This article was originally published on cmo.com Every decade or so, a new, game-changing technology platform changes the way the world works. From the desktop, to mobile, and to the cloud, the landscape continues to advance. We are knee-deep in the artificial intelligence (AI) revolution--one so big that it has been compared to the invention of electricity. AI has the potential to power future innovation, especially in terms of customer experiences and the way we do our jobs.

Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longitudinal sources. A novel application of these ideas is for analyzing group-level differences, i.e., in identifying if trends of estimated objects (e.g., covariance or precision matrices) are different across disparate conditions (e.g., gender or disease). Often, poor effect sizes make detecting the differential signal over the full set of features difficult: for example, dependencies between only a subset of features may manifest differently across groups. In this work, we first give a parametric model for estimating trends in the space of SPD matrices as a function of one or more covariates. We then generalize scan statistics to graph structures, to search over distinct subsets of features (graph partitions) whose temporal dependency structure may show statistically significant group-wise differences. We theoretically analyze the Family Wise Error Rate (FWER) and bounds on Type 1 and Type 2 error. On a cohort of individuals with risk factors for Alzheimer's disease (but otherwise cognitively healthy), we find scientifically interesting group differences where the default analysis, i.e., models estimated on the full graph, do not survive reasonable significance thresholds.

The ubiquity of systems using artificial intelligence or "AI" has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems will require care. AI systems have the potential to synthesize large amounts of data, allowing for greater levels of personalization and precision than ever before---applications range from clinical decision support to autonomous driving and predictive policing. That said, there exist legitimate concerns about the intentional and unintentional negative consequences of AI systems. There are many ways to hold AI systems accountable. In this work, we focus on one: explanation. Questions about a legal right to explanation from AI systems was recently debated in the EU General Data Protection Regulation, and thus thinking carefully about when and how explanation from AI systems might improve accountability is timely. In this work, we review contexts in which explanation is currently required under the law, and then list the technical considerations that must be considered if we desired AI systems that could provide kinds of explanations that are currently required of humans.

"ROBOTS ARE NOT taking over the world", was the message given this week during United Nations talks on the issue of autonomous weapons. That's according to the diplomat leading the first official talks on the issue, the Convention on Conventional Weapons (CCW), as they sought to ease criticism over slow progress towards restricting the use of so-called "killer robots". The United Nations was wrapping up an initial five days of discussions on weapons systems that can identify and destroy targets without human control, which experts say will soon be battle ready. The meeting of the CCW marked an initial step towards an agreed set of rules governing such weapons. But activists warned that time was running out and that the glacial pace of the UN-brokered discussions was not responding to an arms race already underway.

In the summer of 1957, the Earth stood witness as a meteorite cratered in rural Pennsylvania, bringing with it a people-eating plague never seen: an alien amoeba with the taste for human flesh. While we had Steve McQueen around for the first invasion, humanity is now defended against microbial marauders from outer space by NASA and its international counterparts. Biological contamination goes both ways, mind you. Just as important as keeping extraterrestrial organisms from reaching the surface (aka "backward contamination") is ensuring that our planetary probes carry as few microbial hitchhikers from Earth as possible ("forward contamination"). To that end, in 1958, the U.S. National Academy of Sciences (NAS) issued a decree urging "that scientists plan lunar and planetary studies with great care and deep concern so that initial operations do not compromise and make impossible forever after critical scientific experiments."

Australia's Chief Scientist, Alan Finkel, has called on governments and businesses across the world to consider developing a regulatory framework for artificial intelligence devices, ranging from the likes of Apple's Siri to weaponised drones. Dr Finkel, who was speaking at the Creative Innovation Global conference, said he was optimistic about AI, but an ethical stamp needed to be developed, similar to a Fair Trade label, in order to give consumers trust that the AI in a device had been developed according to specified global standards. "Two years ago I published an article in Cosmos magazine calling for a global accord [on weaponised drones]. In the same year, more than 3000 AI and robotics researchers signed an open letter urging the leaders of the world to take action to prevent a global arms race," he said. "On the other end of the spectrum are tools in everyday use, such as social media platforms and smartphones.

Organizations already have plenty to worry about in terms of data protection, but a new type of cyberattack could prove much more damaging and harder to remediate. A destruction of service (DeOS) attack has the potential to destroy the data backups and safety nets organizations rely on to restore their systems and data following an attack, according to Cisco. DeOS attacks are a more dangerous version of distributed denial of service (DDoS), which employs botnets to overload the target organization's servers with traffic until they can no longer handle the extra load. DDoS attacks last hours or days, after which a company can resume normal operations. This is one of the many new security risks that are emerging with the Internet of Things (IoT).

The Sunday Telegraph reports that Chancellor Philip Hammond will announce a pay rise for nurses after "bowing to pressure" from Conservative MPs and cabinet colleagues. The Sunday Times also leads on the Budget, with a picture of what could be a playful Mr Hammond apparently deep in thought and scratching his head. In an interview with the paper, he says he will confirm plans to build 300,000 new homes every year - equivalent to a city the size of Leeds. He is quoted as saying "he will do whatever it takes to get builders building". "Driverless cars by 2021" is the Sunday Express's front page headline.

Driverless cars will be on Britain's roads by 2021 as a result of sweeping regulatory reforms that will put the UK in the forefront of a post-Brexit technological revolution, chancellor Philip Hammond will say this week. In his budget on Wednesday Hammond will allow driverless cars to be tested without any human operator inside or outside the car, and without the legal constraints and rules that apply in many other EU nations, and much of the US. The move – welcomed by the UK motor industry – is part of an attempt by Hammond and the Treasury to project a more upbeat message about the prospects for the UK economy after Brexit, and focus on opportunities as well as the risks. Carmakers have warned that they may have to move at least some production abroad if there is no deal to keep Britain inside the EU single market and customs union, at least for a two-year transition period. But Mike Hawes, chief executive of the Society of Motor Manufacturers and Traders, said it was good news that the government was taking a lead by making the UK attractive to those seeking to develop, test and build an entirely new generation of cars.

Bio: Alexander Gude is currently a data scientist at Lab41 working on investigating recommender system algorithms. He holds a BA in physics from University of California, Berkeley, and a PhD in Elementary Particle Physics from University of Minnesota-Twin Cities. About: Lab41 is a "challenge lab" where the U.S. Intelligence Community comes together with their counterparts in academia, industry, and In-Q-Tel to tackle big data. It allows participants from diverse backgrounds to gain access to ideas, talent, and technology to explore what works and what doesn't in data analytics.