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.

In our fourth installment of the Bytes Chat, we convened a panel of economists to discuss the newly released NBER study on the impact of robots on jobs and wages. Bytes contributors Rob Seamans, associate professor at New York University's Stern School of Business, Bret Swanson, president of Entropy Economics, and Hal Singer, senior fellow at George Washington University's Institute of Public Policy were joined by special guest Marshall Steinbaum, senior economist and fellow of the Roosevelt Institute. The conversation has been edited slightly for readability. First question is at the behest of our president. Are the robots coming over the border? Is this a border problem? Marshall Steinbaum: If you get all the enemies in one place, it's easier to kill them. Singer: Ok, let's get serious.

This blog post is based on a white paper authored by Maurizio Felici. Logistic regression is a popular machine learning algorithm used for binary classification. Logistic regression labels a sample with one of two possible classes, given a set of predictors in the sample. Optionally, the output can be the probability that a sample belongs to a given class. For example, suppose a researcher is interested in the factors that determine if a student will be accepted or rejected to graduate school.

A Ford Fusion laden with self-driving sensors does some winter weather testing. Ford Motor is in pole position when it comes to benefiting from the coming age of autonomous vehicles. That's the conclusion of a study released Monday by Navigant Research, which sells its in-depth surveys of energy and transportation markets to suppliers, policymakers and other industry stakeholders. The Dearborn-based automaker took the top spot by demonstrating that it has the strategic vision and execution capabilities to both develop automated driving systems as well as deploy them across a range of mobility platforms. Many automakers are targeting 2021 for a roll-out of AVs that likely will be part of a ride-sharing network.

U.S. education systems must prepare students to compete with robots in the future's job market, warns Jeb Bush. The former Florida governor told AM 970's John Catsimatidis: 'This is not something that's science fiction. This is happening as we speak. The failed 2016 Republican presidential candidate said: 'The looming challenge of automation and artificial intelligence and the rapid advancement of technology brings great benefits but also creates huge challenges.' Jeb Bush, pictured in September 2016, said that education in the U.S. needs an overhaul to help workers compete with robots in the future's job market The former Florida governor and failed 2016 Republican presidential candidate said: 'This is not something that's science fiction. This is happening as we speak.

In this tutorial we will begin by laying out a problem and then proceed to show a simple solution to it using a Machine Learning technique called a Naive Bayes Classifier. This tutorial requires a little bit of programming and statistics experience, but no prior Machine Learning experience is required. You work as a software engineer at a company which provides email services to millions of people. Lately, spam has a been a major problem and has caused your customers to leave. Your current spam filter only filters out emails that have been previously marked as spam by your customers.

SYS-CON Events announced today that HTBase will exhibit at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. HTBase (Gartner 2016 Cool Vendor) delivers a Composable IT infrastructure solution architected for agility and increased efficiency. It turns compute, storage, and fabric into fluid pools of resources that are easily composed and re-composed to meet each application's needs. With HTBase, companies can quickly provision resources and deploy unique, mission-critical, self-designed solutions to add-onto or create any type of infrastructure as per the business requirement. HTBase is the first company to enable a true multi-cloud strategy, enabling organizations to automate movement of data and workloads between private and public clouds.

"Supergirl" stars Chyler Leigh and Floriana Lima revealed that they were pleasantly surprised by how significant Alex and Maggie's relationship has become to viewers who are struggling with their sexuality. "There was no way we would have known the impact that this would have had," Leigh told People at the 28th GLAAD Media Awards ceremony, where "Supergirl" was nominated for best dramatic TV show for its story featuring the lesbian relationship of Alex and Maggie. "We definitely wanted [Alex and Maggie's relationship] to be a strong representation, and that's why we've thought so hard about it and wanted it to be beautifully done," Leigh continued. "So I'm just really happy with the writers of'Supergirl,' who have really brought this to life." READ: Is there a James-centric "Supergirl" episode coming up? "We're just really humbled by it, very proud of it," added Lima who attended the awards night with Leigh.

We present an algorithm for building probabilistic rule lists that is two orders of magnitude faster than previous work. Rule list algorithms are competitors for decision tree algorithms. They are associative classifiers, in that they are built from pre-mined association rules. They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree. Instead of using greedy splitting and pruning like decision tree algorithms, we fully optimize over rule lists, striking a practical balance between accuracy, interpretability, and computational speed. The algorithm presented here uses a mixture of theoretical bounds (tight enough to have practical implications as a screening or bounding procedure), computational reuse, and highly tuned language libraries to achieve computational efficiency. Currently, for many practical problems, this method achieves better accuracy and sparsity than decision trees; further, in many cases, the computational time is practical and often less than that of decision trees. The result is a probabilistic classifier (which estimates P(y = 1|x) for each x) that optimizes the posterior of a Bayesian hierarchical model over rule lists.

In this paper we develop a new framework that captures the common landscape underlying the common non-convex low-rank matrix problems including matrix sensing, matrix completion and robust PCA. In particular, we show for all above problems (including asymmetric cases): 1) all local minima are also globally optimal; 2) no high-order saddle points exists. These results explain why simple algorithms such as stochastic gradient descent have global converge, and efficiently optimize these non-convex objective functions in practice. Our framework connects and simplifies the existing analyses on optimization landscapes for matrix sensing and symmetric matrix completion. The framework naturally leads to new results for asymmetric matrix completion and robust PCA.