Government & the Courts

CES 2018: The Best--and the Craziest--Gadgets of the Show

IEEE Spectrum Robotics Channel

But many technologies that later came to be considered essential parts of modern life began their life as unnecessary technical baubles. For example, in 1970, the first consumer VCR prototype was unveiled at CES, a technology previously only needed by television studios. The home VCR then started the home-viewing and -recording revolution, leading to a critical U.S. Supreme Court decision regarding copyright, and laying the groundwork for YouTube and Netflix. Even when a technology goes nowhere--3D TV glasses anyone?--looking at widgets, gizmos, and novelties can still provide a unique window into larger technological and cultural trends. So I defend the gadget as a worthy object of inquiry, and consequently have spent the last week at CES scouring the halls looking for interesting examples, particularly from smaller companies and startups.

How AI And Machine Learning Can Impact Legal Services Market In India


As per the National Judicial Data Grid, over 26 Mn cases are pending across all the Local, District and High Courts and the Hon'ble Supreme Court of India and close to 9% of these cases are pending over 10 years or more[1]. On average 30,000 cases are filed every day and roughly 28,000 cases are adjudicated daily.[1] This means that there is a shortfall of 2,000 cases that are undecided, leading to a backlog of 7.3 lakh cases being added to the total cumulative backlog every year. The backlog of cases falls within the purview of the administrative function of the judiciary. The solution to this seemingly perennial problem also involves an exponential increase in Executive funding for judicial infrastructure and court expansion.

The Only Way to Stay Ahead of the Robots Taking Over Our Jobs Is to


This post was originally published on The Business Insider. Goff added that technological advances today are progressing far more rapidly than they did during the shift from agriculture to manufacturing. Even if it's technologically possible, you've got to get through state laws, local laws, federal laws. This post was originally published on The Business Insider.

Predicting Supreme Court Decisions Using Artificial Intelligence


The approach to using data to inform legal predictions (as opposed to pure lawyerly analysis) has been largely championed by Prof. Katz – something that he has dubbed "Quantitative Legal Prediction" in recent work. Many of these approaches employ "Machine Learning" techniques to engage in prediction. Pioneering work in the area of quantitative legal prediction began in 2004 with a seminal project by Prof. Ted Ruger (U Penn), Andrew D. Martin (now dean at U Michigan) and other collaborators, employing statistical methods to predict Supreme Court outcomes. The authors applied this algorithmic approach to examine data about past Supreme Court cases found in the Supreme Court Database.

Dara Khosrowshahi: who is the man chosen as Uber's next CEO?

The Guardian

The man designated as Uber's new chief executive left Tehran for the US aged nine on the eve of the Iranian revolution, and became a driving force behind the success of the online travel company Expedia. Senior executives have departed, while the company has faced accusations of sexual discrimination and harassment, and legal headaches including an intellectual property dispute with Waymo, the company operating Google's self-driving car. After his family left Tehran, Khosrowshahi grew up in New York state, spending six of his teenage years raised solely by his mother after his father was detained in Iran, having returned to take care of his own father. He moved from the investment bank Allen & Company to management at InterActive Corp, which acquired Expedia and appointed Khosrowshahi as chief executive in 2005.

Timber! Top Texas Republicans Look to Axe Local Tree Rules

U.S. News

A home once built by Texas Gov. Greg Abbott is seen in Austin, Texas, Thursday, Aug. 10, 2017. While serving as state attorney general in 2011, Abbott tore down his Austin home and built the new one. City records show Abbott was allowed to do so as long as he didn't damage the root systems of two large pecan trees, though roots were eventually damaged in the renovations.

Automated decision making shows worrying signs of limitation


The service also uses data analysis to target fire safety advice, and has found correlations between high risks of accidental home fires and single-person households, social renting, unemployment, smoking and black and Afro-Caribbean ethnicity. The US Supreme Court recently declined to review the Wisconsin supreme court's ruling in favour of Compas' use in Loomis' case, although the Electronic Privacy Information Centre is involved in several other legal challenges it calls a lack of algorithmic transparency. In the 1990s, Rich Caruana, then a graduate student at Carnegie Mellon University, worked on training a neural net machine learning system to predict the probability of death for pneumonia patients. More recent research found this data similarly suggested that chest pain and heart disease patients were less vulnerable to pneumonia.

Artificial intelligence prevails at predicting Supreme Court decisions


Artificial intelligence can predict Supreme Court decisions better than some experts. Decision outcomes included whether the court reversed a lower court's decision and how each justice voted. The model then looked at the features of each case for that year and predicted decision outcomes. "Every time we've kept score, it hasn't been a terribly pretty picture for humans," says the study's lead author, Daniel Katz, a law professor at Illinois Institute of Technology in Chicago.

Big data is used to sentence criminals, can algorithms predict future risk?


When the judge weighed Loomis' sentence, he considered an array of evidence, including the results of an automated risk assessment tool called COMPAS. Then, developers create a statistical algorithm that weighs stronger predictors more heavily than weaker ones. Algorithms such as COMPAS cannot make predictions about individual defendants, because data-driven risk tools are based on group statistics. The Supreme Court might helpfully opine on these legal and scientific issues by deciding to hear the Loomis case.