The Equifax data breach that leaked information on the now-145 million people was caused by a vulnerability in Apache's Struts system. Trouble is, the software provider supplied a patch back in March that should have eliminated that vulnerability. But Equifax's former CEO (who suddenly retired last week) told the House Energy and Commerce Committee that a single IT technician was at fault for the whole thing after they failed to install the patch. While speaking to the committee (video below), former CEO Richard Smith outlined the company's normal procedure for new patches: Have a technician install it and then scan the system for any remaining vulnerabilities. Apparently, both the human and computer steps failed.

Even if you haven't had a chance to check out TensorFlow in detail, it's clear that your choice of platform has a big impact just as it does for other machine learning frameworks. The adventure from trial to production involves many intermediate destinations, from feature engineering to model-building to execution and real-time evaluation. Even a model with the most spectacular F1-score is only as good as how effectively you can put it to use helping customers. Questions arise such as: do you need to evaluate against data for offline or online analysis (or both)? Where does the preprocessed (or feature-engineered) data live on its way to TensorFlow? Is there a way to preserve data lineage as it moves through the various stages to support both security concerns as well as easy debugging?

Why? What could possibly have motivated Stephen Paddock, a retired accountant, to open fire from a balcony above a Las Vegas music festival, killing at least 59 people and injuring more than 500? Police found 23 guns in Paddock's hotel room, but have not discovered any connection to international terrorism, despite a claim of involvement from so-called Islamic State. President Donald Trump described the act as "pure evil" and some investigators say there is reason to believe the gunman, 64, had a history of psychological problems. Meanwhile, searches have uncovered explosives at Paddock's home in a retirement community in the small town of Mesquite, north east of Las Vegas. There is a second house in northern Nevada which Swat teams are due to check for booby-traps before carrying out a search. The authorities have yet to confirm the identities of any of the people killed, but Jordan McIldoon, 23, from British Columbia in Canada, has been named as a victim of the attack by CBC News.

BEIRUT – Two suicide bombers stormed a police station in the Syrian capital on Monday, killing at least 17 civilians and police, state TV reported, while a drone strike in eastern Syria killed 10 Hezbollah fighters who were helping Syrian troops battle the Islamic State group. The Syrian government is at war with the IS group as well as a local al-Qaida affiliate and an array of rebel groups, none of which immediately claimed the attack. It was also unclear who struck the Hezbollah fighters. Lt. Gen. Mohammad al-Shaar, Syria's interior minister, told reporters that two "terrorists" attacked the police station in the al-Midan neighborhood of Damascus with a number of bombs on Monday, before one of them blew himself up. He said the other bomber made it inside the compound, where police killed him, causing his bomb to explode.

During the height of summer, an open letter to the United Nations warning of the dangers of Artificial Intelligence, signed by Tesla CEO Elon Musk and more than 100 other entrepreneurs, caused quite a stir in the media and the scientific community. The letter gave rise to a heated digitized debate between the Tesla CEO and Facebook founder Mark Zuckerberg. The letter's signatories were alerting the international community about the possible defence applications of AI, and in particular about the threat of lethal autonomous weapons or "killer robots". It is within this context that I wish to address the theme of Artificial Intelligence here at the Women's forum for the Economy & Society Global Meeting in Paris (#WFGM17). The scope of the subject is constantly evolving and we at Thales owe the most recent advances in this domain to a woman at the CNRS / THALES research center in Palaiseau (Researchers unveil the world's first artificial nano-neuron with voice recognition capabilities). To claim that AI could "spell the end of the human race" may grab headlines, but the underlying debate over moral and political limitations on the use of intelligent weapons is legitimate, provided the discussions are clear-headed and dispassionate.

The field of AI has made great strides in the last several years, thanks to developments in machine learning algorithms and deep learning systems based on artificial neural networks (ANNs). Researchers have found that vast sets of example data are the way to train up such systems to produce the desired results, whether that is picking out a face from a photograph or recognizing speech input. But the resultant systems often turn out to operate as an inscrutable "black box" and even their developers find themselves unable to explain why it arrived at a particular decision. That may soon prove unacceptable in areas where an AI's decisions could have an impact on people's lives, such as employment, mortgage lending, or self-driving vehicles. The value of so-called explainable AI was called into question recently by Google research director Peter Norvig, who noted that humans are not very good at explaining their decision-making either, and claimed that the performance of an AI system could be gauged simply by observing its outputs over time.

When classifying point clouds, a large amount of time is devoted to the process of engineering a reliable set of features which are then passed to a classifier of choice. Generally, such features - usually derived from the 3D-covariance matrix - are computed using the surrounding neighborhood of points. While these features capture local information, the process is usually time-consuming, and requires the application at multiple scales combined with contextual methods in order to adequately describe the diversity of objects within a scene. In this paper we present a 1D-fully convolutional network that consumes terrain-normalized points directly with the corresponding spectral data,if available, to generate point-wise labeling while implicitly learning contextual features in an end-to-end fashion. Our method uses only the 3D-coordinates and three corresponding spectral features for each point. Spectral features may either be extracted from 2D-georeferenced images, as shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted directly for passive-derived point clouds,i.e. from muliple-view imagery. We train our network by splitting the data into square regions, and use a pooling layer that respects the permutation-invariance of the input points. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6%. We ranked third place with a mean F1-score of 63.32%, surpassing the F1-score of the method with highest accuracy by 1.69%. In addition to labeling 3D-point clouds, we also show that our method can be easily extended to 2D-semantic segmentation tasks, with promising initial results.

As future wars will be increasingly fought by intelligent robots that effectively replace human soldiers, Norwegian researcher Morten Hansbø fears that his country might be caught unawares by the latest trends in unmanned warfare. Only years from now, war robots fully capable of understanding their surroundings, adapting to weather conditions and navigating without GPS may become a reality, wrote researcher Morten Hansbø of the Norwegian Defense Research Institute (FFI) in his article "Robotics, Combat Power and Sustainability in the Future Defense." Countries that don't use robotics may become easy prey in future warfare, Hansbø claimed in an interview with the Norwegian daily Aftenposten. "A defense that does not rely on robotics will have little credibility 15 years from now," Morten Hansbø said. Hansbø argued that Norway's wait-and-see attitude may soon backfire.

Estonia is known for its'firsts'. We were the first country to declare internet access as a human right, the first country to hold a nationwide election online, the first country in Europe to both legalise ride sharing and delivery bots, and -- of course -- the first country to offer e-Residency. Countries around the world now face the challenge of understanding the rise of Artifical Intelligence, which is increasingly affecting the daily lives of their populations, so which country willl be the first in developing a comprehensive legal framework that ensures the technology can be developed in an ethical and sustainable way? We think the answer once again should be Estonia. This work to understand AI in Estonia started with our self-driving vehicles task force.

Michigan Department of Corrections spokesman Chris Gautz said video surveillance shows that inmates at Richard A. Handlon Correctional Facility received two packages dropped by a drone May 29. Prison officials suspect the packages contained cellphones that were found inside the prison in July. The report, which was obtained by the Detroit News through a Freedom of Information Act request, said a third package containing phones, tobacco and marijuana was delivered that day, but prison officials recovered it. "A source inside the prison informed MDOC staff that it was the result of an unsuccessful drone delivery," according to the report by State Police Detective Sgt. "It was later learned that two packages were successfully delivered (confirmed through video) to prisoners via drone. After the successful drone delivery, two phones were found inside the facility on prisoners."