The log odds ratio is a well-established metric for evaluating the association between binary outcome and exposure variables. Despite its widespread use, there has been limited discussion on how to summarize the log odds ratio as a function of confounders through averaging. To address this issue, we propose the Average Adjusted Association (AAA), which is a summary measure of association in a heterogeneous population, adjusted for observed confounders. To facilitate the use of it, we also develop efficient double/debiased machine learning (DML) estimators of the AAA. Our DML estimators use two equivalent forms of the efficient influence function, and are applicable in various sampling scenarios, including random sampling, outcome-based sampling, and exposure-based sampling. Through real data and simulations, we demonstrate the practicality and effectiveness of our proposed estimators in measuring the AAA.

The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars from many fields. This attention is a timely response to the many urgent questions that this technology raises, but it can sometimes miss important considerations. This paper surveys the evidence for eight potentially surprising such points: 1. LLMs predictably get more capable with increasing investment, even without targeted innovation. 2. Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment. 3. LLMs often appear to learn and use representations of the outside world. 4. There are no reliable techniques for steering the behavior of LLMs. 5. Experts are not yet able to interpret the inner workings of LLMs. 6. Human performance on a task isn't an upper bound on LLM performance. 7. LLMs need not express the values of their creators nor the values encoded in web text. 8. Brief interactions with LLMs are often misleading.

To achieve autonomy in unknown and unstructured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is crucial for safe and efficient robot navigation in environment with mobility-stressing elements that require terrain-specific locomotion policies. We propose the Semantic Belief Graph (SBG), a geometric- and semantic-based representation of a robot's probabilistic roadmap in the environment. The SBG nodes comprise of the robot geometric state and the semantic-knowledge of the terrains in the environment. The SBG edges represent local semantic-based controllers that drive the robot between the nodes or invoke an information gathering action to reduce semantic belief uncertainty. We formulate a semantic-based planning problem on SBG that produces a policy for the robot to safely navigate to the target location with minimal traversal time. We analyze our method in simulation and present real-world results with a legged robotic platform navigating multi-level outdoor environments.

We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual-level data cannot be shared across sites; the sites may also have heterogeneous populations and treatment assignment mechanisms. Motivated by these considerations, we develop federated methods to draw inference on the average treatment effects of combined data across sites. Our methods first compute summary statistics locally using propensity scores and then aggregate these statistics across sites to obtain point and variance estimators of average treatment effects. We show that these estimators are consistent and asymptotically normal. To achieve these asymptotic properties, we find that the aggregation schemes need to account for the heterogeneity in treatment assignments and in outcomes across sites. We demonstrate the validity of our federated methods through a comparative study of two large medical claims databases.

TSCI implements treatment effect estimation from observational data under invalid instruments in the R statistical computing environment. Existing instrumental variable approaches rely on arguably strong and untestable identification assumptions, which limits their practical application. TSCI does not require the classical instrumental variable identification conditions and is effective even if all instruments are invalid. TSCI implements a two-stage algorithm. In the first stage, machine learning is used to cope with nonlinearities and interactions in the treatment model. In the second stage, a space to capture the instrument violations is selected in a data-adaptive way. These violations are then projected out to estimate the treatment effect.

Artificial intelligence (AI) has been advancing at an unprecedented pace, and its development and deployment have sparked concerns among prominent AI experts, tech entrepreneurs, and scientists. A letter written by the Future of Life Institute, an organization focused on technological risks to humanity, calls for a pause on the development and testing of AI technologies more powerful than OpenAI's language model GPT-4 so that the risks it may pose can be properly studied. The letter has been signed by hundreds of individuals, including those working on advanced AI models. The letter warns that language models like GPT-4 can already compete with humans at a growing range of tasks and could be used to automate jobs and spread misinformation. Furthermore, the letter raises the distant prospect of AI systems that could replace humans and remake civilization. Therefore, the pause should be "public and verifiable" and should involve all those working on advanced AI models like GPT-4.

