Artificial intelligence (AI) is real, albiet maturing slowly. You experience it when you talk to Alexa, when you see a creepily-targeted online ad, and when Netxflix turns you on to Stranger Things. Oh yea, and that self-driving car over there is AI super-powered! AI is indeed cool, but many are scared about how it ultimatley may impact society. Stephen Hawking, Elon Musk, and even the Woz warned that "...artificial intelligence can potentially be more dangerous than nuclear war."

If you attend a protest in Washington, D.C., nowadays, better plan on leaving your cellphone at home. That is, unless you want police to confiscate it, mine it for incriminating information and then gather even more data from their BFF -- Facebook. At least one person arrested during protests on Inauguration Day got an email from Facebook's Law Enforcement Response Team alerting them that investigators wanted access to their data. Another received a Facebook data subpoena. The email was basically a countdown to when Facebook inevitably handed that data over to D.C. police. That is, unless the respondent figured out how to file an objection within a 10-day window.

Modern forensic DNA analyses are crucial to crime scene investigations; however the interpretation of the DNA profiles can be complex. Two researchers from the Forensics and National Security Sciences Institute (FNSSI) have turned to computer technology to assist complicated profile interpretation, specifically when it comes to samples containing DNA from multiple people. "There is a massive amount of data that is not being considered, simply due to our limited capability as human beings," says Michael Marciano, FNSSI research assistant professor, explaining why they're counting on computers to make data-driven predictions. Marciano and Jonathan Adelman, FNSSI research assistant professor, have developed a new method to predict the number of people contributing to mixed DNA samples, the results of which are published online in Forensic Science International: Genetics ahead of the journal's March issue. Additionally, the duo's method, dubbed Probabilistic Assessment for Contributor Estimate (PACE), is patent pending.

When AI first reached the ears of the legal market some years ago there was a flurry of stories about the end of lawyers. For years afterward and with Pavlov dogs-like automation any mention of legal AI summoned up the panicked refrain: 'The end of lawyers is coming, the end of lawyers is coming!' This was until law firms and corporates actually started to make use of legal AI systems, especially in the last two years and even more so last year. The clichéd refrain, now exposed to the cleansing light of real experience, seemed to die away upon contact. It turns out there were no androids or already out of date screen grabs from the 2004 Will Smith movie'iRobot' based on the late great Asimov novel.

Artificial intelligence (AI) is real, albiet maturing slowly. You experience it when you talk to Alexa, when you see a creepily-targeted online ad, and when Netxflix turns you on toStranger Things. Oh yea, and that self-driving car over there is AI super-powered! AI is indeed cool, but many are scared about how it ultimatley may impact society. Stephen Hawking, Elon Musk, and even the Woz warned that "…artificial intelligence can potentially be more dangerous than nuclear war."

Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms.

Technology often hits the market at the speed of innovation and we play catch up with its impacts. We craft policy after the fact, write standards for safety and market growth and, perhaps, ponder how the latest technology will shape our behavior and impact society at large. Fortunately, this somewhat ad hoc, imperfect approach has worked well enough in a rough-and-tumble world. But sometimes entirely new, potentially far-reaching, high-impact areas of innovation come along that demand vigorous consideration upfront before opportunities for unintended consequences become manifest. Such is the case, I believe, with artificial intelligence, or AI.

Most of the world's Internet-connected netizens know of Google through its wildly popular consumer-facing products like its search engine and YouTube video hosting platform. Yet, Google's parent company Alphabet also operates a fascinating "think/do tank" called Jigsaw (formerly Google Ideas) that asks "How can technology make the world safer?" Jigsaw is involved in an incredible array of projects from fighting hate speech with deep learning to making the world's constitutions searchable (a project I personally was heavily involved in, building the technology infrastructure that was used to acquire, digitize, version and codify thousands of constitutions and amendments dating back more than 200 years). Yet, one project of particular interest in today's world of botnet-enabled mass DDOS attacks on free speech and the evolution of cyberwarfare is Jigsaw's Project Shield, which offers free DDOS protection for news, human rights and elections monitoring websites, powered by Google's own global infrastructure. To most of us, distributed denial of service (DDOS) attacks are something we read about in the news periodically when one of our favorite websites goes down.

In the recent years more and more high-dimensional data sets, where the number of parameters $p$ is high compared to the number of observations $n$ or even larger, are available for applied researchers. Boosting algorithms represent one of the major advances in machine learning and statistics in recent years and are suitable for the analysis of such data sets. While Lasso has been applied very successfully for high-dimensional data sets in Economics, boosting has been underutilized in this field, although it has been proven very powerful in fields like Biostatistics and Pattern Recognition. We attribute this to missing theoretical results for boosting. The goal of this paper is to fill this gap and show that boosting is a competitive method for inference of a treatment effect or instrumental variable (IV) estimation in a high-dimensional setting. First, we present the $L_2$Boosting with componentwise least squares algorithm and variants which are tailored for regression problems which are the workhorse for most Econometric problems. Then we show how $L_2$Boosting can be used for estimation of treatment effects and IV estimation. We highlight the methods and illustrate them with simulations and empirical examples. For further results and technical details we refer to Luo and Spindler (2016, 2017) and to the online supplement of the paper.

Confidence interval is abbreviated as CI. In this new article (part of our series on robust techniques for automated data science) we describe an implementation both in Excel and Perl, and discuss our popular model-free confidence interval technique introduced in our original Analyticbridge article, as part of our (open source) intellectual property sharing. This is part of our series on data science techniques suitable for automation, usable by non-experts. The next one to be detailed (with source code) will be our Hidden Decision Trees. Figure 1 is based on simulated data that does not follow a normal distribution: see section 2 and Figure 2 in this article. Classical CI's are just based on 2 parameters: mean and variance.