This study designs an adaptive experiment for efficiently estimating average treatment effect (ATEs). We consider an adaptive experiment where an experimenter sequentially samples an experimental unit from a covariate density decided by the experimenter and assigns a treatment. After assigning a treatment, the experimenter observes the corresponding outcome immediately. At the end of the experiment, the experimenter estimates an ATE using gathered samples. The objective of the experimenter is to estimate the ATE with a smaller asymptotic variance. Existing studies have designed experiments that adaptively optimize the propensity score (treatment-assignment probability). As a generalization of such an approach, we propose a framework under which an experimenter optimizes the covariate density, as well as the propensity score, and find that optimizing both covariate density and propensity score reduces the asymptotic variance more than optimizing only the propensity score. Based on this idea, in each round of our experiment, the experimenter optimizes the covariate density and propensity score based on past observations. To design an adaptive experiment, we first derive the efficient covariate density and propensity score that minimizes the semiparametric efficiency bound, a lower bound for the asymptotic variance given a fixed covariate density and a fixed propensity score. Next, we design an adaptive experiment using the efficient covariate density and propensity score sequentially estimated during the experiment. Lastly, we propose an ATE estimator whose asymptotic variance aligns with the minimized semiparametric efficiency bound.

As the world grapples with how to regulate artificial intelligence, Washington faces a unique dilemma: how to secure America's position as the global AI leader, while guarding against AI's possible risks? Although any country seeking to regulate AI must balance regulation and innovation, this task is especially hard for the United States because we have more to lose. The United Kingdom, European Union, and China all have formidable AI companies, but U.S. firms dominate the field, propelled by our uniquely open innovation ecosystem. This dominance was on display recently, which saw OpenAI release Sora, a powerful new text-to-video platform, and Google introduce Gemini 1.5, its next-generation AI model that can absorb requests more than 30 times the size of its predecessor. If these trends continue, and AI proves the game-changer that many expect--surrendering U.S. leadership is not an option.

The US Army Research Laboratory is exploring whether OpenAI's technology can improve battle planning – although only in the context of a military video game. The US military has already explored using AI technologies to analyse battlefield images and even identify targets for airstrikes – but it only recently began testing large language models and other types of generative AI that empower commercial AI chatbots.

AI expert Marva Bailer explains how Will.i.am's app, FYI, powers his AI co-host for his radio show and why the platform has different capabilities than ChatGPT While artificial intelligence made headlines with ChatGPT, behind the scenes, the technology has quietly pervaded everyday life -- screening job resumes, rental apartment applications, and even determining medical care in some cases. While a number of AI systems have been found to discriminate, tipping the scales in favor of certain races, genders or incomes, there's scant government oversight. Lawmakers in at least seven states are taking big legislative swings to regulate bias in artificial intelligence, filling a void left by Congress' inaction. These proposals are some of the first steps in a decades-long discussion over balancing the benefits of this nebulous new technology with the widely documented risks. "AI does in fact affect every part of your life whether you know it or not," said Suresh Venkatasubramanian, a Brown University professor who co-authored the White House's Blueprint for an AI Bill of Rights.

The letter was signed by experts in AI research, policy, and law, including Stanford University's Percy Liang; Pulitzer Prize-winning journalist Julia Angwin; Renée DiResta from the Stanford Internet Observatory; Mozilla fellow Deb Raji, who has pioneered research into auditing AI models; ex-government official Marietje Schaake, a former member of European Parliament; and Brown University professor Suresh Venkatasubramanian, a former adviser to the White House Office of Science and Technology Policy.

CROSSING THE THRESHOLD – Super Tuesday results expected to move Biden, Trump much closer to an all-but-certain general election rematch. GET A GRIP – Speaker slams House Dems as they attempt to reverse SCOTUS order keeping Trump on ballot. 'YOU'RE LYING!' – AOC loses it on pro-Palestinian protesters confronting her at movie theater: 'It's f---ed up.' Continue reading … BORDER BATTLE – SCOTUS weighs in on Texas law that allows police to arrest, detain illegal migrants. RED TAPE – Congressman proposes using AI to cut down on government regulations. LOW BATTERY – GOP senator targets forced electric vehicle rentals.