Anecdotal accounts were already abound of people losing money to scammers cloning the voices of their relatives. Now, it's apparently become enough of a prevalent -- and serious -- issue that federal regulators feel the need to step in. On Monday, the US Federal Trade Commission (FTC) published a consumer alert on emerging voice cloning scams, warning people that their desperate friend or relative on the other end of the phone asking for money may actually be an AI simulacrum of their voice wielded by a scammer. "All [a scammer] needs is a short audio clip of your family member's voice -- which he could get from content posted online -- and a voice-cloning program," the FTC wrote. "When the scammer calls you, he'll sound just like your loved one."

With a distinguished heritage tracing back to Bell Labs, Bellcore, and Telcordia, our experts pave the way. Peraton Labs delivers innovative solutions and revolutionary new capabilities to solve the most difficult and complex challenges for government agencies, utilities, and commercial customers. Our research and engineering protects mission-critical systems and a broad range of initiatives in computer network defense, electronic warfare, secure-by-design techniques, and cyber operations and experimentation platforms. Your expertise and areas of interest may be applied on one or more programs, providing you new opportunities to learn and grow. In this position you will work with a small engineering team to develop target recognition algorithms for military applications.

Ukraine has branded Russia's presidency of the UN Security Council for the month of April "a symbolic blow," joining a chorus of outrage from Western countries. Moscow assumes the presidency as part of its monthly rotation between the Security Council's 15 member states, with ties with the West at their lowest point since the Cold War over Russia's invasion of Ukraine. Andriy Yermak, the Ukrainian president's chief of staff, said Russia's tenure was a "symbolic blow." It is another symbolic blow to the rules-based system of international relations," he wrote on Twitter. Ukraine's Foreign Minister Dmytro Kuleba said Russia assuming the presidency was "a slap in the face to the international community". "I urge the current UNSC members to thwart any Russian attempts to abuse its presidency," he wrote on Twitter on Saturday, calling Russia "an outlaw on the UNSC". Moscow last chaired the council in February 2022, the same month it invaded Ukraine – prompting Kyiv to call for Russia's removal from the council. Russia will hold little influence on decisions but will be in charge of the agenda. Moscow has said Foreign Minister Sergey Lavrov is planning to chair a UN Security Council meeting this month on "effective multilateralism". Russian foreign ministry spokeswoman Maria Zakharova also said that Lavrov would lead a debate on the Middle East on April 25. The Kremlin said on Friday it planned to "exercise all its rights" in the role. The White House urged Russia to "conduct itself professionally" when it assumes the role, saying there was no means to block Moscow from the post. "A country that flagrantly violates the UN Charter and invades its neighbour has no place on the UN Security Council," White House spokesperson Karine Jean-Pierre said on Friday. "Unfortunately, Russia is a permanent member of the Security Council and no feasible international legal pathway exists to change that reality," she added, calling the presidency "a largely ceremonial position". The Baltic states also expressed their concern. Estonia's UN envoy Rein Tammsaar, speaking also on behalf of Latvia and Lithuania, warned the Security Council Friday as it met to discuss Russia's plans to deploy tactical nuclear weapons in neighbouring Belarus. "Isn't it telling that tomorrow, on the anniversary of the Bucha killings, Russia will assume the Presidency of the UN Security Council?

NASA shared a video taken by its Solar Dynamic Observatory showing dark patches on the sun, giving the illusion of a smile. NASA said Thursday that a new computer model that combines artificial intelligence and agency satellite data could help prepare for dangerous space weather. The model, called DAGGER (Deep Learning Geomagnetic Perturbation), uses the technical tool to analyze spacecraft measurements of the solar wind and forecast where an impending solar storm will strike on Earth – with 30 minutes of advance warning. An international team of researchers at the Frontier Development Lab said the model can produce predictions in less than a second, with predictions updating every minute. The lab is a partnership that includes NASA, the U.S. Geological Survey and the Department of Energy.