Perplexity, an AI search engine that has courted controversy by lifting liberally from news articles and skirting web-scraping rules, this week promised to serve as a reliable source for live information on the tightly contested US presidential election. Perplexity promised that its Election Information Hub would serve as "an entry point for understanding key issues, voting intelligently, and tracking election results." "There is only one AI that can do this," Perplexity's CEO, Aravind Srinivas posted on X. Srinivas appeared to troll the publisher of The New York Times by posting a message on X offering to help while Times Tech Guild workers strike during contract negotiations; he later posted that the offer was for infrastructure rather than AI-generated content. Perplexity's tool did not end up making any gaffes last night, providing mostly accurate voting information and also accurately tracking the results as they came in--but largely because it dialed down the use of AI. Perplexity is currently finalizing a funding round worth 500 million that would give the company a valuation of 9 billion, a source familiar with the situation confirmed to WIRED yesterday.

Chinese scientists claim to have created a'real-life Death Star' capable of devastating enemy satellites in orbit. The science-fiction-inspired weapon combines pulses of microwave radiation into a single powerful beam - just like the planet-destroying lasers shown in Star Wars. In order to merge, the electromagnetic pulses must hit the exact same target within 170 trillionths of a second. That requires levels of timing more precise than the atomic clocks on advanced GPS satellites - a feat previously thought to be impossible. However, the weapon has now completed experimental trials for potential military applications thanks to breakthroughs in'ultra-high time precision synchronisation'.

At long last, NASA's Europa Clipper mission is on its way. It launched on October 14 and is now en route to its target: Jupiter's ice-covered moon Europa, whose frozen shell almost certainly conceals a warm saltwater ocean. When the spacecraft gets there, it will conduct dozens of close flybys in order to determine what that ocean is like and, crucially, where it might be hospitable to life. Europa Clipper is still years away from its destination--it is not slated to reach the Jupiter system until 2030. But that hasn't stopped engineers and scientists from working on what would come next if the results are promising: a mission capable of finding evidence of life itself. Living as long as a thousand years, the American chestnut tree once dominated parts of the Eastern forest canopy, with many Native American nations relying on them for food.

Nowadays, it's easy to get fooled by AI content on the web. Whether it's a picture of the Pope sporting a puffy Balenciaga jacket or Trump getting tackled and arrested, these AI-generated images appear super realistic (as long as you're not looking too close), so it can be hard to separate fact from fiction. This is because AI doesn't really understand context in the cultural or historical sense. While some AI-generated images are harmless and do not spread misinformation, others, especially ones involving celebrities or politicians, can cause a great deal of damage and brain rot. Heck, I consider myself to be a relatively tech-savvy person and even I've been fooled once or twice.

Election day is here and so too is the anxiety that has parked itself in the middle of the room. Some people are trying their best to meet the moment -- with mixed results -- while others have simply chosen not to let the race for president and control of Congress dominate their lives. For most all of the voters we spoke to, it's easier said than done. They planned to go for a hike and visit art galleries around downtown L.A. but admitted they were stressed about the election. "We're just sort of out walking around and trying to have a pleasant day and not think about it too much," Mark "I think we'll be glued to our TVs tonight to find out how the rest of our lives are gonna go."

Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the non-Euclidean domain. Naturally, GNNs can effectively tackle the challenges posed by diverse modalities, multiple sensors, and the heterogeneous nature of EO data. To introduce GNNs in the related domains, our review begins by offering fundamental knowledge on GNNs. Then, we summarize the generic problems in EO, to which GNNs can offer potential solutions. Following this, we explore a broad spectrum of GNNs' applications to scientific problems in Earth systems, covering areas such as weather and climate analysis, disaster management, air quality monitoring, agriculture, land cover classification, hydrological process modeling, and urban modeling. The rationale behind adopting GNNs in these fields is explained, alongside methodologies for organizing graphs and designing favorable architectures for various tasks. Furthermore, we highlight methodological challenges of implementing GNNs in these domains and possible solutions that could guide future research. While acknowledging that GNNs are not a universal solution, we conclude the paper by comparing them with other popular architectures like transformers and analyzing their potential synergies.

Cooperatively Localizing robots should seek optimal control strategies to maximize precision of position estimation and ensure safety in flight. Observability-Aware Trajectory Optimization has strong potential to address this issue, but no concrete link between observability and precision has been proven yet. In this paper, we prove that improvement in positioning precision inherently follows from optimizing observability. Based on this finding, we develop an Observability-Aware Control principle to generate observability-optimal control strategies. We implement this principle in a Model Predictive Control framework, and we verify it on a team of quadrotor Unmanned Aerial Vehicles comprising a follower vehicle localizing itself by tracking a leader vehicle in both simulations and real-world flight tests. Our results demonstrate that maximizing observability contributed to improving global positioning precision for the quadrotor team.

Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Language models (LMs) struggle to perform such reasoning consistently. We propose an approach to pinpoint and rectify multi-hop reasoning failures through targeted memory injections on LM attention heads. First, we analyze the per-layer activations of GPT-2 models in response to single- and multi-hop prompts. We then propose a mechanism that allows users to inject relevant prompt-specific information, which we refer to as "memories," at critical LM locations during inference. By thus enabling the LM to incorporate additional relevant information during inference, we enhance the quality of multi-hop prompt completions. We empirically show that a simple, efficient, and targeted memory injection into a key attention layer often increases the probability of the desired next token in multi-hop tasks, by up to 424%. We observe that small subsets of attention heads can significantly impact the model prediction during multi-hop reasoning. To more faithfully interpret these heads, we develop Attention Lens: an open source tool that translates the outputs of attention heads into vocabulary tokens via learned transformations called lenses. We demonstrate the use of lenses to reveal how a model arrives at its answer and use them to localize sources of model failures such as in the case of biased and malicious language generation.

Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the socio-cultural values that they express, for reasons of safety, accuracy, inclusion, and cultural fidelity. We present a validated approach to automatically (1) extracting heterogeneous latent value propositions from texts, (2) assessing resonance and conflict of values with texts, and (3) combining these operations to characterize the pluralistic value alignment of human-sourced and LLM-sourced textual data.

Short video platforms, such as YouTube, Instagram, or TikTok, are used by billions of users globally. These platforms expose users to harmful content, ranging from clickbait or physical harms to misinformation or online hate. Yet, detecting harmful videos remains challenging due to an inconsistent understanding of what constitutes harm and limited resources and mental tolls involved in human annotation. As such, this study advances measures and methods to detect harm in video content. First, we develop a comprehensive taxonomy for online harm on video platforms, categorizing it into six categories: Information, Hate and harassment, Addictive, Clickbait, Sexual, and Physical harms. Next, we establish multimodal large language models as reliable annotators of harmful videos. We analyze 19,422 YouTube videos using 14 image frames, 1 thumbnail, and text metadata, comparing the accuracy of crowdworkers (Mturk) and GPT-4-Turbo with domain expert annotations serving as the gold standard. Our results demonstrate that GPT-4-Turbo outperforms crowdworkers in both binary classification (harmful vs. harmless) and multi-label harm categorization tasks. Methodologically, this study extends the application of LLMs to multi-label and multi-modal contexts beyond text annotation and binary classification. Practically, our study contributes to online harm mitigation by guiding the definitions and identification of harmful content on video platforms.