We propose an Economic Lexicon (EL) specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: 1) to have a wide coverage of terms used in documents discussing economic concepts, and 2) to provide a human-annotated sentiment score in the range [-1,1]. We illustrate the use of the EL in the context of a simple sentiment measure and consider several applications in economics. The comparison to other lexicons shows that the EL is superior due to its wider coverage of domain relevant terms and its more accurate categorization of the word sentiment.

Detecting and tracking emerging trends and weak signals in large, evolving text corpora is vital for applications such as monitoring scientific literature, managing brand reputation, surveilling critical infrastructure and more generally to any kind of text-based event detection. Existing solutions often fail to capture the nuanced context or dynamically track evolving patterns over time. BERTrend, a novel method, addresses these limitations using neural topic modeling in an online setting. It introduces a new metric to quantify topic popularity over time by considering both the number of documents and update frequency. This metric classifies topics as noise, weak, or strong signals, flagging emerging, rapidly growing topics for further investigation. Experimentation on two large real-world datasets demonstrates BERTrend's ability to accurately detect and track meaningful weak signals while filtering out noise, offering a comprehensive solution for monitoring emerging trends in large-scale, evolving text corpora. The method can also be used for retrospective analysis of past events. In addition, the use of Large Language Models together with BERTrend offers efficient means for the interpretability of trends of events.

The multivariate Gaussian distribution underpins myriad operations-research, decision-analytic, and machine-learning models (e.g., Bayesian optimization, Gaussian influence diagrams, and variational autoencoders). However, despite recent advances in adversarial machine learning (AML), inference for Gaussian models in the presence of an adversary is notably understudied. Therefore, we consider a self-interested attacker who wishes to disrupt a decisionmaker's conditional inference and subsequent actions by corrupting a set of evidentiary variables. To avoid detection, the attacker also desires the attack to appear plausible wherein plausibility is determined by the density of the corrupted evidence. We consider white- and grey-box settings such that the attacker has complete and incomplete knowledge about the decisionmaker's underlying multivariate Gaussian distribution, respectively. Select instances are shown to reduce to quadratic and stochastic quadratic programs, and structural properties are derived to inform solution methods. We assess the impact and efficacy of these attacks in three examples, including, real estate evaluation, interest rate estimation and signals processing. Each example leverages an alternative underlying model, thereby highlighting the attacks' broad applicability. Through these applications, we also juxtapose the behavior of the white- and grey-box attacks to understand how uncertainty and structure affect attacker behavior.

A pigeon-inspired robot has solved the mystery of how birds fly without the vertical tail fins that human-designed aircraft rely on. Its makers say the prototype could eventually lead to passenger aircraft with less drag, reducing fuel consumption. Tail fins, also known as vertical stabilisers, allow aircraft to turn from side to side and help avoid changing direction unintentionally. Some military planes, such as the Northrop B-2 Spirit, are designed without a tail fin because it makes them less visible to radar. Instead, they use flaps that create extra drag on just one side when needed, but this is an inefficient solution.

Many people in Nashville say they don't trust artificial intelligence chatbots to give them unbiased information amid the backlash Google faces over its Gemini program. On Tuesday, the Department of Education's Office for Civil Rights (OCR) released presidentially-mandated guidance that lays out how schools' use of artificial intelligence (AI) can be discriminatory toward minority and transgender students, "likely" opening them up to federal investigations. President Biden signed Executive Order 14110 last year mandating that the Education Department develop resources, policies and guidance regarding AI in schools to help ensure responsible and non-discriminatory use, "including the impact AI systems have on vulnerable and underserved communities." "The growing use of AI in schools, including for instructional and school safety purposes, and AI's ability to operate on a mass scale can create or contribute to discrimination," the Education Department's guidance states. "This resource provides information regarding federal civil rights laws in OCR's jurisdiction and includes examples of types of incidents that could, depending on the facts and circumstances, present OCR with sufficient reason to open an investigation."

But assigning responsibility is complicated. These three visualizations help explain why. Take advantage of epic savings on award-winning reporting, razor-sharp analysis, and expert insights on your favorite technology topics. Subscribe today to save 50% on an annual subscription, plus receive a free digital copy of our "Generative AI and the future of work" report. This could be the cultivated meat industry's future: as a luxury product for the few.

The UK government is expected to launch a parliamentary inquiry into the roll of social media in summer riots, particularly around the use of generative AI, The Guardian reported. As part of that, MPs (members of Parliament) wish to cross-examine X owner Elon Musk, along with senior executives from Meta and TikTok, as part of a Commons science and technology select committee social media inquiry. "[Musk] has very strong views on multiple aspects of this," said Labour chair of the select committee, Chi Onwurah. "I would certainly like the opportunity to cross-examine him to see … how he reconciles his promotion of freedom of expression with his promotion of pure disinformation. The government is looking into the use of fake images created by generative AI, often containing Islamophobic content, which were widely shared in social media posts on Facebook and X.

The head of an education technology startup that created a highly touted chatbot for the Los Angeles school system has been arrested and charged with fraud. Federal prosecutors, in an indictment unsealed Tuesday, accused Joanna Smith-Griffin of defrauding investors and charged her with securities fraud, wire fraud and aggravated identity theft. Smith-Griffin, 33, is the founder and former chief executive of AllHere, the Boston-based company that created "Ed," an artificial-intelligence tool billed as revolutionary for students' education and the interaction between the L.A. Unified School District and the families it serves. After unveiling the chatbot with great fanfare in March, L.A. school officials, months later, quietly disconnected the tool -- which was supposed to respond to any question from students or parents in an accurate, helpful and private manner. LAUSD board members at Tuesday's meeting will consider resolutions on immigration sanctuary, LGBTQ protection and accelerating the teaching of current events.

Astronauts aboard the International Space Station (ISS) had slower memory, attention and processing speed after six months, raising concerns about the impact of cognitive impairment on future space missions to Mars. The extreme environment of space, with reduced gravity, harsh radiation and the lack of regular sunrises and sunsets, can have dramatic effects on astronaut health, from muscle loss to an increased risk of heart disease. However, the cognitive effects of long-term space travel are less well documented. Inside NASA's ambitious plan to bring the ISS crashing back to Earth Now, Sheena Dev at NASA's Johnson Space Center in Houston, Texas, and her colleagues have looked at the cognitive performance of 25 astronauts during their time on the ISS. The team ran the astronauts through 10 tests, some of which were done on Earth, once before and twice after the mission, while others were done on the ISS, both early and later in the mission.

Extended time in space is not exactly harmless to the human body. Radiation, altered gravity, sleep loss, can all take their toll on astronauts. Some are even hospitalized upon their return to Earth. Minor mistakes in space can have devastating consequences, so it is important to know how these stresses can impact an astronaut's cognitive performance. A new study published November 20 in the journal Frontiers in Physiology followed 25 astronauts in Low Earth orbit aboard the International Space Station (ISS).