At least, that's what we heard from the analysts, customers, partners, and experts we reached out to in order to get a sense from the HR community of what to expect in the next year. Anna Tavis, Ph.D. Associate Professor, Academic Director of HCM at NYU, notes that the strategic role of HR continues to rise. "HR is not only at the table but in the engine room of every organizations," Tavis says. "There are a lot new beginnings for HR as what used to be called the "soft" stuff breaks down hardwired brands." Josh Bersin, founder of Bersin by Deloitte, would agree.

Research conducted by Censuswide on behalf of BlackLine, Inc., a leading provider of financial controls and automation software that enables Continuous Accounting, showed Officials said the survey incorporated the responses of 300 CFOs, finance directors and accountants in American companies with annual revenues of more than $150 million. Nearly half of the respondents indicated AI already plays a role in their organization today, while 30 percent are currently investigating its use. Further analysis revealed more than half of respondents said the technology should enable them to complete accounts payable and receivable functions without the need for human intervention. Almost half of the respondents said AI would further facilitate the automation of reconciliations and nearly one-third said the tools would assist the financial close. "The responses underscore the growing power and use of AI tools and the implications for the finance organization," Therese Tucker, founder and CEO of BlackLine, said.

In 2014, user data on OkCupid showed that most men on the site rated black women as less attractive than women of other races and ethnicities. That resonated with Ari Curtis, 28, and inspired her blog, Least Desirable. In 2014, user data on OkCupid showed that most men on the site rated black women as less attractive than women of other races and ethnicities. That resonated with Ari Curtis, 28, and inspired her blog, Least Desirable. I usually like "bears," but no "panda bears."

The Justice Department will soon start trying to jam cellphones smuggled into federal prisons and used for criminal activity, part of a broader safety initiative that is also focused on preventing drones from airdropping contraband to inmates. Deputy Attorney General Rod J. Rosenstein told the American Correctional Association's conference in Orlando on Monday that, while the law prohibits cellphone use by federal inmates, the Bureau of Prisons confiscated 5,116 such phones in 2016, and preliminary numbers for 2017 indicate a 28 percent increase. "That is a major safety issue," he said in his speech. "Cellphones are used to run criminal enterprises, facilitate the commission of violent crimes and thwart law enforcement." When he was the U.S. attorney in Maryland, Rosenstein prosecuted an inmate who used a smuggled cellphone to order the murder of a witness.

This article describes Team Kernel Glitches' solution to the National Institute of Justice's (NIJ) Real-Time Crime Forecasting Challenge. The goal of the NIJ Real-Time Crime Forecasting Competition was to maximize two different crime hotspot scoring metrics for calls-for-service to the Portland Police Bureau (PPB) in Portland, Oregon during the period from March 1, 2017 to May 31, 2017. Our solution to the challenge is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. Our model can be understood as an approximation to the popular log-Gaussian Cox Process model: we discretize the spatiotemporal point pattern and learn a log intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags, bandwidths for smoothing kernels, as well as cell shape, size, and rotation, were learned using crossvalidation. Resulting predictions exceeded baseline KDE estimates by 0.157. Performance improvement over baseline predictions were particularly large for sparse crimes over short forecasting horizons.

Judge Michael Topolski said Mohammed, 36, had been "planning an explosion to kill and maim innocent people in the cause of Islamic State." The judge said El-Hassan, 33, was "ideologically motivated to provide him with support, motivation and assistance."

The most productive, wealthy and highly efficient people devote at least 30 minutes a day to reading. If it works for them, it could work for you. You can make 2018 your best year yet. These are the books many people will read to get better, smarter, improve how they think or change how they work. They are books I am most excited about.

University of Pennsylvania workshop addresses potential biases in the predictive technique. Stephen Hawking once warned that advances in artificial intelligence might eventually "spell the end of the human race." And yet decision-makers from financial corporations to government agencies have begun to embrace machine learning's enhanced power to predict--a power that commentators say "will transform how we live, work, and think." During the first of a series of seven Optimizing Government workshops held at the University of Pennsylvania Law School last year, Aaron Roth, Associate Professor of Computer and Information Science at the University of Pennsylvania, demystified machine learning, breaking down its functionality, its possibilities and limitations, and its potential for unfair outcomes. Chairman of the Penn Department of Criminology Richard Berk offers commentary. Machine learning, in short, enables users to predict outcomes using past data sets, Roth said.

The recent tax reform bill passed in the US has raised a lot of questions about wealth distribution in the country. While there's been a lot of focus on how the tax plan will impact income, there's been less attention focused on how this plan impacts the assets of wealthy households. The goal of this post is to show how the R programming language can be used to data mine publicly available sources to better understand the net worth of affluent households in the US. To answer these questions, we present descriptive statistics of this survey data and perform cluster analysis on affluent households, which we identify as households with a net worth of more than $1,000,000 USD. Based on the survey data, our analysis shows that the net worth of the top 1% of households in the US is $10.4M and the net worth of the top 0.1% of households is $43.2M.

Artificial Intelligence (AI) is the hottest trend at the moment, everyone is talking about how it may change our lives and even take our jobs. Potentially every industry will be affected by AI in the (near) future, but this doesn't mean it will be a negative effect. I have a background in Law so naturally I'm interested to see how AI might change the legal profession for the better. As AI continues to develop and learn it can be used to cut time in proof-reading and research. A study in America found that it took legal professionals on average one hour to proof a document for mistakes, but it only took the AI a matter of minutes.