. Jupyter Notebook is a popular tool for scientists and students performing data analysis and visualization. It is also useful for instructors wishing to build interactive tutorials for students, since Jupyter Notebooks can be converted to slide shows with live code execution. Initially, Jupyter Notebook included built-in support for the Julia, Python, and R programming languages, but it now includes an interface for supporting other languages as well. The NCAR Command Language (NCL) is an interpreted language designed specifically for scientific data analysis and visualization.

This is a contributed article by Irina Raicu, the director of the Internet Ethics program at the Markkula Center for Applied Ethics. "Sometimes a type of glory lights up the mind of a man," writes John Steinbeck in his novel "East of Eden," which is set in a California valley -- Salinas, though, not Silicon. "It happens to nearly everyone. You can feel it growing or preparing like a fuse burning toward dynamite. Okay, but what does that have to do with artificial intelligence? I don't know how it will be in the years to come. There are monstrous changes taking place in the world, forces shaping a future whose face we do not know. Some of these forces seem evil to us, perhaps not in themselves but because their tendency is to eliminate other things we hold good. That line finds an echo in our times. Various ethicists are writing, these days, about the concerns that AI might eliminate some things "we hold good" -- and not just meaning "jobs." They write, for example, about the threat of "moral de-skilling" in the age of algorithmic decision-making. About what might be lost or diminished by the advent of robot caretakers. About what role humans will play, in general, in an age of machine learning and neural networks making so many of the decisions that shape human lives. "It is true," Steinbeck writes, A group can build automobiles quicker and better than one man, and bread from a huge factory is cheaper and more uniform. When our food and clothing and housing all are born in the complication of mass production, mass method is bound to get into our thinking and to eliminate all other thinking. We are in the process of shifting from the kind of mass production that Steinbeck talked about to a kind of mass production that requires much less human involvement. If "mass method" was bound to get into our thinking back then, how is it shaping our thinking now? Is this what the current focus on data collection and analysis of patterns is about? "In our time," adds Steinbeck, This in my time is the danger. There is great tension in the world, tension toward a breaking point, and men are unhappy and confused. In our own time, AI is spreading into all the various spheres of our lives, and there is tension and great concern about its impact. We are confused by dueling claims that AI will eliminate jobs or create new ones; that it will eliminate bias or perpetuate it and make it harder to identify; that it will lead us to longer, happier lives -- or to extinction. "At such a time," writes Steinbeck's narrator, "it seems natural and good to me to ask myself these questions.

In this Learning Plan, instructor Ted Hills identifies the goals of Data Modeling and prepares you for new concepts using Concept and Object Modeling Notations (COMN). Within the six-course program, you will learn how to represent real-world models based on COMN, model objects within a computer's memory or storage, and dive into modeling requirements and their expressions in data. By the end of this program, you will understand the differences between SQL and NoSQL and between ACID and BASE, and how to model each in a data-centric organization, whether using a document database or data warehouse. Enroll in one of these individual courses or purchase the entire Learning Plan. We offer several bulk licensing options for corporate and group use.

Recently, I rediscovered my passion for mathematics and artificial intelligence, which I used to hate while I was getting my degree in Computer Sciences. Lately, I've been focused on software design, automated testing, microservices, and functional programming. I also love learning new programming languages, so I've been wondering whether to go deeper into Golang, Scala, or Python. But in the end, all of them are tools -- tools for building what kind of things? In my case, my day-to-day job consists in building and maintaining microservices, as a full-stack developer.

These days, I am sure 90% of LinkedIn traffic contains one of these terms: DS, ML or DL -- acronyms for Data Science, Machine Learning or Deep Learning. Beware of the cliche though: "80% of all the statistics are made on the spot". If you blinked on these acronyms perhaps you need to google a bit and then continue reading the rest of this post. This post has 2 goals. First, it attempts to put all the fellow Data Science learners at ease.

They're often depicted as being lazy, but a new study suggests that teenagers are going through one of the best times to learn a new skill. Scientists have discovered increased activity in an area of the brain called the striatum in 17-20 year-olds, which boosts the way they learn from feedback. The findings suggest that adolescence is a unique life phase for increased feedback-learning performance. The researchers studied over 230 participants aged eight to 25. Each participant completed a feedback learning task, in which good performance was rewarded with positive feedback.

Microsoft took the wraps off a language learning app it described as "an always available, artificially intelligent" assistant. The app, Microsoft Learn Chinese, uses speech and natural language processing technology to enable learners to practice speaking the language. It uses "a suite of AI tools such as deep neural networks that have been tuned…to recognise what the language learners are trying to say and evaluate the speakers' pronunciation". Users get feedback in the form of scores, along with highlighted words which need improvement and links to sample audio to hear proper pronunciation. The machine-learning and neural networks powering the service are language-independent, Microsoft said. The app was developed in the computing giant's Asia research lab in Beijing.

Second-order methods for neural network optimization have several advantages over methods based on first-order gradient descent, including better scaling to large mini-batch sizes and fewer updates needed for convergence. But they are rarely applied to deep learning in practice because of high computational cost and the need for model-dependent algorithmic variations. We introduce a variant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our method computes the curvature approximation matrix only for pairs of parameters from the same layer or block of the neural network and performs conjugate gradient updates independently for each block. Experiments on deep autoencoders, deep convolutional networks, and multilayer LSTMs demonstrate better convergence and generalization compared to the original Hessian-free approach and the Adam method.

A regularized optimization problem over a large unstructured graph is studied, where the regularization term is tied to the graph geometry. Typical regularization examples include the total variation and the Laplacian regularizations over the graph. When applying the proximal gradient algorithm to solve this problem, there exist quite affordable methods to implement the proximity operator (backward step) in the special case where the graph is a simple path without loops. In this paper, an algorithm, referred to as "Snake", is proposed to solve such regularized problems over general graphs, by taking benefit of these fast methods. The algorithm consists in properly selecting random simple paths in the graph and performing the proximal gradient algorithm over these simple paths. This algorithm is an instance of a new general stochastic proximal gradient algorithm, whose convergence is proven. Applications to trend filtering and graph inpainting are provided among others. Numerical experiments are conducted over large graphs.