Sweden appears to have accidentally leaked the details of almost all of its citizens. The brewing scandal – based around a leak that actually happened in 2015 but only emerged last week – could see prominent members of Sweden's government removed from their post. The leak allowed unvetted IT workers in other countries to see the details of people registered in Swedish government and police databases. It happened after the government looked to outsource data held by the Transport Agency, but did so in a way that allowed that information to be available to almost anyone, critics have claimed. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Last week, Elon Musk dashed off 125 characters announcing a remarkably ambitious plan to send Amtrak to an early grave. "Just received verbal govt approval for The Boring Company to build an underground NY-Phil-Balt-DC Hyperloop. NY-DC in 29 mins," he proclaimed in a tweet. Sign up to get Backchannel's weekly newsletter. Yet something about this particular moonshot seemed off.

This spring, as part of their coursework, four Stanford University students found themselves in Coronado, California, doing pushups on the beach and charging into a 61-degree surf while overseen by Navy SEAL trainers. They performed this extraordinary homework to better understand the process of inculcating recruits into the elite corps of military frogmen and women. The end result of their (literal) immersion was a solution to an inefficiency in evaluating prospective SEALS: the time-consuming process of analyzing the mountains of comments made about each candidate. Tackling the problem like the internet entrepreneurs they hoped to become, the students created a mobile app to streamline the process. Their reward was thanks from a grateful military establishment--and college credit. Dan Raile is a freelance journalist based in San Francisco.

Subscribe to our mailing list and get interesting stuff and updates to your email inbox. Many aspects of artificial intelligence – the technique of training a machine to learn in a way which resembles a human being – may not be entirely new, but with the increasing availability of relatively cheap and flexible computing power, the technology is becoming far more accessible. A number of tech companies and vendors are now offering APIs and frameworks that allow businesses to create their own intelligence services. Ocado is using a combination of Google's open source TensorFlow machine learning tools and cloud APIs to support internal AI projects. One such initiative focuses on automating management of the deluge of customer service-related emails the company receives.

Two unicorn companies valued at over $1B are included in the map: the automated endpoint protection company Tanium and the predictive intelligence company Cylance. Mobile Security: Included in this category are startups such as Appthority, which provides a cloud-based platform that automatically identifies and grades risky behavior in mobile apps including known and unknown malware, new malware used in targeted attacks, corporate data ex-filtration, and intellectual property exposure. Similarly, Skycure's predictive technology leverages massive crowd knowledge to proactively identify threats to secure mobile devices. Behavioral Analytics / Anomaly Detection: Startups in this category include Darktrace which uses advanced mathematics and machine learning to detect anomalous behavior in organizations' systems and networks in order detect cyber-attacks.

In an age of Photoshop, filters and social media, many of us are used to seeing manipulated pictures – subjects become slimmer and smoother or, in the case of Snapchat, transformed into puppies. However, there's a new breed of video and audio manipulation tools, made possible by advances in artificial intelligence and computer graphics, that will allow for the creation of realistic looking footage of public figures appearing to say, well, anything. Hillary Clinton describing the stolen children she keeps locked in her wine cellar. Tom Cruise finally admitting what we suspected all along … that he's a Brony. This is the future of fake news.

SAN FRANCISCO – Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the threat that artificial intelligence might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question. Thanks to AI, computers are learning to do a variety of tasks that have long eluded them -- everything from driving cars to detecting cancerous skin lesions to writing news stories. But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction. Two weeks ago, Musk warned U.S. governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said.

Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the possible threat artificial intelligence might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question. Thanks to AI, computers are learning to do a variety of tasks that have long eluded them -- everything from driving cars to detecting cancerous skin lesions to writing news stories. But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction. Two weeks ago, Musk warned U.S. governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said at the time.

And how can businesses, as well as members of the public, best keep themselves informed about the extent to which advances in AI may impact on the economy, as well as our society? A recent consultation by the UK House of Lords Select Committee on Artificial Intelligence has called for evidence on the economic, ethical and social implications of advances in artificial intelligence [call for evidence PDF]. The consultation poses a range of questions in particular topic areas, such as the impact of AI on society and the public perception of it, as well as ethical considerations and the role of the governemnt in responding to AI's development and use. For example, one question, targeted at experts in the field, asks "What is the current state of artificial intelligence and what factors have contributed to this?". Another, that could be answered by a much wider audience, seeks to explore the extent to which "efforts [should be] be made to improve the public's understanding of, and engagement with, artificial intelligence" and how they should be pursued.

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.