Artificial intelligence is already reshaping the labor market. Its impact will likely become even more disruptive. But experts have historically been bad at predicting which jobs and tasks will be lost to automation, and public officials have historically been slow to respond to technological advances with smart, effective regulations. That's the nutshell of a RAND Corporation report on "The Risks of Artificial Intelligence to Security and the Future of Work," released earlier this week. What can K-12 educators and policymakers take away from the work?

Expert system can be made use of for photo recognition, like in this screen at a current modern technology conference. Researchers are rushing to recognize the prospective ramifications of A.I. Credit scores Saul Loeb/Agence France-Presse– Getty Images There are basically 3 huge inquiries about artificial intelligence and its impact on the economic climate: Just what can it do? As well as just how quickly will it spread out? Three new records incorporate to suggest these answers: It can possibly do less today compared to you believe. However it will ultimately do more than you possibly believe, in even more locations than you possibly assume, as well as will possibly evolve faster than powerful modern technologies have in the past.

MONTREAL – Technological advances in artificial intelligence are fuelling a new race between hackers and those toiling to protect cybersecurity networks. Cybersecurity is always a race between offence and defence but new tools are giving companies that employ them a leg up on those trying to steal their data. Whereas past responses to cybercrimes often looked for known hacking methods long after they occurred, AI techniques using machine learning scan huge volumes of data to detect patterns of abnormal behaviour that are imperceptible to humans. Experts expect machines will become so sophisticated that they'll develop answers to questions that humans won't clearly understand. David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence.

The reality of the situation is that those establishing the laws governing the development of AI have a sound understanding of current laws, however, their technical knowledge is, usually, very limited. When attempting to regulate the internet in the late 20th century, lawmakers used the 19th century telecommunications law as a guideline. Repeating the same mistake for artificial intelligence could have serious consequences. Proposals for AI regulations must be debated effectively between tech experts and government officials to determine the appropriate solution. They must be based of the current level of development and influenced by the development direction of the technology.

The U.S. military has carried out 32 airstrikes this year against the Somalia-based al-Shabab and a small but growing presence of fighters linked to the Islamic State group. The Trump administration early this year approved expanded military operations against extremists in the Horn of Africa nation, as the Trump administration puts counterterrorism at the top of its foreign policy agenda for Africa.

The interaction between humans and robots has been a common theme in science fiction and popular culture, but robotic machines don't have to be humanoid or even have much personality for people to develop a relationship with them. Never mind Data in "Star Trek" or BB-8 in "Star Wars," a Georgia Tech study released back in 2010 found people had become emotionally attached to their Roomba robotic vacuum cleaners, giving them names and assigning them genders. A 2013 study at the University of Washington found members of the Army's Explosive Ordnance Disposal units had developed bonds with their bomb disposal robots and although those feelings hadn't affected their performance, raised the question of whether they could eventually compromise decision-making. When robots of one kind or another develop personalities, through machine learning, natural language processing and other artificial intelligence technologies, human operators can get to "know" them, even like them. But the bottom line for government organizations looking to make increasing use of human-machine teaming is whether humans can trust them.

Kepler gets an A.I. boost? NASA will be hosting a somewhat unusual press conference on Thursday to announce the latest find from its planet-hunting Kepler Space Telescope. Kepler has found many hundreds of planets beyond our solar system over the years, but this week's announcement will be different because Google will be sharing in the science spotlight. "The discovery was made by researchers using machine learning from Google," reads a release from the space agency, adding that the breakthrough "demonstrates new ways of analyzing Kepler data." Christopher Shallue, an engineer with Google AI and Google Brain wil be featured during the NASA press conference alongside scientists from NASA, its Kepler team and NASA Sagan Fellow Andrew Vanderburg from the University of Texas.

The future of content is becoming more and more a science fiction business, but we still have a few years to go before the creation of content is entirely in the hands of the software. Researching the topic and keywords, adding appropriate media and resources, formatting the post, and overall, creating something with "real value" for readers are important steps to producing interesting articles. For now, most AI software cannot completely do all these things on their own but they can be of great assistance. The fact is that a lot of writing algorithms have already been developed and sold to big publications which they use daily in producing most of their content at scale. Today, we introduce you to three AI writing software assisting major publications in their content creation process. In Automated Insights' own words "Wordsmith is a natural language generation (NLG) platform that turns data into insightful narratives."

There is a questionable assumption that is prevalent that Deep Learning is a form of probabilistic or statistical induction. We see this in DARPA's presentation of the 3 waves of AI. Statistical Learning -- Where programmers create statistical models for specific problem domains and train them on big data. This is a broad category that includes Bayesian methods, template based methods (i.e. SVM), tree based predictors, mathematical programming and Deep Learning.

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.