Artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other. Andrew Ng, chief scientist at Chinese internet giant Baidu Inc. and co-founder of education startup Coursera, and Neil Jacobstein, chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University, sat down with The Wall Street Journal's Scott Austin to discuss AI's opportunities and challenges. What is Baidu focused on? NG: For large enterprises like Baidu, AI creates two big pockets of opportunities. One is our core business.

For those of you who have an interest, and or involvement in "Deep Learning" or want to learn more I've created this matrix. It's by no means all inclusive. It will provide you with a landscape of some Deep Learning resources to get you started or complement resources you might already have. The original version is available here as a 5-page PDF document. You can click on the 5 images below to zoom in.

Standardized testing is also expensive and time-consuming. On the other hand, we should expect some sort of accountability in education, right? Schools are expensive, and, as new industries demand more educated workers, the stakes are higher than ever when it comes to the global economy and class mobility. Developed economies no longer have the safety net of middle-class manufacturing jobs. Whatever Trump says, that's permanent.

While participating in Jeremy Howard's excellent deep learning course I realized I was a little rusty on the prerequisites and my fuzziness was impacting my ability to understand concepts like backpropagation. I decided to put together a few wiki pages on these topics to improve my understanding. Here is a prettier version of my linear algebra page. In the context of deep learning, linear algebra is a mathematical toolbox that offers helpful techniques for manipulating groups of numbers simultaneously. It provides structures like vectors and matrices (spreadsheets) to hold these numbers and new rules for how to add, subtract, multiply, or divide them.

This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest neighbor method. In this paper, we propose strengthening the recognition performance of these models as well as their ability to capture structural variation. The proposed method constructs a large number of graph models and trains decision trees using the models. This paper makes two main contributions. The first is a novel graph model that can quickly perform calculations, which allows us to construct several models in a feasible amount of time. The second contribution is a novel approach to structural data recognition: graph model boosting. Comprehensive structural variations can be captured with a large number of graph models constructed in a boosting framework, and a sophisticated classifier can be formed by aggregating the decision trees. Consequently, we can carry out structural data recognition with powerful recognition capability in the face of comprehensive structural variation. The experiments shows that the proposed method achieves impressive results and outperforms existing methods on datasets of IAM graph database repository.

Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model student dropout using the largest known dataset on higher education attrition, which tracks over 32,500 students' demographics and transcript records at one of the nation's largest public universities. Our results highlight several early indicators of student attrition and show that dropout can be accurately predicted even when predictions are based on a single term of academic transcript data. These results highlight the potential for machine learning to have an impact on student retention and success while pointing to several promising directions for future work.

Jeff Dean talks about how machine learning is being used at Google. After our first article covering the landscape of machine learning, and our second one bringing insights about TensorFlow and what's to come, we close our series with Jeff telling us about how machine learning is being used inside Google, what resources Google offers to developers and how you can get started with machine learning today. Where is it being used? JD: In our team, we've been building tools for solving machine-learning problems and then collaborating with lots of other teams over the last 5 or 6 years at Google to solve different machine-learning problems. And we started out collaborating with a handful of teams: the speech-recognition team, various teams that had computer-vision problems and then we built infrastructure software that allowed us to solve problems with those teams in machine learning.

A trio of university students is giving Indian startups unprecedented access to Silicon Valley. In April 2016, 20-year-old Abhinav Kukreja and two of his fellow freshman students at the University of California, Berkeley, Anish Prabhu, and Aryaman Dalmia, created an incubator called Moonshot that connects Indian startups with experts, funds, and talent from Silicon Valley. Its four-month program gives companies access to over 20 mentors in India and California, exposes them to various venture capitalists and angel investors, and provides interns. The first batch of startups Moonshot incubated last year included student benefits platform Frapp, marketplace ListUP, small-business cash-flow management startup Numberz, home appliance automation company Hombot, online B2B billing solutions portal Pumpcharge, and real estate and rental management service Azuro. For their second round in January 2017, Moonshot zeroed in on startups "advancing science and technology, artificial intelligence (AI), machine learning, and companies that have a positive social impact," Kukreja, a computer science and statistics major, said.

PreSeries is a tool for investors that uses machine-learning algorithms to predict the success of startups in their very early stages. Its co-founder explained the methodology behind it at the 4YFN startup event at Mobile World Congress in Barcelona.

NEW YORK – Cursive writing is looping back into style in schools across the country after a generation of students who know only keyboarding, texting and printing out their words longhand. Alabama and Louisiana passed laws in 2016 mandating cursive proficiency in public schools, the latest of 14 states that require cursive. And last fall, the 1.1 million-student New York City schools, the nation's largest public school system, encouraged the teaching of cursive to students, generally in the third grade. "It's definitely not necessary but I think it's, like, cool to have it," said Emily Ma, a 17-year-old senior at New York City's academically rigorous Stuyvesant High School who was never taught cursive in school and had to learn it on her own. Penmanship proponents say writing words in an unbroken line of swooshing l's and three-humped m's is just a faster, easier way of taking notes.