Donald Trump will host executives from the video game industry at the White House on Thursday, resurrecting a debate over the link between violent video games and gun-related deaths in the aftermath of the Parkland high school shooting. The meeting comes as the president and lawmakers in Washington face continued pressure to act in the wake of the 14 February massacre at Marjory Stoneman High School, which left 17 people dead. Although the White House has provided few details on the meeting, Trump's focus on the role of violence in entertainment signaled the president may be embracing a more conservative response to the Parkland shooting, even as the push for stricter gun laws reaches a fever pitch. Each time there's a school shooting in America, someone inevitably points the finger at video games. Trump similarly targeted video games and movies for influencing children and young adults when discussing the Parkland tragedy at the White House last month.

With all the headlines on gender diversity, workplace harassment and equal pay, have we come a long way, baby? International Women's Day, which is Thursday, is a good time to look at this question. Studies show that having more women in senior management jobs improves the financial performance of companies. And having more female members of company boards helps companies deal more effectively with risk and brings other benefits as well. Despite this, there has been scant progress in increasing the number of women in leadership roles in the past 10 years.

As more companies turn to artificial intelligence to discover new talent, a recent survey has shown that a majority of job-seeking university students don't want their abilities judged by the technology. About 67.5 percent of 1,258 university and graduate students slated to graduate in March 2019 said they don't want AI to assess their job qualifications during interviews, according to the online survey published Wednesday by Tokyo-based recruitment consulting firm Disco Inc. The survey, conducted from March 1 to Tuesday, also showed that 50.1 percent of respondents don't want AI to read their resume and decide whether they qualify for the next round. The results reflect students' honest feelings that they don't feel comfortable having their job qualifications judged by algorithms without even meeting a human staffer, Disco spokesman Osamu Yoshida said Thursday. "For professionals, companies see applicants' skills and achievements in work -- which are relatively easy to assess. But for new university graduates they are more likely to value students' potential and willingness to work, which are difficult for AI to evaluate," Yoshida said.

People are terrified of the prospect of robots replacing humans in the workforce. But apparently they're not so scared that they fear a robot may be coming for their own job. That's according to a recent Gallup survey, which found that roughly 75% of U.S. adults think AI will'eliminate more jobs than it creates.' However, only 23% of American workers fear they will lose their own job to technology. A new Gallup survey finds that people believe robots are going to replace more and more human jobs, just not their own.

Google has made its machine learning education program available to everyone-- from researchers, to developers and companies, to students. The Learn with Google AI portal which was earlier exclusive to Google employees, called Googlers, is now available to everyone else with an interest in the field. Anyone, from novice to an expert, can learn the basics as well as the advanced art of the trade. "This site provides ways to learn about core ML (machine learning) concepts, develop and hone your ML skills, and apply ML to real-world problems. From deep learning experts looking for advanced tutorials and materials on TensorFlow, to "curious cats" who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here," said Zuri Kemp who leads Google's machine learning education effort.

Machine Learning/ AI development is one of the fields that I am looking into. I was wondering if you guys can tell me anything about the the job climate, how hard it is to get a job in the field and anything else that I should know.

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it is the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.

In extreme classification problems, learning algorithms are required to map instances to labels from an extremely large label set. We build on a recent extreme classification framework with logarithmic time and space, and on a general approach for error correcting output coding (ECOC), and introduce a flexible and efficient approach accompanied by bounds. Our framework employs output codes induced by graphs, and offers a tradeoff between accuracy and model size. We show how to find the sweet spot of this tradeoff using only the training data. Our experimental study demonstrates the validity of our assumptions and claims, and shows the superiority of our method compared with state-of-the-art algorithms.

Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend. Relatedly, researchers advocating for new methods typically compare their method to at best 2 or 3 other causal inference strategies and test using simulations that may or may not be designed to equally tease out flaws in all the competing methods. The causal inference data analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both of these issues. The researchers creating the data testing grounds were distinct from the researchers submitting methods whose efficacy would be evaluated. Results from 30 competitors across the two versions of the competition (black box algorithms and do-it-yourself analyses) are presented along with post-hoc analyses that reveal information about the characteristics of causal inference strategies and settings that affect performance. The most consistent conclusion was that methods that flexibly model the response surface perform better overall than methods that fail to do so. Finally new methods are proposed that combine features of several of the top-performing submitted methods.

Doubly stochastic learning algorithms are scalable kernel methods that perform very well in practice. However, their generalization properties are not well understood and their analysis is challenging since the corresponding learning sequence may not be in the hypothesis space induced by the kernel. In this paper, we provide an in-depth theoretical analysis for different variants of doubly stochastic learning algorithms within the setting of nonparametric regression in a reproducing kernel Hilbert space and considering the square loss. Particularly, we derive convergence results on the generalization error for the studied algorithms either with or without an explicit penalty term. To the best of our knowledge, the derived results for the unregularized variants are the first of this kind, while the results for the regularized variants improve those in the literature. The novelties in our proof are a sample error bound that requires controlling the trace norm of a cumulative operator, and a refined analysis of bounding initial error.