Last month, the British television network Channel 4 broadcast an "alternative Christmas address" by Queen Elizabeth II, in which the 94-year-old monarch was shown cracking jokes and performing a dance popular on TikTok. Of course, it wasn't real: The video was produced as a warning about deepfakes--apparently real images or videos that show people doing or saying things they never did or said. If an image of a person can be found, new technologies using artificial intelligence and machine learning now make it possible to show that person doing almost anything at all. The dangers of the technology are clear: A high-school teacher could be shown in a compromising situation with a student, a neighbor could be depicted as a terrorist. Can deepfakes, as such, be prohibited under American law?

In the year 2020, one thing that turned out to have a massive impact on our daily lives and society is artificial intelligence. The Government of Telangana has even declared 2020 as the year of artificial intelligence. Starting from GPT-3 and improvements in health-tech to a conversation on ethical AI and advancements in neural networks, the year has seen it all. And this is the time when businesses are going to come out and talk about their contribution to the field of AI, as well as research and developments around it. With the starting of this new year, we have come up with a list of upcoming AI conferences that one can attend in 2021, to keep themselves at the forefront of this technology.

Labs across the UK are to be upgraded to help tackle infectious diseases, cut greenhouse emissions and more -- thanks to a £213 million government investment. The support -- part of the British government's wider'Research & Development Roadmap' -- was announced yesterday by Science Minister Amanda Solloway. It will give British scientists access to facilities including super computers in Cardiff to track infectious diseases and a floating offshore wind testing lab in Plymouth. The government's roadmap aims to make the UK'the best place in the world for scientists, researchers and entrepreneurs to live and work.' The new investment will not only provide support for the sciences, however, but will also be used to promote research in the arts and humanities.

In 1991 two hikers in the Italian Alps stumbled on a mummified body buried in the ice. The Iceman, it turned out, died more than 5,000 years ago. At first, archeologists assumed he'd fallen in a snowstorm and frozen to death. Then they discovered various cuts and bruises on his body and an arrowhead embedded in his shoulder. They also found traces of blood on the stone knife he was carrying. Most likely, he died fighting. Canadian historian Margaret MacMillan regards the Iceman story as emblematic of our violent nature. Humans are a quarrelsome lot with a special talent for waging war. In her book War: How Conflict Shaped Us, she argues that warfare is so deeply embedded in human history that we barely recognize its ripple effects.

Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being deployed across multiple functionalities, ranging from healthcare devices and wearables to critical infrastructures, e.g., nuclear power plants, autonomous vehicles, smart cities, and smart homes. These devices are inherently not secure across their comprehensive software, hardware, and network stacks, thus presenting a large attack surface that can be exploited by hackers. In this article, we present an innovative technique for detecting unknown system vulnerabilities, managing these vulnerabilities, and improving incident response when such vulnerabilities are exploited. The novelty of this approach lies in extracting intelligence from known real-world CPS/IoT attacks, representing them in the form of regular expressions, and employing machine learning (ML) techniques on this ensemble of regular expressions to generate new attack vectors and security vulnerabilities. Our results show that 10 new attack vectors and 122 new vulnerability exploits can be successfully generated that have the potential to exploit a CPS or an IoT ecosystem. The ML methodology achieves an accuracy of 97.4% and enables us to predict these attacks efficiently with an 87.2% reduction in the search space. We demonstrate the application of our method to the hacking of the in-vehicle network of a connected car. To defend against the known attacks and possible novel exploits, we discuss a defense-in-depth mechanism for various classes of attacks and the classification of data targeted by such attacks. This defense mechanism optimizes the cost of security measures based on the sensitivity of the protected resource, thus incentivizing its adoption in real-world CPS/IoT by cybersecurity practitioners.

When the inquisition required him to drop his study of what the Roman Catholic Church insisted was not a heliocentric solar system, Galileo Galilei turned his energy to the less controversial question of how to stick a telescope onto a helmet. The king of Spain had offered a hefty reward to anyone who could solve the stubborn mystery of how to determine a ship's longitude while at sea: 6,000 ducats up front and another 2,000 per year for life. Galileo thought his headgear, with the telescope fixed over one eye and making its wearer look like a misaligned unicorn, would net him the reward. Determining latitude is easy for any sailor who can pick out the North Star, but finding longitude escaped the citizens of the 17th century, because it required a precise knowledge of time. That's based on a simple principle: Say you set your clock before sailing west from Greenwich.

During President Obama's two terms in the White House, Jason Furman was a top economic policy adviser and a key voice on the growing importance of artificial intelligence. Furman served as deputy director of the National Economic Council before becoming chair of the Council of Economic Advisers. He also coauthored a report issued by the Obama administration in October 2016 that detailed the economic importance of AI to the US. Furman, who is now a professor of the practice of economic policy at Harvard, spoke to WIRED senior writer Will Knight. This transcript has been edited for length and clarity.

Officials at Dulles International Airport in Virginia unveil new biometric facial recognition scanners in September 2018.Bill O'Leary/Getty In 2018, the federal government started scanning people's faces as they drove into and out of the country at the Anzalduas International Bridge, which connects the Rio Grande Valley of Texas to Mexico. Customs and Border Protection said collecting these biometric images would enhance security and make identifying travelers more efficient. But less than a year later, a data breach compromised 100,000 facial images and 105,000 license plate images. Nineteen facial images from the breach were posted to the dark web. Now, CBP wants to expand facial surveillance beyond Anzalduas and other sites that were part of a pilot program, even as the program saw potential security vulnerabilities in at least four airports, according to a report from the Department of Homeland Security inspector general.

Neural networks are sensitive to small, imperceptible to humans, perturbations of their inputs that can cause state-of-the-art classifiers to completely fail [1]. As deep learning models are deployed in realworld applications, guaranteeing their robustness to malicious actors becomes increasingly important: for instance, an adversarial image could evade automated content filtering on social networks [2]. Adversarial attacks can be carried out in different frameworks: in the white-box setting, the attacker has full access to the model, while black-box attacks only rely on queries. It is also possible to craft an attack on a different model and transfer it to the model targeted [3]. There is no universal defense, and state-ofthe-art techniques often come with a large computational cost, as well as reduced natural accuracy [4]. Some of these defenses rely on obfuscated gradients: the model is designed so that the gradients are unsuitable for attacks, for instance by using non-differentiable layers. However, attackers can choose to alter the network structure, using Backward Pass Differentiable Approximation (BPDA) [5], replacing obfuscating layers with well-behaved approximations. Furthermore, approaches relying on obfuscation do not generally provide robustness against transfer and black-box attacks.

When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to regionalize - to divide a large spatial domain into multiple regions and study each region separately - instead of fitting a single model on the entire data (also known as unification). Traditional wisdom in these fields suggests that models built for each region separately will have higher performance because of homogeneity within each region. However, by partitioning the training data, each model has access to fewer data points and cannot learn from commonalities between regions. Here, through two hydrologic examples (soil moisture and streamflow), we argue that unification can often significantly outperform regionalization in the era of big data and deep learning (DL). Common DL architectures, even without bespoke customization, can automatically build models that benefit from regional commonality while accurately learning region-specific differences. We highlight an effect we call data synergy, where the results of the DL models improved when data were pooled together from characteristically different regions. In fact, the performance of the DL models benefited from more diverse rather than more homogeneous training data. We hypothesize that DL models automatically adjust their internal representations to identify commonalities while also providing sufficient discriminatory information to the model. The results here advocate for pooling together larger datasets, and suggest the academic community should place greater emphasis on data sharing and compilation.