Artificial Intelligence is on the verge of penetrating every major industry from healthcare to advertising, transportation, finance, legal, education, and now inside the workplace. Many of us may have already interacted with a chatbot (defined as an automated, yet personalized, conversation between software and human users) whether it's on Facebook Messenger to book a hotel room or ordering flowers through 1-800 flowers. According to Facebook Vice President, David Marcus, there are now more than 100,000 chatbots on the Facebook Messenger platform, up from 33,000 in 2016. As we increase the usage of chatbots in our personal lives, we will expect to use them in the workplace to assist us with things like finding new jobs, answering frequently asked HR related questions or even receiving coaching and mentoring. Chatbots digitize HR processes and enable employees to access HR solutions from anywhere.

YANGON, Myanmar – Two foreign journalists accused of illegally flying a drone over parliament buildings in Myanmar have appeared in court for the first time since their arrest last month. The two Malaysians appeared during a hearing in the capital, Naypyitaw, along with their local interpreter and driver. The four men working for Turkish Radio and Television were charged under the Export and Import Law and face up to three years in prison if found guilty. The four were detained on Oct. 27.

In a usual scenario today, a salesperson might receive a draft of a nondisclosure agreement from a potential customer and forward it to a company's in-house lawyers. It could take a couple days for the legal team to review the contract and send it back--or a couple of weeks. As the salesperson waits, he or she loses the ability to move the deal forward. "There are only a few pieces or items that you care about, but there's a labyrinth of clauses, so you don't know what will trip it up," said Andrew (Ondřej) Antos, Klarity's CEO. "We decided to use natural language processing and AI to accelerate review."

There are many different types of sites that provide a wealth of free, freemium and paid data that can help audience developers and journalists with their reporting and storytelling efforts, The team at State of Digital Publishing would like to acknowledge these, as derived from manual searches and recognition from our existing audience. Kaggle's a site that allows users to discover machine learning while writing and sharing cloud-based code. Relying primarily on the enthusiasm of its sizable community, the site hosts dataset competitions for cash prizes and as a result it has massive amounts of data compiled into it. Whether you're looking for historical data from the New York Stock Exchange, an overview of candy production trends in the US, or cutting edge code, this site is chockful of information. It's impossible to be on the Internet for long without running into a Wikipedia article.

"Ultimately lawyers are like programmers, only difference being, they code/write in legal language, which in most cases is English," says Shashank Bijapur, co-founder of AI driven legaltech startup SpotDraft and a former Wall Street Lawyer. It is this belief which led Shashank, a Harvard Law School graduate, from the echelons of Wall Street to finding a startup in the legaltech space which would be capable of using AI to read through contracts, organise, manage and finally, analyse them. But we are jumping the gun here. Roll back a few years to Shashank's Wall Street days where he saw day in and day out what lawyers did. I have seen upfront what lawyers do and how they work. I realised that the process is too time consuming and riddled with inefficiencies.

During a recent four-day trip to Marib with a group of Western journalists and researchers, I saw a town struggling for a sense of normalcy -- and even progress -- despite the collapsed country around it. The trip was organized by the Sana Center for Strategic Studies, a research institute focused on Yemen, and led by Farea al-Muslimi, an energetic young Yemeni scholar, who said he worried that the international community was forgetting about Yemen, to the peril of both. "We can't stop the war in Yemen right now, but at least we can cause more conversation about it," he said. "We want to bring the world to Yemen and bring Yemen to the world." Marib's unlikely success is partly a symptom of the near complete shattering of the Yemeni state, which has left regions to fend for themselves in providing life's basics for their people.

Artificial Intelligence is on the verge of penetrating every major industry from healthcare to advertising, transportation, finance, legal, education, and now inside the workplace. Many of us may have already interacted with a chatbot (defined as an automated, yet personalized, conversation between software and human users) whether it's on Facebook Messenger to book a hotel room or ordering flowers through 1-800 flowers. According to Facebook Vice President, David Marcus, there are now more than 100,000 chatbots on the Facebook Messenger platform, up from 33,000 in 2016. As we increase the usage of chatbots in our personal lives, we will expect to use them in the workplace to assist us with things like finding new jobs, answering frequently asked HR related questions or even receiving coaching and mentoring. Chatbots digitize HR processes and enable employees to access HR solutions from anywhere.

Decisions about whether to grant bail could be better made by a machine than by a human. Predictions based on machine learning could outperform judges when deciding which defendants to jail before trial and which to release on bail. Kleinberg et al. exploited data on more than 758,000 defendants who were arrested in New York City between 2008 and 2013. Compared with carefully devised counterfactual scenarios based on actual judges' decisions, the machine predictions based on defendants' histories could reduce crime by up to 25% with no increase in jailing, or reduce jailing up to 42% with no increase in crime. All categories of crime, including violent crimes, could be reduced, and, critically, so could racial disparities in jailing rates.

In July 2015, Google's public-relations machine was in full-on crisis mode. Earlier that year, the search giant announced Photos, an AI-driven app that used machine-learning to automatically tag and organize your pictures based on the people, places and things depicted in them. It was an exciting step forward, but Photos wasn't perfect. While the app was capable of recognizing some faces, it mistook others. It would have been easy to pass this off as a routine software bug if it weren't for the nature of the failure.

Many countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the \emph{spatiotemporal (ST) causal pathways} for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis, (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high, and (3) the \emph{ST causal pathways} are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}. First, \emph{Pattern mining} helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as "causers". Then, \emph{Bayesian learning} carefully encodes the local and ST causal relations with a Gaussian Bayesian network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors, in three regions of China from 01-Jun-2013 to 19-Dec-2015. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.