Artificial Intelligence Impact Assessments ("AIIAs"), a family of tools that provide structured processes to imagine the possible impacts of a proposed AI system, have become an increasingly popular proposal to govern AI systems. Recent efforts from government or private-sector organizations have proposed many diverse instantiations of AIIAs, which take a variety of forms ranging from open-ended questionnaires to graded score-cards. However, to date that has been limited evaluation of existing AIIA instruments. We conduct a classroom study (N = 38) at a large research-intensive university (R1) in an elective course focused on the societal and ethical implications of AI. We assign students to different organizational roles (for example, an ML scientist or product manager) and ask participant teams to complete one of three existing AI impact assessments for one of two imagined generative AI systems. In our thematic analysis of participants' responses to pre- and post-activity questionnaires, we find preliminary evidence that impact assessments can influence participants' perceptions of the potential risks of generative AI systems, and the level of responsibility held by AI experts in addressing potential harm. We also discover a consistent set of limitations shared by several existing AIIA instruments, which we group into concerns about their format and content, as well as the feasibility and effectiveness of the activity in foreseeing and mitigating potential harms. Drawing on the findings of this study, we provide recommendations for future work on developing and validating AIIAs.

Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks, achieved through physical objects that can corrupt their predictions, raises serious security concerns for their application in safety-critical domains. Existing defense methods focus on single-frame analysis and are characterized by high computational costs that limit their applicability in multi-frame scenarios, where real-time decisions are crucial. To address this problem, this paper proposes an efficient attention-based defense mechanism that exploits adversarial channel-attention to quickly identify and track malicious objects in shallow network layers and mask their adversarial effects in a multi-frame setting. This work advances the state of the art by enhancing existing over-activation techniques for real-world adversarial attacks to make them usable in real-time applications. It also introduces an efficient multi-frame defense framework, validating its efficacy through extensive experiments aimed at evaluating both defense performance and computational cost.

Imagine going to space and deciding between Spaceship 1 and Spaceship 2. Although it has never been in flight, Spaceship 1 comes with precise equations outlining how it operates. Even though it is unknown how Spaceship 2 flies, it has undergone considerable testing and years of successful flights, including the one you are about to take. Cassie Kozyrkov, chief decision scientist at Google, posed this dilemma at the World Summit AI in 2018. We cannot provide a solution to this question because it is philosophical and perhaps generates a more profound inquiry on which better inspires trust--explanation or testing. For a while, it appeared one issue with artificial intelligence (AI) algorithms, particularly cutting-edge deep learning techniques, was they were black boxes.

In recent years, there has been a boom in various applications implementing artificial intelligence systems. Nowadays, the most striking representatives of artificial intelligence (AI) are chatbots. The most popular of them is ChatGPT, developed by Microsoft company groups. Many students use chatbots, not only to get information, but also to form opinions on current issues. Chatbots have spread rapidly all over the world; the leading IT corporations each have created their own versions.

Sign up to receive our weekly newsletter of the best New Yorker podcasts. The State Department's Special Envoy to Monitor and Combat Antisemitism, the historian Deborah Lipstadt, says the prejudice is coming "from all ends of the political spectrum, and in between." It threatens not only Jews, she says, but the stability of democracies. Lipstadt and David Remnick discuss how antisemitic sentiments may overlap in complicated ways with political opposition to Israel, including anti-Zionism. Plus, The New Yorker's ideas editor speaks with Geoffrey Hinton, the computer scientist known as the godfather of A.I. Hinton pioneered neural networks, the artificial brains that power ChatGPT, for example.

Entrepreneur Andrew Yang ran a surprisingly successful presidential campaign in 2020, captivating the internet with fresh ideas and a fun, geeky persona. More than any other candidate, Yang seemed to channel the optimistic spirit of science fiction shows like Star Trek. "There are a bunch of things that are happening now that mean we should be thinking more ambitiously about what our society could and should look like, and I ran for president on those ideas," Yang says in Episode 554 of the Geek's Guide to the Galaxy podcast. "I'd like to think that I was the presidential candidate that a lot of science fiction and fantasy people would recognize as one of their own." Yang, author of the nonfiction books Forward and The War on Normal People, recently released his first novel, The Last Election, about a plot by the Joint Chiefs of Staff to seize power in the wake of a disputed election.

