Jeff Maggioncalda was recently named CEO of Coursera. I have interviewed both founders of the company, Andrew Ng and Daphne Koller, so I was curious about Maggioncalda's perspective on the company, education technology and the massive open online courses more generally, and his own background as an entrepreneur. Regarding the last point, Maggioncalda was previously the CEO of Financial Engines Inc, a company co-founded by economist and Nobel Prize winner William Sharpe and recently sold for $3 billion. During his 18 years as CEO of Financial Engines Inc, Maggioncalda had to pivot three times from his original idea before becoming a success. Financial Engines would go on to beocme the largest independent online retirement advice platform with more than $100b under management.

It used to be that cameras never lie. We tend to privilege visual content, trust what we see, and rely on police cams, mobile recording tools and similar devices to tell us about what is really happening on the streets, in local businesses, and more. Catherine Brooks (@catfbrooks) is an Associate Professor of Information at the University of Arizona, where she is the associate director of the School of Information and founding director of the Center for Digital Society and Data Studies. She is a Public Voices Fellow with the Op Ed Project. Take, for example, a viral video that shows a white woman calling the police as black men in Oakland attempt to barbecue. Millions are laughing, and the woman's image is being used as a meme across the Internet.

One of the behaviors considered to be uniquely human is our creativity. While many animal species create visually stunning displays or constructions -- think of a spider's delicate web or the colorful, intricate structures built by bowerbirds -- they are typically created with a practical purpose in mind, such as snagging prey or seducing a mate. Humans, however, make art for its own sake, as a form of personal expression. And as computer engineers attempt to imbue artificial intelligence (AI) with humanlike capabilities and behaviors, a question arises: Can AI create art? The AMC series "Humans," which returns June 5 for its third season, is populated by Synths -- intelligent robots that resemble people, save for their unnaturally green eyes.

Above two are intro course to deep learning. This is a Youtube channel which contains courses by Prof. Andrew Ng on various topics in deep learning. This a 14 week course, taught by Jeremy Howard. It cover most of the topics in deep learning. This course is a gentle introduction to Reinforcement Learning. It walks you through most of the topics in Reinforcement Learning in high level. This course is not taught at a basic level, so you need to be familiar with basic concepts and perhaps a little more. For more stories follow AiMantra.

You probably use machine-learning systems every day without even knowing it. The technology gives us spam filters, our Facebook News Feeds, digital assistants, search engines, Netflix picks, Amazon recommendations, fraud detection systems, chatbots and more. And it's only going to become more pervasive. For forward-looking parents, it's time to get your kids on it. Software developer and dad Dale Lane has created Machine Learning for Kids, a collection of free projects that teach students how to build with this technology.

Are you looking for game design and development courses? Here is the list of best game development courses, tutorials, training and certification for the individuals interested in becoming a game developer, game designer, game artist or a game programmer. Do you want to learn how to develop games? Then these Game Development Online Courses will show you the right path to get started. Building games is an innovative and technical art form.

Artificial Intelligence or Machine Learning, as it's better known as, is something that has garnered my interest over recent years. I even used the technology to design a company logo for a side hustle project. The expanding uses for Artificial Intelligence in business is fascinating and only becomes more exciting to see what is in store as the technology continues to mature. After being in the business for 25 years I have learnt and experienced first-hand the ever-changing landscape in which marketing continues to grow. We have seen a huge change in the industry over the last ten years, with the most prevalent and revolutionary change being the utter dependence of the online sphere. I have grown my business from strength to strength in the online realm, with platforms such as Twitter being a core tool.

Over the years Artificial Intelligence (AI) tools have been gradually getting smarter and reaching new levels of sophistication and precision which are beginning to address complex business issues. Artificial Intelligence is being deployed in multiple ways depending upon the business needs. AI manifests itself as assisted intelligence replacing the mundane and repetitive tasks being performed and provides directions or guidance. Augmented intelligence underscores the importance of man and machine working together and enabling superior decision making. Emotional intelligence, creativity and innovation that humans possess when combined with the ability of the machines to crunch enormous datasets and provide predictive analytics to define probability of occurrence of certain events lead to higher order outcomes.

Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that our proposed metric learning approach improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.

The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language. Large datasets for Machine Reading have led to the development of neural models that cater to deeper language understanding compared to information retrieval tasks. Different components in these neural architectures are intended to tackle different challenges. As a first step towards achieving generalization across multiple domains, we attempt to understand and compare the peculiarities of existing end-to-end neural models on the Stanford Question Answering Dataset (SQuAD) by performing quantitative as well as qualitative analysis of the results attained by each of them. We observed that prediction errors reflect certain model-specific biases, which we further discuss in this paper.