Hurricanes, earthquakes, wildfires, floods, volcanic eruptions and tornadoes are constant threats to society. While they have always happened, humans are now on the scene. Human lives and property are constantly threatened. Such risks are further amplified along coastlines, fault lines, volcanic regions, or flood zones. Craig Fugate is the former FEMA Administrator under President Obama and one of the most effective emergency managers in the world (period).

Today, machine learning (ML) is coming into its own, ready to serve mankind in a diverse array of applications – from highly efficient manufacturing, medicine and massive information analysis to self-driving transportation, and beyond. However, if misapplied, misused or subverted, ML holds the potential for great harm – this is the double-edged sword of machine learning. "Over the last decade, researchers have focused on realizing practical ML capable of accomplishing real-world tasks and making them more efficient," said Dr. Hava Siegelmann, program manager in DARPA's Information Innovation Office (I2O). But, in a very real way, we've rushed ahead, paying little attention to vulnerabilities inherent in ML platforms – particularly in terms of altering, corrupting or deceiving these systems." In a commonly cited example, ML used by a self-driving car was tricked by visual alterations to a stop sign. While a human viewing the altered sign would have no difficulty interpreting its meaning, the ML erroneously interpreted the stop sign as a 45 mph speed limit posting. In a real-world attack like this, the self-driving car would accelerate through the stop sign, potentially causing a disastrous outcome. This is just one of many recently discovered attacks applicable to virtually any ML application. To get ahead of this acute safety challenge, DARPA created the Guaranteeing AI Robustness against Deception (GARD) program. GARD aims to develop a new generation of defenses against adversarial deception attacks on ML models. Current defense efforts were designed to protect against specific, pre-defined adversarial attacks and, remained vulnerable to attacks outside their design parameters when tested. GARD seeks to approach ML defense differently – by developing broad-based defenses that address the numerous possible attacks in a given scenario. "There is a critical need for ML defense as the technology is increasingly incorporated into some of our most critical infrastructure.

Bosses could soon be monitoring employees even in their spare time, if developments at the US's Department of Defense (DOD) are anything to go by. A New-York based startup has been awarded £1.87 million ($2.42mn)to create an AI driven phone that constantly learns what its user is doing. The phone will know its user so well that it can tell a change in identity through the way the user walks, types messages and the activities they undertake. The system would replace all common access cards and passwords for the Defence Department and know if someone else is using the device. Bosses could soon be monitoring employees even in their spare time, if developments at the US's Department of Defense (DOD) are anything to go by.

Since the world learned of state-sponsored campaigns to spread disinformation on social media and sway the 2016 election, Twitter has scrambled to rein in the bots and trolls polluting its platform. But when it comes to the larger problem of automated accounts on Twitter designed to spread spam and scams, inflate follower counts, and game trending topics, one study argues that the company still isn't keeping up with the deluge of garbage and abuse. In fact, the paper's two researchers write that with a machine learning approach they developed themselves, they could identify abusive accounts in far greater volumes and faster than Twitter does--often flagging the accounts months before Twitter spotted and banned them. In an 16-month study of 1.5 billion tweets, Zubair Shafiq, a computer science professor at the University of Iowa, and his graduate student Shehroze Farooqi, identified more than 167,000 apps using Twitter's API to automate bot accounts that spread tens of millions of tweets pushing spam, links to malware, and astroturfing campaigns. They write that more than 60 percent of the time, Twitter waited for those apps to send more than 100 tweets before identifying them as abusive; the researchers' own detection method had flagged the vast majority of the malicious apps after just a handful of tweets.

Faced with mounting criticism of its "Rekognition" system, Amazon has come out in favor of legislating facial recognition technology. In a blog post, the company has revealed its "proposed guidelines" for the responsible use of the tech that it hopes policymakers in the US and worldwide will consider when drafting new laws. Amazon's five-step rulebook essentially calls for use of the tech to be governed by current laws, including those that protect civil rights. It also urges human oversight when facial recognition is used by law enforcement and recommends a 99 percent confidence score threshold for identification, adding that the tech should not be the "sole determinant" in an investigation. It calls for law enforcement to release regular transparency reports on their use of the systems.

It's common to say that China and the US are in an "AI arms race," as each country races to develop and commercialize deep learning and other AI technologies before the other. A new report shows that a more literal AI arms race is also under way. View from Beijing: The new report, from the Center for a New American Security, an influential think tank, offers an unusual level of access. It draws on various conferences as well as meetings with officials in China's Ministry of Foreign Affairs. Military mind-set: The report says that China is moving quickly to add ever more AI and autonomy to military weapons systems.

The European Commission's Joint Research Center released a report that explores the European perspective on artificial intelligence (AI), along with the global AI landscape's state of play. The report recognizes the value of AI across industry, but while acknowledging the fierce competition on AI taking place between the EU, China, and the United States, it ultimately dismisses the need for Europe to win this global race, arguing instead that for the EU, the more important goal is focusing on developing values and ethics in AI. This is a naive perspective, especially given that China is not only fiercely competing on developing AI, but also aspiring to dominate in AI so as to compete in industries where Europe is leading today. Ironically, even if the EU's first priority is to shape the values and ethics of AI, it will be severely limited in its ability to do so if it is not leading the development and adoption of this technology. Europe would be wrong to forget that any competition involves winner and losers--and more often than not, the winners are those who compete with gusto.

In Washington County, Oregon, sheriff's deputies use a mobile app to send photos of suspects to Amazon's cloud computing service. The e-commerce giant's algorithms check those faces against a database of tens of thousands of mugshots, using Amazon's Rekognition image analysis service. Such use of facial recognition by law enforcement is essentially unregulated. But some developers of the technology want to change that. In a blog post Thursday, Amazon asked Congress to put some rules around the use of the technology, echoing a call by Microsoft in December.

An unsupervised learning algorithm to cluster hyperspectral image (HSI) data is proposed that exploits spatially-regularized random walks. Markov diffusions are defined on the space of HSI spectra with transitions constrained to near spatial neighbors. The explicit incorporation of spatial regularity into the diffusion construction leads to smoother random processes that are more adapted for unsupervised machine learning than those based on spectra alone. The regularized diffusion process is subsequently used to embed the high-dimensional HSI into a lower dimensional space through diffusion distances. Cluster modes are computed using density estimation and diffusion distances, and all other points are labeled according to these modes. The proposed method has low computational complexity and performs competitively against state-of-the-art HSI clustering algorithms on real data. In particular, the proposed spatial regularization confers an empirical advantage over non-regularized methods.

Deep neural networks were shown to be vulnerable to single pixel modifications. However, the reason behind such phenomena has never been elucidated. Here, we propose Propagation Maps which show the influence of the perturbation in each layer of the network. Propagation Maps reveal that even in extremely deep networks such as Resnet, modification in one pixel easily propagates until the last layer. In fact, this initial local perturbation is also shown to spread becoming a global one and reaching absolute difference values that are close to the maximum value of the original feature maps in a given layer. Moreover, we do a locality analysis in which we demonstrate that nearby pixels of the perturbed one in the one-pixel attack tend to share the same vulnerability, revealing that the main vulnerability lies in neither neurons nor pixels but receptive fields. Hopefully, the analysis conducted in this work together with a new technique called propagation maps shall shed light into the inner workings of other adversarial samples and be the basis of new defense systems to come.