FIRST ON FOX: House Rep. Andy Biggs is eyeing artificial intelligence (AI) technology as a way to cut unnecessary government red tape. The Arizona Republican is introducing a bill on Tuesday that would mandate federal agencies use AI to review regulations under their purview with the aim of cutting rules that fail to meet certain standards. "American businesses must be given the opportunity to thrive without overbearing, costly, contradictory, and duplicative regulations mandated by the DC Swamp," Biggs told Fox News Digital. "Federal overregulation takes a colossal toll on the U.S. economy. Thousands of new regulations go into effect every year, and there simply isn't enough manpower or existing technology to sift through previously issued regulations. AI technology is an effective tool that can save taxpayer dollars, benefit American business owners, and promote economic growth."

ABSTRACT Exoplanet discovery at long orbital periods requires reliably detecting individual transits without additional information about the system. Techniques like phase-folding of light curves and periodogram analysis of radial velocity data are more sensitive to planets with shorter orbital periods, leaving a dearth of planet discoveries at long periods. We present a novel technique using an ensemble of Convolutional Neural Networks incorporating the onboard spacecraft diagnostics of Kepler to classify transits within a light curve. We create a pipeline to recover the location of individual transits, and the period of the orbiting planet, which maintains > 80% transit recovery sensitivity out to an 800-day orbital period. Our neural network pipeline has the potential to discover additional planets in the Kepler dataset, and crucially, within the η-Earth regime. We report our first candidate from this pipeline, KOI 1271.02. KOI 1271.01 is known to exhibit strong Transit Timing Variations (TTVs), and so we jointly model the TTVs and transits of both transiting planets to constrain the orbital configuration and planetary parameters and conclude with a series of potential parameters for KOI 1271.02, as there is not enough data currently to uniquely constrain the system. We conclude that KOI 1271.02 has a radius of 5.32 0.20 R INTRODUCTION studies to measure masses and potentially detect their atmospheric composition. Since the discovery of the first exoplanets, there has Thousands of confirmed planets and thousands of been a rapid increase in the number of exoplanets discovered more planet candidate signals have been found within (Wolszczan & Frail 1992; Mayor & Queloz 1995; the Kepler field of view (Borucki et al. 2011; Batalha Charbonneau et al. 2000). With the discovery of more et al. 2013; Thompson et al. 2018; Morton et al. 2016) exoplanets, it became possible to perform demographic as well as within the current TESS sample Guerrero studies of exoplanets and dissect the population along et al. (2021). These discoveries have enabled statistical other axes (such as stellar metallicity, for example). Of particular interest is the occurrence observed roughly 150,000 stars photometrically during rate of Earth-like planets around Sun-like stars (i.e. - its main mission Borucki et al. (2010). Kepler continued η-Earth) (Fressin et al. 2013; Catanzarite & Shao 2011; to observe the sky after two of its reaction wheels broke Petigura et al. 2013; Foreman-Mackey et al. 2014; Farr as the K2 mission Howell et al. (2014). Kepler was a statistical et al. 2014; Silburt et al. 2015; Burke et al. 2015; Traub mission aimed at finding the frequency of Earthlike 2015; Garrett et al. 2018; Mulders et al. 2018; Hsu et al. planets around Sun-like stars, η-Earth.

The voluminous nature of geospatial temporal data from physical monitors and simulation models poses challenges to efficient data access, often resulting in cumbersome temporal selection experiences in web-based data portals. Thus, selecting a subset of time steps for prioritized visualization and pre-loading is highly desirable. Addressing this issue, this paper establishes a multifaceted definition of salient time steps via extensive need-finding studies with domain experts to understand their workflows. Building on this, we propose a novel approach that leverages autoencoders and dynamic programming to facilitate user-driven temporal selections. Structural features, statistical variations, and distance penalties are incorporated to make more flexible selections. User-specified priorities, spatial regions, and aggregations are used to combine different perspectives. We design and implement a web-based interface to enable efficient and context-aware selection of time steps and evaluate its efficacy and usability through case studies, quantitative evaluations, and expert interviews.

Stance detection of social media text is a key component of downstream tasks involving the identification of groups of users with opposing opinions on contested topics such as vaccination and within arguments. In particular, stance provides an indication of an opinion towards an entity. This paper introduces DIVERSE, a dataset of over 173,000 YouTube video comments annotated for their stance towards videos of the U.S. military. The stance is annotated through a human-guided, machine-assisted labeling methodology that makes use of weak signals of tone within the sentence as supporting indicators, as opposed to using manual annotations by humans. These weak signals consist of the presence of hate speech and sarcasm, the presence of specific keywords, the sentiment of the text, and the stance inference from two Large Language Models. The weak signals are then consolidated using a data programming model before each comment is annotated with a final stance label. On average, the videos have 200 comments each, and the stance of the comments skews slightly towards the "against" characterization for both the U.S. Army and the videos posted on the channel.