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Rep. Mike Gallagher, R-Wis., tells'America Reports' that China is the primary geopolitical threat to the U.S. and should be addressed more by presidential candidates. The tentacles of the Chinese Communist Party (CCP) are creeping into an increasing number of U.S. industries in China's coordinated campaign to infiltrate critical industries to the U.S. economy. The CCP views theft of U.S. intellectual property as "a strategic resource," and China remains the largest source of counterfeit and pirated goods in the world. Chinese companies are building electric vehicle (EV) battery plants near key U.S. military installations, and China still controls the global chips market despite investments in U.S. production. China is also flexing its dominance of the rare earth sector in retaliation for U.S. trade policy, and China is in talks to create a new military facility just miles from the American homeland.

The US has expanded the restriction of exports of Nvidia artificial intelligence chips beyond China to some countries in the Middle East. Nvidia, which is one of the world's most valuable companies at $1.2tn, said in a regulatory filing this week the curbs affected its A100 and H100 chips, which are used to accelerate machine-learning tasks on major artificial intelligence apps, such as ChatGPT. The firm said the controls would not have an "immediate material impact" on its results. It did not say which countries in the Middle East were affected by these restrictions. Nvidia's rival in the sector, AMD, had also received an informed letter with similar restrictions, a person familiar with the matter told Reuters.

The rapid advancement of artificial intelligence (AI) has been nothing short of remarkable. From health care to finance, AI is transforming industries and has the potential to elevate human productivity to unprecedented levels. However, this exciting promise is accompanied by a looming concern among the public and some experts: the emergence of "Killer AI." In a world where innovation has already changed society in unexpected ways, how do we separate legitimate fears from those that should still be reserved for fiction? To help answer questions like these, we recently released a policy brief for the Mercatus Center at George Mason University titled "On Defining'Killer AI.'"

The U.S. has expanded the restriction of exports of sophisticated Nvidia artificial-intelligence chips beyond China to other regions including some countries in the Middle East, the company said in a regulatory filing this week. U.S. officials usually impose export controls for national security reasons. A similar move announced last year signaled an escalation of the U.S. crackdown on China's technological capabilities, but it was not immediately clear what risks were posed by exports to the Middle East. The company said the curbs, which affect its A100 and H100 chips designed to speed up machine-learning tasks, would not have an "immediate material impact" on its results.

We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics. Such programs are common in High Energy Physics due to the presence of branching processes and clustering-based analysis. Thus differentiating such programs can open the way for gradient based optimization in the context of detector design optimization, simulator tuning, or data analysis and reconstruction optimization. We discuss several possible gradient estimation strategies, including the recent Stochastic AD method, and compare them in simplified detector design experiments. In doing so we develop, to the best of our knowledge, the first fully differentiable branching program.

Neural networks have gained much interest because of their effectiveness in many applications. However, their mathematical properties are generally not well understood. If there is some underlying geometric structure inherent to the data or to the function to approximate, it is often desirable to take this into account in the design of the neural network. In this work, we start with a non-autonomous ODE and build neural networks using a suitable, structure-preserving, numerical time-discretisation. The structure of the neural network is then inferred from the properties of the ODE vector field. Besides injecting more structure into the network architectures, this modelling procedure allows a better theoretical understanding of their behaviour. We present two universal approximation results and demonstrate how to impose some particular properties on the neural networks. A particular focus is on 1-Lipschitz architectures including layers that are not 1-Lipschitz. These networks are expressive and robust against adversarial attacks, as shown for the CIFAR-10 and CIFAR-100 datasets.

In this article, the optimal sample complexity of learning the underlying interaction/dependencies of a Linear Dynamical System (LDS) over a Directed Acyclic Graph (DAG) is studied. The sample complexity of learning a DAG's structure is well-studied for static systems, where the samples of nodal states are independent and identically distributed (i.i.d.). However, such a study is less explored for DAGs with dynamical systems, where the nodal states are temporally correlated. We call such a DAG underlying an LDS as \emph{dynamical} DAG (DDAG). In particular, we consider a DDAG where the nodal dynamics are driven by unobserved exogenous noise sources that are wide-sense stationary (WSS) in time but are mutually uncorrelated, and have the same {power spectral density (PSD)}. Inspired by the static settings, a metric and an algorithm based on the PSD matrix of the observed time series are proposed to reconstruct the DDAG. The equal noise PSD assumption can be relaxed such that identifiability conditions for DDAG reconstruction are not violated. For the LDS with WSS (sub) Gaussian exogenous noise sources, it is shown that the optimal sample complexity (or length of state trajectory) needed to learn the DDAG is $n=\Theta(q\log(p/q))$, where $p$ is the number of nodes and $q$ is the maximum number of parents per node. To prove the sample complexity upper bound, a concentration bound for the PSD estimation is derived, under two different sampling strategies. A matching min-max lower bound using generalized Fano's inequality also is provided, thus showing the order optimality of the proposed algorithm.

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. When designed well, these systems promise more objective decisions while saving large amounts of resources and freeing up human time. However, when ADM systems are not designed well, they can lead to unfair decisions which discriminate against societal groups. The downstream effects of ADMs critically depend on the decisions made during the systems' design and implementation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these design decisions are made implicitly, without knowing exactly how they will influence the final system. It is therefore important to make explicit the decisions made during the design of ADM systems and understand how these decisions affect the fairness of the resulting system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit design decisions into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible "universes" of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand variability and robustness of algorithmic fairness using an exemplary case study of predicting public health coverage of vulnerable populations for potential interventions. Our results illustrate how decisions during the design of a machine learning system can have surprising effects on its fairness and how to detect these effects using multiverse analysis.

The UK should introduce new legislation to control artificial intelligence or risk falling behind the EU and the US in setting the pace for regulating the technology, MPs have said. Rishi Sunak's government was urged to act as it prepares to host a global AI safety summit at Bletchley Park, home of the Enigma codebreakers, in November. The science, innovation and technology committee said on Thursday the regulatory approach outlined in a recent government white paper risked falling behind others. "The AI white paper should be welcomed as an initial effort to engage with this complex task, but its proposed approach is already risking falling behind the pace of development of AI," the committee said in an interim report on AI governance. "This threat is made more acute by the efforts of other jurisdictions, principally the European Union and the United States, to set international standards." The EU, a trendsetter in tech regulation, is pushing ahead with the AI Act, while in the US the White House has published a blueprint for an AI bill of rights and the US senate majority leader, Chuck Schumer, has published a framework for developing AI regulations.