A new Forrester Research report, Predictions 2018: Automation Alters The Global Workforce, outlines 10 predictions about the impact of AI and automation on jobs, work processes and tasks, business success and failure, and software development, cybersecurity, and regulatory compliance. We will see a surge in white-collar automation, half a million new digital workers (bots) in the US, and a shift from manual to automated IT and data management. "Companies that master automation will dominate their industries," Forrester says. Here's my summary of what Forrester predicts will be the impact of automation in 2018: Automation will eliminate 9% of US jobs but will create 2% more. In 2018, 9% of US jobs will be lost to automation, partly offset by a 2% growth in jobs supporting the "automation economy."

MOGADISHU, Somalia – The Islamic State group's growing presence in Somalia could become a "significant threat" if it attracts fighters fleeing collapsing strongholds in Syria and Iraq, experts say, and already it seems to be influencing local al-Shabab extremists to adopt tactics like beheadings. The U.S. military this month carried out its first drone strikes against IS fighters in Somalia, raising questions about the strength of the group that emerged just two years ago. A second strike targeted the fighters on Sunday, with the U.S. saying "some terrorists" were killed. The Islamic State group burst into public view in Somalia late last year as dozens of armed men seized the port town of Qandala in the northern Puntland region, calling it the seat of the "Islamic Caliphate in Somalia." They beheaded a number of civilians, causing more than 20,000 residents to flee, and held the town for weeks until they were forced out by Somali troops, backed by U.S. military advisers.

In my last article, I discussed how Artificial Intelligence can either be a boon or a bane. This totally depends on how you use the model, how you train it and what output you wish to generate. The role of AI as a boon or a bane depends much on how you train the model initially before testing the same. Today, I'll be talking about how open data has opened opportunistic doors for businesses and researchers in order to perform data analysis. Data Analysis deals with analyzing and organizing the data in such a manner that will give you some productive results.

Humanity has been on the road for a very long time--from the beginning, when each individual had to collect sufficient food to survive every single day--to the point where we invented agriculture. At that point, we moved from 99% survival and 1% reproduction to a brand new model. Growing food marked the introduction of leisure. Since then, every step in our evolution has proceeded along the lines of doing more and more with less and less. You might recall the 1899 story of Charles H. Duell, Commissioner of the U.S. Patent Office, lobbying President McKinley for its closure, claiming that "everything had already been invented."

In the meantime, if one of them goes berserk, here's a useful tactic: Shut the door behind you. One after another, robots in a government-sponsored contest were stumped by an unlocked door that blocked their path at an outdoor obstacle course. One bipedal machine managed to wrap a claw around the door handle and open it but was flummoxed by a breeze that kept blowing the door shut before it could pass through. Robots excel at many tasks, as long as they don't involve too much hand-eye coordination or common sense. Like some gifted children, they can perform impressive feats of mental arithmetic but are profoundly klutzy on the playground.

Artificial intelligence is already helping determine your future – whether it's your Netflix viewing preferences, your suitability for a mortgage or your compatibility with a prospective employer. But can we agree, at least for now, that having an AI determine your guilt or innocence in a court of law is a step too far? Worryingly, it seems this may already be happening. When American Chief Justice John Roberts recently attended an event, he was asked whether he could forsee a day "when smart machines, driven with artificial intelligences, will assist with courtroom fact finding or, more controversially even, judicial decision making". He responded: "It's a day that's here and it's putting a significant strain on how the judiciary goes about doing things".

Women in politics are sometimes accused of consciously exploiting their femininity to get ahead in a male-dominated world. Frances Perkins did that, but in an unusual way: she tried to remind men of their mothers. She dressed in a plain, three-cornered hat, and she refined the way she acted, based on careful observation of what seemed to be most effective in persuading men to accept her ideas. Perhaps it's no coincidence that those ideas could reasonably be described as maternal or parental. Any parent wants to shield their children from serious harm, and Perkins believed governments should do the same for their citizens. She became President Franklin D Roosevelt's Secretary of Labour in 1933.

Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements. Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets. One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry. However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself. Improving on this situation, we present a scalable, communication-efficient, parallel algorithm for computing the Wasserstein barycenter of arbitrary distributions. Our algorithm can operate directly on continuous input distributions and is optimized for streaming data. Our method is even robust to nonstationary input distributions and produces a barycenter estimate that tracks the input measures over time. The algorithm is semi-discrete, needing to discretize only the barycenter estimate. To the best of our knowledge, we also provide the first bounds on the quality of the approximate barycenter as the discretization becomes finer. Finally, we demonstrate the practical effectiveness of our method, both in tracking moving distributions on a sphere, as well as in a large-scale Bayesian inference task.

The MPs behind this independent review certainly seem to think so. Culture Secretary Karen Bradley suggested AI has "the potential to improve our everyday lives", while Business Secretary Greg Clark praised the "huge social and economic benefits its use can bring". More than that: they want to make Britain the world leader in AI and add £630bn to the UK economy. With UK productivity having remained largely stagnant since the 2008 financial crisis, we need to start innovating in areas like AI to create bold new ways of working. But to make this work we need more than government reports.

WASHINGTON (AFP) - Police in the US state of Delaware are poised to deploy "smart" cameras in cruisers to help authorities detect a vehicle carrying a fugitive, missing child or straying senior. The video feeds will be analyzed using artificial intelligence to identify vehicles by license plate or other features and "give an extra set of eyes" to officers on patrol, says David Hinojosa of Coban Technologies, the company providing the equipment. "We are helping officers keep their focus on their jobs," said Hinojosa, who touts the new technology as a "dashcam on steroids." The program is part of a growing trend to use vision-based AI to thwart crime and improve public safety, a trend which has stirred concerns among privacy and civil liberties activists who fear the technology could lead to secret "profiling" and misuse of data. US-based startup Deep Science is using the same technology to help retail stores detect in real time if an armed robbery is in progress, by identifying guns or masked assailants.