Troubled by something deeply unethical going on at work? Or maybe you're plotting to leak sensitive information on the company that just sacked you? Either way, you best think twice before making your next move because an all-seeing artificial intelligence might just be analysing every email you send, every file you upload, every room you scan into – even your coffee routine. The latest wave of cyber-defence technology employs machine learning to monitor use of the ever-expanding number of smart household objects connected to the Internet of Things – shutting down hackers before they've broken into corporate databases or whistleblowers before they've forwarded on information to the media. One of the leading proponents is cyber-defence company Darktrace, founded in 2013 by former British intelligence officers in Cambridge and today featuring 370 employees in 23 offices globally.

PARIS – Translators play a vital role in saving the world's languages, their work allowing 6,000 to 7,000 spoken tongues to exist, and 3,000 rare dialects to survive. "Without translation, there is no history of mankind," said linguist Astrid Guillaume, of the Sorbonne University in Paris. "We know histories and cultures of the world only by way of translations," she added. The word "translate" comes from the Latin "traducere," which means "to carry across." Here are three examples of how translators serve linguistic diversity.

We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using dont cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.

Last year, director Oscar Sharp and AI researcher Ross Goodwin released the stunningly weird short film Sunspring. It was a sci-fi tale written entirely by an algorithm that eventually named itself Benjamin. Now the two humans have teamed up with Benjamin again to create a follow-up movie, It's No Game, about what happens when AI gets mixed up in an impending Hollywood writers' strike. Ars is excited to debut the movie here, so go ahead and watch. We also talked to the film cast and creators about what it's like to work with an AI.

Want to receive a weekly deep dive into all things auto, transportation, & logistics tech? Click here to subscribe to our auto tech newsletter. The "last mile problem" has long been a thorn in the side of logistics providers, transportation companies, and retailers alike. Compared to the main legs of bulk shipping, train, truck, or aircraft transport, the final leg (or last mile) from logistics hubs to individual homes and offices has traditionally incurred the highest cost and complexity. Last mile challenges have only grown as the proliferation of online shopping strains capacity.

Machine Learning algorithms can predict patterns based on previous experiences. The overarching practice of Machine Learning includes both robotics (dealing with the real world) and the processing of data (the computer's equivalent of thinking). These algorithms find predictable, repeatable patterns that can be applied to eCommerce, Data Management, and new technologies such as driverless cars. The full impact of Machine Learning is just starting to be felt, and may significantly alter the way products are created, and the way people earn a living. Machine Learning algorithms are trained with large amounts of data, allowing the "robot" to learn and anticipate problems and patterns.

And back to our emerging coverage of Asia/Pacific… where people tend to focus on China, India and Australia. However, the Japanese IT services market is larger than these three markets combined – and is growing. So, let's have our Asia/Pacific research lead, Andrew Milroy, discuss some of the important – and unique – aspects of this lucrative market. Japan's ageing and shrinking population creates real skills shortages and very high labor costs Japan is currently the only major developed country that is experiencing a population decline. Unlike other developed economies, it is not offsetting population decline with immigration.

Driving long distances (or using New York City's subway system) used to be a much more complicated affair, generally requiring maps, a sense of direction, some luck and the occasional stop to ask questions of strangers. Turn-by-turn navigation apps have changed all that: You may still take a wrong turn along the way, but the apps usually get you back on track with little fuss. Self-service integration specialist SnapLogic is turning to artificial intelligence (AI) to help its customers achieve that sort of turn-by-turn navigation when it comes to enterprise integration. Citing GPS navigation and digital home assistants like Amazon's Alexa, SnapLogic Founder and CEO Gaurav Dhillon says the company's new technology, Iris, will eliminate the integration backlog that stifles so many technology initiatives through the use of AI to automate highly repetitive, low-level development tasks. "Companies can't innovate and transform their businesses if they're bogged down in rote, repetitive tasks that don't do much for the organization," Doug Henschen, vice president and principal analyst at Constellation Research, said in a statement last week.

PREDICTING HOW artificial intelligence technology will evolve in the following ten or 20 years, or even beyond, is very difficult to say the least. However, certain is the fact that there is much to be gained to go around for everyone. It is estimated that by the year 2018, robots will literally be supervising more than three million of us at work; and by 2020, smart machines will become a major investment priority amongst at least 30% of all CIOs. Right now, many different fields, spanning from customer service to journalism, are already being set aside by increasingly able AI that can replicate human abilities and experience. Already before our eyes is an aspect that we once thought only belonged in future technology.

According to Clark's seminal work on common ground and grounding, participants collaborating in a joint activity rely on their shared information, known as common ground, to perform that activity successfully, and continually align and augment this information during their collaboration. Similarly, teams of human and artificial agents require common ground to successfully participate in joint activities. Indeed, without appropriate information being shared, using agent autonomy to reduce the workload on humans may actually increase workload as the humans seek to understand why the agents are behaving as they are. While many researchers have identified the importance of common ground in artificial intelligence, there is no precise definition of common ground on which to build the foundational aspects of multi-agent collaboration. In this paper, building on previously-defined modal logics of belief, we present logic definitions for four different types of common ground. We define modal logics for three existing notions of common ground and introduce a new notion of common ground, called salient common ground. Salient common ground captures the common ground of a group participating in an activity and is based on the common ground that arises from that activity as well as on the common ground they shared prior to the activity. We show that the four definitions share some properties, and our analysis suggests possible refinements of the existing informal and semi-formal definitions.