Coolant leaks, space debris collisions and unplanned engine thrusts are just some of the unexpected challenges astronauts aboard the International Space Station must overcome. NASA intends to build civilian housing on the lunar surface using 3D-printing robots within two decades, according to several of the organization's scientists. The agency is developing concepts for lunar rocket landing pads, 3D printers, concrete mixtures, construction robots and more to complete structures that would shelter humans on the moon by 2040, according to the New York Times. NASA plans to send a construction robot to the moon, which will use mineral fragments, dust and lunar concrete from the moon's surface to build the dwellings. The workroom inside of NASA's 3D printed Crew Health and Performance Exploration Analog habitat built by ICON.

Amman, Jordan – The Israeli army released footage on October 22 of its Maglan commando unit deploying a new precision-guided 120mm mortar bomb called the Iron Sting, against Hamas in Gaza. The bomb's Haifa-based manufacturer, Elbit Systems, has been advertising its qualities on the public relations page of its website since March 2021, when it was integrated into the Israeli military. Benny Gantz, then Israel's defence minister and now a part of Prime Minister Benjamin Netanyahu's war cabinet, described the Iron Sting as "designed to engage targets precisely, in both open terrains and urban environments, while reducing the possibility of collateral damage and preventing injury to non-combatants". It's a claim echoed by Mark Regev, Netanyahu's former spokesperson, for the country's overall approach to its war on Gaza, in which, he has said, Israel is "trying to be as surgical as humanly possible". Yet, more than one month after Israel launched the aerial bombardment of Gaza following a surprise Hamas attack, it has killed at least 11,400 Palestinian civilians, and injured 30,000 in the besieged strip and the occupied West Bank.

Ukraine's armed forces claim to have made significant headway via a series of attacks on the Russian-occupied east bank of the Dnipro river. The country's Marine Corps said in a statement published on social media on Friday that it had gained "a foothold on several bridgeheads" of Dnipro, near the key southern city of Kherson. The waterway is the de facto front line in the south of Ukraine. However, Russia conceded for the first time this week that Ukrainian forces had claimed back some territory on the opposing bank. "The Defence Forces of Ukraine conducted a series of successful operations on the left bank of the Dnipro River, along the Kherson front," the marines said, and "managed to gain a foothold on several bridgeheads."

This paper addresses a gap in the current state of the art by providing a solution for modeling causal relationships that evolve over time and occur at different time scales. Specifically, we introduce the multiscale non-stationary directed acyclic graph (MN-DAG), a framework for modeling multivariate time series data. Our contribution is twofold. Firstly, we expose a probabilistic generative model by leveraging results from spectral and causality theories. Our model allows sampling an MN-DAG according to user-specified priors on the time-dependence and multiscale properties of the causal graph. Secondly, we devise a Bayesian method named Multiscale Non-stationary Causal Structure Learner (MN-CASTLE) that uses stochastic variational inference to estimate MN-DAGs. The method also exploits information from the local partial correlation between time series over different time resolutions. The data generated from an MN-DAG reproduces well-known features of time series in different domains, such as volatility clustering and serial correlation. Additionally, we show the superior performance of MN-CASTLE on synthetic data with different multiscale and non-stationary properties compared to baseline models. Finally, we apply MN-CASTLE to identify the drivers of the natural gas prices in the US market. Causal relationships have strengthened during the COVID-19 outbreak and the Russian invasion of Ukraine, a fact that baseline methods fail to capture. MN-CASTLE identifies the causal impact of critical economic drivers on natural gas prices, such as seasonal factors, economic uncertainty, oil prices, and gas storage deviations.