Security properties are often focused on the technological side of the system. One implicitly assumes that the users will behave in the right way to preserve the property at hand. In real life, this cannot be taken for granted. In particular, security mechanisms that are difficult and costly to use are often ignored by the users, and do not really defend the system against possible attacks. Here, we propose a graded notion of security based on the complexity of the user's strategic behavior. More precisely, we suggest that the level to which a security property $\varphi$ is satisfied can be defined in terms of (a) the complexity of the strategy that the voter needs to execute to make $\varphi$ true, and (b) the resources that the user must employ on the way. The simpler and cheaper to obtain $\varphi$, the higher the degree of security. We demonstrate how the idea works in a case study based on an electronic voting scenario. To this end, we model the vVote implementation of the \Pret voting protocol for coercion-resistant and voter-verifiable elections. Then, we identify "natural" strategies for the voter to obtain receipt-freeness, and measure the voter's effort that they require. We also look at how hard it is for the coercer to compromise the election through a randomization attack.

We consider the problem of active learning in the context of spatial sampling for level set estimation (LSE), where the goal is to localize all regions where a function of interest lies above/below a given threshold as quickly as possible. We present a finite-horizon search procedure to perform LSE in one dimension while optimally balancing both the final estimation error and the distance traveled for a fixed number of samples. A tuning parameter is used to trade off between the estimation accuracy and distance traveled. We show that the resulting optimization problem can be solved in closed form and that the resulting policy generalizes existing approaches to this problem. We then show how this approach can be used to perform level set estimation in higher dimensions under the popular Gaussian process model. Empirical results on synthetic data indicate that as the cost of travel increases, our method's ability to treat distance nonmyopically allows it to significantly improve on the state of the art. On real air quality data, our approach achieves roughly one fifth the estimation error at less than half the cost of competing algorithms.

Surrogate modeling and active subspaces have emerged as powerful paradigms in computational science and engineering. Porting such techniques to computational models in the social sciences brings into sharp relief their limitations in dealing with discontinuous simulators, such as Agent-Based Models, which have discrete outputs. Nevertheless, prior applied work has shown that surrogate estimates of active subspaces for such estimators can yield interesting results. But given that active subspaces are defined by way of gradients, it is not clear what quantity is being estimated when this methodology is applied to a discontinuous simulator. We begin this article by showing some pathologies that can arise when conducting such an analysis. This motivates an extension of active subspaces to discontinuous functions, clarifying what is actually being estimated in such analyses. We also conduct numerical experiments on synthetic test functions to compare Gaussian process estimates of active subspaces on continuous and discontinuous functions. Finally, we deploy our methodology on Flee, an agent-based model of refugee movement, yielding novel insights into which parameters of the simulation are most important across 8 displacement crises in Africa and the Middle East.

The Biden administration has announced it is tightening export controls on semiconductor chips used for artificial intelligence and the equipment used to manufacture them, in an effort to prevent China from acquiring or producing advanced chips. The rules update restrictions that the U.S. announced a year ago prohibiting the sale of chips above a certain capability threshold in China and other restricted countries, and banned the sale of specific chip manufacturing equipment. The new rules aim to close loopholes that emerged from the 2022 export control curbs, and to account for technological developments since then. The export restrictions announced Tuesday have been extended to chips that have fewer capabilities than those that were previously subject to the rules. They also imposed controls on additional types of chip manufacturing equipment.

By now the sophistication of false information about Israel and Hamas is clear to anyone who opened their phone this week. As tech platforms rely ever more on artificial intelligence in their battle against disinformation, the havoc in the Middle East exposes the limits of technology to police technology's harms. It is more important than ever that we understand how global platforms like Meta, Google, and X, the platform formerly known as Twitter, make decisions about what content gets amplified and what taken down. It's not as though platforms didn't know they had a huge disinformation problem that human content moderators alone could not solve. Two years ago, Facebook whistleblower Frances Haugen detailed for Congress how growth and profit drove decisions: "The result has been more division, more harm, more lies, more threats and more combat," she testified.

A year ago, the US government introduced chip sanctions aimed at hobbling China's ability to develop advanced artificial intelligence. But those sanctions had loopholes that allowed Chinese firms to keep buying and building chips used to train some of the world's most advanced AI algorithms. Today, the US announced it is tightening controls to try to close those gaps. The new restrictions, announced by the Commerce Department, also impose new rules for reporting the sales of other types of advanced chips, new controls on sales of advanced chipmaking equipment and design software, and statutes to prevent Chinese companies from obtaining chips through foreign subsidiaries. "This is a doubling down by the Biden administration on the goals of last year's export controls," says Gregory Allen, who is director of a center for advanced studies at the Center for Strategic and International Studies and supports the restrictions.

The Biden administration on Tuesday announced additional limits on the kinds of advanced semiconductors that American firms can sell to China, shoring up restrictions issued last October to limit China's progress on supercomputing and artificial intelligence. The rules appear likely to bring to a halt most shipments of advanced semiconductors from the United States to Chinese data centers, which use them to produce models capable of artificial intelligence. More U.S. companies seeking to sell China advanced chips, or the machinery used to make them, will be required to notify the government of their plans, or obtain a special license. To prevent the risk that advanced U.S. chips travel to China through third countries, the United States will also require chip makers to obtain licenses to ship to dozens of other countries that are subject to U.S. arms embargoes. The Biden administration argues that China's access to such advanced technology is dangerous because it could aid the country's military in tasks like guiding hypersonic missiles, setting up advanced surveillance systems or cracking top-secret U.S. codes.

Now I can't say that there's not going to be some slowdown in Chinese capability around artificial intelligence at that high level that could possibly be used for goodness,

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Pras Michel of the Fugees is seeking a new trial by arguing his former lawyer used artificial intelligence to generate his closing argument before the hip-hop artist was found guilty of helping a foreign national launder millions of dollars in illegitimate contributions to former President Barack Obama's campaign. Michel was convicted in April after being accused of taking part in an extensive conspiracy to use about $88 million in foreign funds to engage in illegal back-channel lobbying and make unlawful campaign contributions at the direction of the People's Republic of China. He filed a motion on Monday asking the court for a new trial on all counts.

This article was syndicated from the Bulletin of the Atomic Scientists, which has covered human-made threats to humanity since 1945. The main artery in Montreal's Little Italy is lined with cafés, wine bars, and pastry shops that spill into tree-lined, residential side streets. Generations of farmers, butchers, bakers, and fishmongers sell farm-to-table goods in the neighborhood's large, open-air market, the Marché Jean-Talon. But the quiet enclave also accommodates a modern, 90,000-square-foot global AI hub known as Mila–Quebec AI Institute. Mila claims to house the largest concentration of deep learning academic researchers in the world, including more than 1,000 researchers and more than 100 professors who work with more than 100 industry partners from around the globe.