Amid an outbreak of recent wildfires in California, Google announced a commitment to spend 13 million to improve satellite imaging to help track and detect wildfires, starting as early as next year. FireSat, a constellation of more than 50 satellites, will be able to detect wildfires as small as the size of a classroom, about 16 by 16 feet, and the first satellite will launch in early 2025, the media giant announced Monday. Firefighting authorities currently rely on satellite imagery that detects wildfires but only when they reach about the size of a football field, or more than an acre. "We realized that if we can pair satellites with machine learning and artificial intelligence, it was the perfect platform to generate real-time operational intelligence on fires," Christopher Van Arsdale, who leads the Google Research Climate & Energy group and is chairman of the Earth Fire Alliance, said in a video announcement. The initiative is being led by the Earth Fire Alliance, a nonprofit that was launched in May to create FireSat and develop wildfire datasets, with funding from Google and the Gordon and Betty Moore Foundation.

Now that alleged would-be Trump assassin Ryan Routh is in custody, the FBI and Florida police will have their hands full unraveling his planning process and what may have motivated him. Former NYPD investigator and security expert Patrick Brosnan told Fox News Digital that investigators will need to trawl through a litany of information in the coming weeks, including "all things cellular, online shopping; phone camera images, bank records, email correspondence, recent search engine inquiries, dating app activity, identification of any possible burner phones, footage from … city streets, UPS trucks, Amazon trucks or backup cameras, and all cell tower pings within a fixed distance." Using this information, investigators will build Routh's profile to answer these questions, according to Gene Petrino, a SWAT commander with nearly three decades in law enforcement and a master's degree in security management. Ryan W. Routh, suspected of attempting to assassinate Republican presidential nominee former President Trump at his West Palm Beach golf course, stands handcuffed after his arrest during a traffic stop near Palm City, Florida, Sept. 15, 2024. Petrino said investigators will obtain warrants to scour Routh's social media and speak with his family and associates to determine whether someone else was involved in planning his assassination attempt on Sunday afternoon or anyone who may have trained him beforehand.

Inventor and futurist Ray Kurzweil, researcher and Brookings Institution fellow Chinasa T. Okolo, director of the U.S. Artificial Safety Institute (AISI) Elizabeth Kelly, and Cognizant CEO Ravi Kumar S, discussed the transformative power of AI during a panel at a TIME100 Impact Dinner in San Francisco on Monday. During the discussion, which was moderated by TIME's editor-in-chief Sam Jacobs, Kurzweil predicted that we will achieve Artificial General Intelligence (AGI), a type of AI that might be smarter than humans, by 2029. "Nobody really took it seriously until now," Kurzweil said about AI. "People are convinced it's going to either endow us with things we'd never had before, or it's going to kill us." Cognizant sponsored Monday's event, which celebrated the 100 most influential people leading change in AI. Jacobs probed the four panelists--three of whom were named to the 2024 list--about the opportunities and challenges presented by AI's rapid advancement.

The Hong Kong government is preparing to issue its maiden policy statement on the use of artificial intelligence in finance, according to people familiar with the matter, in a move that could catalyze the use of the technology in areas from trading to investment banking and cryptocurrencies. The city's Financial Services and Treasury Bureau plans to issue a framework of guidelines to touch on the ethical use of AI and general principles for applying the technology in the finance world, the people said, asking not to be identified discussing private information. Officials are still drafting the document while getting feedback from the industry, the people said. Details are still subject to change in the coming weeks, they added. While specifics remain unclear, the document is broadly intended to signal Hong Kong's support for AI, as governments around the world get to grips with the technology's potential.

At least one person was injured and several homes damaged in a Russian drone attack on Ukraine's Kyiv region, Governor Ruslan Kravchenko said. Ukraine's Air Force said it shot down 53 of the 56 Russian drones that targeted the country's central, northern and southern regions. Air defence units destroyed nearly 20 drones that were heading towards Kyiv itself, the military said. Ukrainian President Volodymyr Zelenskyy, speaking in his nightly video address, said there had been 100 battles over the past 24 hours on the eastern front with the heaviest fighting in the Pokrovsk and Kurakhove sectors. Russia ordered the evacuation of settlements close to the Ukrainian border in the Kursk region and said it had retaken two villages – Uspenovka and Borki – Ukraine captured last month in a surprise cross-border incursion.

A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizations, a baseline GNN layer (termed a message passing layer, which updates local node properties) is modified to account for synchronization of coincident graph nodes, rendering compatibility with commonly used element-based mesh connectivities. The architecture is multiscale in nature, and is comprised of a combination of coarse-scale and fine-scale message passing layer sequences (termed processors) separated by a graph unpooling layer. The coarse-scale processor embeds a query element (alongside a set number of neighboring coarse elements) into a single latent graph representation using coarse-scale synchronized message passing over the element neighborhood, and the fine-scale processor leverages additional message passing operations on this latent graph to correct for interpolation errors. Demonstration studies are performed using hexahedral mesh-based data from Taylor-Green Vortex flow simulations at Reynolds numbers of 1600 and 3200. Through analysis of both global and local errors, the results ultimately show how the GNN is able to produce accurate super-resolved fields compared to targets in both coarse-scale and multiscale model configurations.

Though there is much interest in fair AI systems, the problem of fairness noncompliance -- which concerns whether fair models are used in practice -- has received lesser attention. Zero-Knowledge Proofs of Fairness (ZKPoF) address fairness noncompliance by allowing a service provider to verify to external parties that their model serves diverse demographics equitably, with guaranteed confidentiality over proprietary model parameters and data. They have great potential for building public trust and effective AI regulation, but no previous techniques for ZKPoF are fit for real-world deployment. We present OATH, the first ZKPoF framework that is (i) deployably efficient with client-facing communication comparable to in-the-clear ML as a Service query answering, and an offline audit phase that verifies an asymptotically constant quantity of answered queries, (ii) deployably flexible with modularity for any score-based classifier given a zero-knowledge proof of correct inference, (iii) deployably secure with an end-to-end security model that guarantees confidentiality and fairness across training, inference, and audits. We show that OATH obtains strong robustness against malicious adversaries at concretely efficient parameter settings. Notably, OATH provides a 1343x improvement to runtime over previous work for neural network ZKPoF, and scales up to much larger models -- even DNNs with tens of millions of parameters.

The growth of the tech startup ecosystem in Latin America (LATAM) is driven by innovative entrepreneurs addressing market needs across various sectors. However, these startups encounter unique challenges and risks that require specific management approaches. This paper explores a case study with the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics within the context of the online food delivery industry in LATAM, serving as a model for valuing startups using the Discounted Cash Flow (DCF) method. By analyzing key emerging powers such as Argentina, Colombia, Uruguay, Costa Rica, Panama, and Ecuador, the study highlights the potential and profitability of AI-driven startups in the region through the development of a ranking of emerging powers in Latin America for tech startup investment. The paper also examines the political, economic, and competitive risks faced by startups and offers strategic insights on mitigating these risks to maximize investment returns. Furthermore, the research underscores the value of diversifying investment portfolios with startups in emerging markets, emphasizing the opportunities for substantial growth and returns despite inherent risks.

Goal recognition (GR) involves inferring an agent's unobserved goal from a sequence of observations. This is a critical problem in AI with diverse applications. Traditionally, GR has been addressed using 'inference to the best explanation' or abduction, where hypotheses about the agent's goals are generated as the most plausible explanations for observed behavior. Alternatively, some approaches enhance interpretability by ensuring that an agent's behavior aligns with an observer's expectations or by making the reasoning behind decisions more transparent. In this work, we tackle a different challenge: explaining the GR process in a way that is comprehensible to humans. We introduce and evaluate an explainable model for goal recognition (GR) agents, grounded in the theoretical framework and cognitive processes underlying human behavior explanation. Drawing on insights from two human-agent studies, we propose a conceptual framework for human-centered explanations of GR. Using this framework, we develop the eXplainable Goal Recognition (XGR) model, which generates explanations for both why and why not questions. We evaluate the model computationally across eight GR benchmarks and through three user studies. The first study assesses the efficiency of generating human-like explanations within the Sokoban game domain, the second examines perceived explainability in the same domain, and the third evaluates the model's effectiveness in aiding decision-making in illegal fishing detection. Results demonstrate that the XGR model significantly enhances user understanding, trust, and decision-making compared to baseline models, underscoring its potential to improve human-agent collaboration.

An inherent fragility of quadrotor systems stems from model inaccuracies and external disturbances. These factors hinder performance and compromise the stability of the system, making precise control challenging. Existing model-based approaches either make deterministic assumptions, utilize Gaussian-based representations of uncertainty, or rely on nominal models, all of which often fall short in capturing the complex, multimodal nature of real-world dynamics. This work introduces DroneDiffusion, a novel framework that leverages conditional diffusion models to learn quadrotor dynamics, formulated as a sequence generation task. DroneDiffusion achieves superior generalization to unseen, complex scenarios by capturing the temporal nature of uncertainties and mitigating error propagation. We integrate the learned dynamics with an adaptive controller for trajectory tracking with stability guarantees. Extensive experiments in both simulation and real-world flights demonstrate the robustness of the framework across a range of scenarios, including unfamiliar flight paths and varying payloads, velocities, and wind disturbances.